NetLogo banner

Home
Download
Help
Forum
Resources
Extensions
FAQ
NetLogo Publications
Contact Us
Donate

Models:
Library
Community
Modeling Commons

Beginners Interactive NetLogo Dictionary (BIND)
NetLogo Dictionary

User Manuals:
Web
Printable
Chinese
Czech
Farsi / Persian
Japanese
Spanish

  Donate

References

This page lists publications that have used or cited NetLogo software and/or models.

This list is by no means complete or exhaustive. If you are using and/or citing NetLogo in your work, or you know of work that is not listed, please send the relevant citations to netlogo-refs@ccl.northwestern.edu.

Google Scholar's database lists roughly 38,600 Netlogo citations. You can explore it here:

Bold = Publications authored by the CCL

In Press 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999

In Press

  • Abrahamson, D. (in press). W(h)ither the Learning Sciences? An acerbic rumination. In M.-C. Shanahan, B. Kim, K. Koh, A. P. Preciado-Babb, & M. A. Takeuchi (Eds.), Learning sciences in conversation: Theories, methodologies, and boundary spaces. New York: Routledge.
  • Chen, J., & Wilensky, U. J. (2023). Tortuga: Building Interactive Scaffolds for Agent-based Modeling and Programming in NetLogo. Proceedings of ISLS Annual Meeting 2023.
  • Flood, V. J., Shvarts, A., & Abrahamson, D. (in press). Responsive teaching for embodied learning with technology. In S. Macrine & J. Fugate (Eds.), Movement matters: How embodied cognition informs teaching and learning. MIT Press.
  • Kelter, J. Wit, J., Conboy, W., Potvin, J., & Wilensky, U. (2022). Poster: A General-Purpose ‘Economic Petri Dish’ ABM with ‘Land’ and ‘Organization’ to Test Indexed Pricing Methods for Stability and Resilience. The Computational Social Science Society of the Americas (CSS) 2022.
  • Peel, A., Hao, D., Horn, M.S., Wilensky, U. (In Review). How teachers integrated CT into science and math co-designed curricular units. Paper submitted to the Annual Meeting of the American Educational Research Association (AERA) 2022. Chicago, IL.
  • Wagh, A., Levy, S., Horn, M., Guo, B., & Wilensky, U. (In Review) Learning to Reason About Natural Selection as a Computational Process by Programming Agent-Based Models in a Code-First Environment.

2025

  • Alonso-Adame, A., Farahbakhsh, S., Van Meensel, J., Marchand, F., & Van Passel, S. (2025). Factors to scale out innovative organic farming systems: A case study in Flanders region, Belgium. Agricultural Systems, 224, 104219.
  • Nicolajsen, S. M., Caspersen, M. E., & Brabrand, C. (2025). Circle of Life: Microworld Project at the End of CS1.
  • Shen, Y., & Yang, Q. (2025). A mathematical model for biological motor learning based on synaptic dynamics. Chaos, Solitons & Fractals, 191, 115839.
  • Simpkins, C. E., Bellingham, P. J., Reihana, K., Brock, J. M., & Perry, G. L. (2025). Evaluating the effects of two newly emerging plant pathogens on northern Aotearoa-New Zealand forests using an individual-based model. Ecological Modelling, 500, 110938.
  • Sosa, B., Zellner, M., Chiale, C., & Achkar, M. (2025). Explaining woody invasions in riparian systems with agent-based simulations: Implications for conservation management. Forest Ecology and Management, 576, 122363.

2024

  • Abdelaziz, D. E., & Kadri, O. (2024). Enhancing epidemic management: agent-based simulation and remote diagnosis. Brazilian Journal of Technology, 7(2), e70355-e70355.
  • Abd Hashim, Y. J., & Hasson, S. T. (2024, July). A Proposed Approach to Improve the WSN Performance Based on LEACH Algorithm. In 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA) (pp. 168-173). IEEE.
  • Abd Hashim, Y. J., & Hasson, S. T. (2024). Proposed Approach to Increase the Reliability of Wireless Sensor Networks through Dijkstra’s Algorithm. In SHS Web of Conferences (Vol. 204, p. 04002). EDP Sciences.
  • Abdidizaji, S., Yalabadi, A. K., Yazdani-Jahromi, M., Garibay, O. O., & Garibay, I. (2024). Agent-Based Modeling of C. Difficile Spread in Hospitals: Assessing Contribution of High-Touch vs. Low-Touch Surfaces and Inoculations' Containment Impact. arXiv preprint arXiv:2401.11656.
  • Abdo, J. B., & Hossain, L. (2024). Modeling the Invisible Internet. In Complex Networks & Their Applications XII: Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3 (p. 359). Springer Nature.
  • Abos, P. (2024). Computational Platforms and Toolkits: There are a variety of software platforms, libraries, and tools available for developing and simulating agent-based models.
  • Abos, P. (2024). Participatory Modeling and Stakeholder Engagement: Agent-based models can be used as tools for engaging stakeholders and the public in collaborative modeling exercises.
  • Adair, A. (2024). AI-Driven Assessment and Scaffolding for Mathematical Modeling and Explanations During Science Investigations (Doctoral dissertation, Rutgers The State University of New Jersey, School of Graduate Studies).
  • Adam, C., & Gaudou, B. (2024). An agent-based model of modal choice with perception biases and habits. arXiv preprint arXiv:2406.02063.
  • Adday, G. H., Subramaniam, S. K., Zukarnain, Z. A., & Samian, N. (2024). Investigating and Analyzing Simulation Tools of Wireless Sensor Networks: A Comprehensive Survey. IEEE Access, 12, 22938-22977.
  • Adderley, T., Concepcion, S. M., Ramirez, M. F., Tannis, D., & Browne, J. Pile Bay Agent-Based Modeling Exercise Proposal PPUA 5246: Participatory Modeling for Collaborate Decision Making.
  • Agarwal, R. (2024). AIoT‐Based Smart Bin for Real‐Time Monitoring and Management of Solid Waste. Reshaping Intelligent Business and Industry: Convergence of AI and IoT at the Cutting Edge, 295-303.
  • Agarwal, A., & Canfield, C. (2024). Analysis of rural broadband adoption dynamics: A theory-driven agent-based model. PloS one, 19(6), e0302146.
  • Aghaabbasi, M., & Sabri, S. (2025). Potentials of digital twin system for analyzing travel behavior decisions. Travel Behaviour and Society, 38, 100902.
  • Ahmed, M., Kazar, O., & Harous, S. (2024). Cyber‐physical system model based on multi‐agent system. IET Cyber‐Physical Systems: Theory & Applications.
  • Ahmed, R., Osman, R. A., & Amer, M. (2024). Navigating urban congestion: A Comprehensive strategy based on an efficient smart IoT wireless communication for PV powered smart traffic management system. PloS one, 19(10), e0310002.
  • Ahrweiler, P., Gilbert, N., Bicket, M., Coll, A. S., Capellas, B. L., Wurster, D., & Siqueiros, J. M. Gamification and Simulation for Innovation. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 121). Springer Nature.
  • Ainurohmah, S. (2024). Desain Ui/ux Aplikasi Sistem Lampu dan Suhu Otomatis pada Rumah Cerdas untuk Lansia (Doctoral dissertation, Universitas Islam Indonesia).
  • Aizstrauts, A., & Ginters, E. (2024). Easy Communication Environment for Heterogeneous and Distributed Simulation Models Design.
  • Akbar, Chairil, Omar Alsawafy, Nabeel Alanbar, Ahmed Attia, and Productivity Improvement. "Simulation-Based Optimization for Multi-Echelon Petrochemical Supply Chain."
  • Akman, O., Bhumpelli, S., & Hay-Jahans, C. (2024). Agent-Based Modeling for the Neophyte: An Application of NetLogo. Spora: A Journal of Biomathematics, 10(1), 37-49.
  • Albati, M., Sakurahara, T., Reihani, S., Kee, E., Yang, J., von Thaden, T., ... & Mohaghegh, Z. (2024). Uncertainty-Based Validation Methodology and Experimental Analysis for External Control Room Human Performance Simulation: Application to Fire Probabilistic Risk Assessment of Nuclear Power Plants. Nuclear Science and Engineering, 1-20.
  • Aldahlawi, R. Y., Akbari, V., & Lawson, G. (2024). A Systematic Review of Methodologies for Human Behavior Modelling and Routing Optimization in Large-scale Evacuation Planning. International Journal of Disaster Risk Reduction, 104638.
  • Alexandridis, K., Sabri, S., Smith, J., Logan, B., Bartfai-Walcott, K., & Migliori, D. (2024). Distributed AI Modeling and Simulation for Smart Airport Digital Twin Applications. Digital Twins, Simulation, and the Metaverse: Driving Efficiency and Effectiveness in the Physical World through Simulation in the Virtual Worlds, 195.
  • Alooeff, E., & Adzinets, D. (2024). Multi-agent system for intelligent scheduling.
  • Al-Sukhni, M. (2024). Blockchain Technology for Information Sharing to Mitigate the Bullwhip Effect. In Disruptive Technologies and Optimization Towards Industry 4.0 Logistics (pp. 95-121). Cham: Springer International Publishing.
  • Alteri, S. P. (2024). A COMPREHENSIVE FRAMEWORK FOR ENHANCING LARGE-SCALE EVENT SECURITY (Doctoral dissertation, Purdue University Graduate School).
  • Altmann, P., Schönberger, J., Illium, S., Zorn, M., Ritz, F., Haider, T., ... & Gabor, T. (2024). Emergence in Multi-Agent Systems: A Safety Perspective. arXiv preprint arXiv:2408.04514.
  • Alulema, D., Paredes-Velasco, M., & de Arriba Lasso, R. (2024). LESCA: Scaffolding and its impact on the higher cognitive levels and emotions of the student. Journal of Universal Computer Science, 30(7), 935.
  • Alves, R. B. D. O., Tomasiello, D. B., Almeida, C. M. D., Rosalen, D. L., Pereira, L. H., Silva, H. P. D., & Rodrigues, C. L. (2024). Agent-Based Spatial Dynamic Modeling of Diatraea saccharalis and the Natural Parasites Cotesia flavipes and Trichogramma galloi in Sugarcane Crops. Remote Sensing, 16(15), 2693.
  • Amakama, N. J., Duserre, G., Cadiere, A., & Schuette, R. W. (2024). Predicting the Impact of Mobile Field Hospital Deployment in Reducing Patients Mean Wait Times in Emergency Departments: An Agent-Based Simulation Approach.
  • Amakama, N. J., Dusserre, G., Cadiere, A., Schuette, R. W., & Zacharewicz, G. (2024). How Effective are Tabletop Role-Playing (Serious) Games in Understanding and Validating the Predictive Capabilities of Disaster Response Agent-based Models?. American Journal of Multidisciplinary Research and Innovation, 3(4), 49-61.
  • Amiri, M., Radfar, R., & Faezy Razi, F. (2025). Designing a Social Banking Model to Reduce Conflict of Financial Interest between Banks and Manufacturing Firms through Agent-Based Modeling Simulation. International Journal of Finance & Managerial Accounting, 10(37), 47-60.
  • Apetrei, C. I., Strelkovskii, N., Khabarov, N., & Rincón, V. J. (2024). Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning. Ecological Modelling, 489, 110609.
  • Aqib, M., & Ukil, A. (2024). Battery degradation model of electric vehicle with grid integration. Journal of Energy Storage, 97, 112709.
  • Arnold, E. G., Burroughs, E. A., Burroughs, O., & Carlson, M. A. International Journal of Mathematical Education in Science and Technology.
  • Arora, K., Gupta, V., Rajan, A., Kumar, L., Munjal, P., Jain, R., & Srivastava, A. Efficient and Sensitive Proteomic Based Electrochemical Immunosensor Using Nio Thin Film Electrode for Early Identification of a Biomarker Unique to Breast Cancer (Ca 15-3) and Agent-Based Modelling for Effective Treatment Strategy. Available at SSRN 5010009.
  • Arshad, J., Talha, M., Saleem, B., Shah, Z., Zaman, H., & Muhammad, Z. (2024). A Survey of Bug Bounty Programs in Strengthening Cybersecurity and Privacy in the Blockchain Industry. Blockchains, 2(3), 195-216.
  • Ashrafi, B., Kim, G., Naseri, M., Barabady, J., Dhar, S., Heo, G., & Baek, S. (2024). An agent-based modelling framework for performance assessment of search and rescue operations in the Barents Sea. Safety in Extreme Environments, 1-18.
  • Aslan, U., Horn, M., & Wilensky, U. (2024). Why are some students “not into” computational thinking activities embedded within high school science units? Key takeaways from a microethnographic discourse analysis study. Science Education, 1–28. https://doi.org/10.1002/sce.21850
  • ASSOGBA, G. G. C., Speelman, E., Berre, D., Adam, M., & Descheemaeker, K. Exploring Opportunities to Improve Crop-Livestock Integration and Production in Mixed Farms with a Serious Game: The Case of Semi-Arid Burkina Faso. Available at SSRN 5002412.
  • Astuti, R. N. M., & Ramdhan, D. H. (2024). Factors Contributing to the Successful Implementation of an OHS Management System in the Informal/SME Sector. Media Publikasi Promosi Kesehatan Indonesia (MPPKI), 7(6), 1458-1466.
  • Azamuke, D., Katarahweire, M., & Bainomugisha, E. (2024). MoMTSim: A Multi-agent-based Simulation Platform Calibrated for Mobile Money Transactions. IEEE Access.
  • Babkin, E., Golov, V., & Malyzhenkov, P. Check for updates Multi-agent Simulations of Mutual Trust Management Strategies as a Base of Innovative Organizational Forms Engineering. In Advances in Enterprise Engineering XVII: 13th Enterprise Design and Engineering Working Conference, EDEWC 2023, Vienna, Austria, November 28–29, 2023, Revised Selected Papers (p. 18). Springer Nature.
  • Baccile, S., Cornacchia, G., & Pappalardo, L. (2024). Measuring the Impact of Road Removal on Vehicular CO2 Emissions. In Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference. CEUR-WS.
  • Bai, L., Pan, T., Liu, S., Xie, Q., Zhang, Z., & Ma, Y. (2024). Dynamic measurement for the impact of project portfolio synergy. Heliyon.
  • Baker, M., & Reimann, P. (2024). CSCL: a learning and collaboration science?. International Journal of Computer-Supported Collaborative Learning, 1-9.
  • Baldo, H. (2024). Towards a Quantitative Theory of Digraph-Based Complexes and its Applications in Brain Network Analysis (Doctoral dissertation, Universidade de São Paulo).
  • Bandari, M., Azar, A., & Fathi Hafshejani, K. (2024). Design of An Agent-Based Simulation Model of Service Supply Chain. Journal of Industrial and Systems Engineering.
  • Bansal, R. (2024). Challenges in Large Language Model Development and AI Ethics. In Challenges in Large Language Model Development and AI Ethics (pp. 25-81). IGI Global.
  • Barbara, K., & Katarzyna, S. W. (2024). Impact of Cognitive Dissonance on Social Hysteresis: Insights fromthe Expressed and Private Opinions Model. arXiv preprint arXiv:2410.16934.
  • Barbrook-Johnson, P., van Voorn, G., Wang, H. H., Zare, F., Grant, W. E., Posnik, Z., & Lippe, M. (2024). Cross-scale feedbacks and tipping points in aggregated models of socio-ecological systems. Socio-Environmental Systems Modelling, 6, 18616-18616.
  • Barton, C. M., Aura-Tortosa, J. E., Garcia-Puchol, O., Riel-Salvatore, J., & Ullah, I. (2024). Lessons for an invisible future from an invisible past: Risk and resilience in deep time. The Holocene, 09596836241259788.
  • Basha, S. M., de Albuquerque, V. H. C., Chelloug, S. A., Elaziz, M. A., Mohisin, S. H., & Pathan, S. P. (2024). Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images. CMES-Computer Modeling in Engineering & Sciences, 138(2).
  • Batzke, M. C. L. (2024). Dynamics of Norms in Decision-Making (Doctoral dissertation, University of Kassel).
  • Bauer, R. (2024). Computational Modelling of Cell Migration. In Cell Migration in Development, Health and Disease (pp. 223-243). Cham: Springer Nature Switzerland.
  • BEAUCHAMP-MUSTAFAGA, N. A. T. H. A. N., GREEN, K., MARCELLINO, W., LILLY, S., & SMITH, J. (2024). Dr. Li Bicheng, or How China Learned to Stop Worrying and Love Social Media Manipulation.
  • Becher, M. A., Twiston‐Davies, G., Osborne, J. L., & Lander, T. A. (2024). Resource gaps pose the greatest threat for bumblebees during the colony establishment phase. Insect Conservation and Diversity.
  • Beddiaf, F., & Schumann, R. (2024). Modeling Coopetition Dynamics Using Agent-Based Approaches. Matrix, 2(2), 0.
  • Beek, M. V., Lopate, M. Z., Goodhart, A., Peterson, D. A., Edgerton, J., Xiong, H., ... & Braumoeller, B. F. (2024). Hierarchy and war. American Journal of Political Science.
  • Beghdad, R., Benmenzer, F., & Eddine Khalfoune, A. (2024). Cloud Survivability Scenarios Under Attacks With and Without Countermeasures. Acta Informatica Pragensia.
  • Beghdad, R., Benmenzer, F., & eddine Khalfoune, A. Survivability Scenarios of the Cloud under Attacks With/Without Countermeasures.
  • Belfrage, M., Lorig, F., & Davidsson, P. (2024). Simulating Change-A Systematic Literature Review of Agent-Based Models for Policy-Making. In Annual Modeling and Simulation Conference (ANNSIM 2024), Washington DC, USA, May 20-23, 2024. Society for Modeling and Simulation International (SCS).
  • Benedicto-Rodriguez, G., Zorrilla-Muñoz, V., Fernandez, E., & Ferrandez, J. M. (2024, August). Technological Influence on the Measurement of Quality of Life in Persons with Autism Spectrum Disorder. In The International Conference on Innovations in Computing Research (pp. 691-698). Cham: Springer Nature Switzerland.
  • Berceanu, C., & Patrascu, M. Comparative Analysis of Agent-based Modeling Frameworks for Signal Propagation in Complex Networks: Netlogo and Python Mesa. system, 13(14), 15.
  • Berger, U., Bell, A., Barton, C. M., Chappin, E., Dreßler, G., Filatova, T., ... & Grimm, V. (2024). Towards reusable building blocks for agent-based modelling and theory development. Environmental Modelling & Software, 106003.
  • Bergman, D. R., Jackson, T. L., Jain, H. V., & Norton, K. A. (2024). SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters. bioRxiv, 2024-09.
  • Bernigaud, N., Bondeau, A., Guiot, J., Bertoncello, F., Ouriachi, M. J., Bouby, L., ... & Isoardi, D. (2024). The impact of climate change on the agriculture and the economy of Southern Gaul: New perspectives of agent-based modelling. Plos one, 19(3), e0298895.
  • Bertorello, S., Cei, F., Fink, D., Niccolai, E., & Amedei, A. (2024). The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations. Microorganisms, 12(9), 1828.
  • Beutel, A., Xiao, K., Heidecke, J., & Weng, L. Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning.
  • Bhatia, V., & Jaglan, V. (2024). Integrating Artificial Intelligence and IoT for Smart Cities: Applications and Challenges. Reshaping Intelligent Business and Industry: Convergence of AI and IoT at the Cutting Edge, 267-278.
  • Bhatt, S., Verbert, K., & Van Den Noortgate, W. (2024). A Method for Developing Process-Based Assessments for Computational Thinking Tasks. Journal of Learning Analytics, 1-17.
  • Bijlard, H. M. Open vs Micro; comparing different agent populations and their impacts.
  • Bijli, M. K., Verma, P., & Singh, A. P. (2024). A systematic review on the potency of swarm intelligent nanorobots in the medical field. Swarm and Evolutionary Computation, 101524.
  • Bilge, U., Olgan, S., Ciplak, B., Ozbek, M., Kayikci, D., & Uysal, H. (2024). A Neanderthal Extinction Hypothesis: Agent Based Simulation of Birth Complications Theory.
  • Blanco, R., Patow, G., & Pelechano, N. (2024). Simulating real-life scenarios to better understand the spread of diseases under different contexts. Scientific Reports, 14(1), 2694.
  • Blee, J. A., Gorochowski, T. E., & Hauert, S. (2024). Optimization of periodic treatment strategies for bacterial biofilms using an agent-based in silico approach. Journal of the Royal Society Interface, 21(213), 20240078.
  • Blonder, A. (2024). An Agent-Based Simulation of Preferential Aggregation Based on Past Experience in Fission-Fusion Societies (Doctoral dissertation, UC Davis).
  • Bodine, E. N. (2024). BODINE: Modeling the Impacts of an Invasive Weevil on the Evolutionary Dynamics and Population Viability of a Florida Endangered Bromeliad.
  • Boetttcher, L., Fonseca, L. L., & Laubenbacher, R. (2024). Control of Medical Digital Twins with Artificial Neural Networks. bioRxiv, 2024-03.
  • Bommel, P., & Le Page, C. (2024). Rapport de mission à Parakou, Bénin, du 2 au 12 janvier 2024. Formation à la modélisation multi-agent pour des applications à la gestion intégrée des ressources naturelles (Doctoral dissertation, CIRAD (Montpellier; France)).
  • Borah, D. K., Zhang, H. X., Zellner, M., Ahmadisharaf, E., Babbar-Sebens, M., Quinn, N. W., ... & Lott, C. (2024). Advances in Total Maximum Daily Load Implementation Planning by Modeling Best Management Practices and Green Infrastructures. Journal of Environmental Engineering, 150(7), 03124003.
  • Borowsky, A. T., & Bailey-Serres, J. (2024). Rewiring gene circuitry for plant improvement. Nature Genetics, 1-9.
  • Bouaziz, N., Bettayeb, B., Sahnoun, M. H., & Yassine, A. (2024). Incorporating uncertain human behavior in production scheduling for enhanced productivity in Industry 5.0 context. International Journal of Production Economics, 109311.
  • Bourgais, M., Saval, A., Tranouez, P., Gillet, O., & Daudé, É. (2024). Designing a Serious Game of crisis management on top of an Agent-Based Simulation of population evacuation.
  • Bowden, S. A. (2024). Musical Visuality: Embodying Meter and Overcoming Noise in the Marching Arts (Doctoral dissertation, Northwestern University).
  • Bowers, J., & Eidin, E. (2024). Analyzing students’ systems thinking in-situ through screencasts in the context of computational modeling: a case study. Disciplinary and Interdisciplinary Science Education Research, 6(1), 24.
  • Boz, Z., Kiker, G., Haase, H., Orr, R., Vignesh, A., Campbell, C., ... & Clemen, T. Modeling Waste: An Agent-Based Model to Support the Measurement of Household Food Waste. Available at SSRN 5017237.
  • Bozzi, A., Graffione, S., Jimenez, J. F., Sacile, R., & Zero, E. (2024). A Platoon-Based Approach for AGV Scheduling and Trajectory Planning in Fully Automated Production Systems. IEEE Transactions on Industrial Informatics.
  • Braccini, M., De Filippo, A., Lombardi, M., & Milano, M. (2024). Swarm Intelligence: A Novel and Unconventional Approach to Dance Choreography Creation.
  • Brady, C., Ramírez, P., & Lesh, R. (2024). Problem Posing and Modeling: Confronting the Dilemma of Rigor or Relevance. In Problem Posing and Problem Solving in Mathematics Education: International Research and Practice Trends (pp. 33-50). Singapore: Springer Nature Singapore.
  • Brainard, J. S., Lake, I. R., & Hunter, P. R. (2024). Evaluation of three control strategies to limit mpox outbreaks in an agent based model. medRxiv.
  • Bravo, R. R. (2024). 3D Organ-Scale Models of Tumor Growth and Treatment (Doctoral dissertation, University of South Florida).
  • Brouillet, M. J., & Georgiev, G. Y. (2024). Why and How do Complex Systems Self-Organize at All? Average Action Efficiency as a Predictor, Measure, Driver, and Mechanism of Self-Organization. arXiv preprint arXiv:2408.10278.
  • Brown, T. L., Morgan, E. R., & Caplaet, P. Nemasim: Using an Agent Based Model to Explore the Role of Landscape Fragmentation and Deer Ecology in Transmitting Drug Resistant Nematodes between Sheep Pastures. Available at SSRN 4783624.
  • Bryant, G. A., & Smaldino, P. E. (2024). The cultural evolution of distortion in music.
  • Buchsbaum, K. (2024). From the Ground Up: A Complex Systems Approach to Climate Change Adaptation in Agriculture.
  • Burillo, F., Lambán, M. P., Royo, J. A., Morella, P., & Sánchez, J. C. (2024). Real-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots. Applied Sciences, 14(5), 1890.
  • Buteau, C., Namukasa, I., & Sardella, J. (2024). CCMEME 2023 Proceedings.
  • Butler, M., Doughty, A., Lifton, N., Kelley, S., Moucheron, M., O’Brien, C., ... & Warren, G. (2024). Looking Up for prehistoric hunter-gatherer archaeological sites in mountain landscapes in Europe: A case study from the Cairngorms, Scotland. Hunter Gatherer Research, 1-30.
  • Buzato, D., & Cunha, E. (2024, May). Agent-based Modeling of Language Change in a Small-world Network. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 594-599).
  • Cabrera-Revuelta, E., Tavolare, R., Buldo, M., & Verdoscia, C. (2024). Planning for terrestrial laser scanning: Methods for optimal sets of locations in architectural sites. Journal of Building Engineering, 85, 108599.
  • Cai, C., Tian, S., Shi, Y., Chen, Y., & Li, X. (2024). Influencing Factors Analysis in Railway Engineering Technological Innovation under Complex and Difficult Areas: A System Dynamics Approach. Mathematics, 12(13), 2040.
  • Canales, M., Castilla-Rho, J., Rojas, R., Vicuña, S., & Ball, J. (2024). Agent-based models of groundwater systems: A review of an emerging approach to simulate the interactions between groundwater and society. Environmental Modelling & Software, 175, 105980.
  • Canals, C., Maroulis, S., Mizala, A., Canessa, E., & Chaigneau, S. (2024). Mechanisms Behind Public School Enrollment Trends in School Choice Systems: The Case of Chile. Journal of Artificial Societies and Social Simulation, 27(4), 1-2.
  • Caprioli, C. The Integration of Multi-Agent System and Multicriteria Analysis for Developing Participatory Planning Alternatives in Urban Contexts. Available at SSRN 4918496.
  • Carbo, J., Pedraza, J., & Molina, J. M. (2024). Agents preserving privacy on intelligent transportation systems according to EU law. Artificial Intelligence and Law, 1-34.
  • Caraiani, P., Cotfas, L. A., Darie, F. C., Delcea, C., Iglesias, C. A., & Prodan, R. (2024). JobKG: A Knowledge Graph of the Romanian Job Market based on Natural Language Processing.
  • Carley, K. M. (2024). Unveiling Transport Dynamics in Developing Countries, a Data-Driven Agent-Based Modeling Approach.
  • Carpenter, C. J., Cruz, S. M., & Reynolds, R. M. (2024). Expansion and Exploration of the Superdiffuser Model With Agent-Based Modeling. Communication Research, 00936502241285574.
  • Casadei, R., Fornari, F., Mariani, S., & Savaglio, C. Fluidware Meets Digital Twins Check for updates. Fluidware: Novel Approaches for Large-Scale IoT Systems, 137.
  • Castellani, B., & Gerrits, L. (2024). Cartography and constructing the atlas. In The Atlas of Social Complexity (pp. 55-61). Edward Elgar Publishing.
  • Castellani, B., & Gerrits, L. (2024). Mapping the new methodological terrain. In The Atlas of Social Complexity (pp. 416-445). Edward Elgar Publishing.
  • Castellani, B., & Gerrits, L. (2024). Thirteen situations. In The Atlas of Social Complexity (pp. 35-54). Edward Elgar Publishing.
  • Castillo Grisales, J. A., Ceballos, Y. F., Bastidas-Orrego, L. M., Jaramillo Gómez, N. I., & Chaparro Cañola, E. (2024). Development of an Agent-Based Model to Evaluate Rural Public Policies in Medellín, Colombia. Sustainability, 16(18), 8185.
  • Castillo Losada, L. T. (2024). La educación ambiental y las TIC en la educación secundaria y media en Colombia: una revisión de literatura.
  • Cavallaro, C., Crespi, C., Cutello, V., Pavone, M., & Zito, F. (2024). Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem. Algorithms, 17(2), 63.
  • Cerdá, M., Hamilton, A. D., Hyder, A., Rutherford, C., Bobashev, G., Epstein, J. M., ... & Keyes, K. M. (2024). Simulating the simultaneous impact of medication for opioid use disorder and naloxone on opioid overdose death in eight New York counties. Epidemiology.
  • Černohorský, J., Šafránek, S., & Štekerová, K. (2024). Game Theory and Agent-Based Models in Epidemiology: Exploration of Strategies with NetLogo. doi: 10.36689/uhk/hed/2024-01-009.
  • Chae, S. J., Kim, D. W., Igoshin, O. A., Lee, S., & Kim, J. K. (2024). Beyond microtubules: The cellular environment at the endoplasmic reticulum attracts proteins to the nucleus, enabling nuclear transport. bioRxiv, 2024-01.
  • Chakraborty, S., Goyal, S., Rieder, A., Onuchowska, A., & Berndt, D. J. (2024). Freedom of speech or freedom of reach? Strategies for mitigating malicious content in social networks. Decision Support Systems, 114235.
  • Chang, X., Chen, J., & Ye, L. (2024). Trend prediction of farmers’ spontaneous land transfer behavior: evidence from China. Applied Economics, 1-15.
  • Chao, S., Tao, Y., & Zhou, H. (2024). Technological Progress and its Job Market Impacts: A Basic Simulation Framework for Human-Technology Collaboration. Available at SSRN 4747459.
  • Chattoe-Brown, E., Elsenbroich, C., & Gilbert, N. Reculer Pour Mieux Sauter: Resurrecting a Household Budgeting ABM for Deteriorating Economic Conditions. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 35). Springer Nature.
  • Chen, F., & Chen, G. (2024). Technology-Enhanced Collaborative Inquiry in K–12 Classrooms: A Systematic Review of Empirical Studies. Science & Education, 1-43.
  • Chen, I., & Cheng, K. L. (2024). Agent-Based Modeling Framework for Predicting Regional Electricity Consumption Considering Occupant Behavior Shift and Exogenous Policy Impact. Karly, Agent-Based Modeling Framework for Predicting Regional Electricity Consumption Considering Occupant Behavior Shift and Exogenous Policy Impact.
  • Chen, J., Lotsos, A., Zhao, L., Hullman, J., Sherin, B., Wilensky, U., & Horn, M. (2024). A Computational Method for Measuring" Open Codes" in Qualitative Analysis. arXiv preprint arXiv:2411.12142.
  • Chen, J., Lu, X., Du, Y., Rejtig, M., Bagley, R., Horn, M. S., & Wilensky, U.(2024). Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/3613904.3642377
  • Chen, J., Zhao, L., Li, Y., Xie, Z., Wilensky, U., & Horn, M. S.(2024). Oh My God! It’s Recreating Our Room!” Understanding Children’s Experiences with A Room-Scale Augmented Reality Authoring Toolkit. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/3613904.3642043
  • Chen, K., Wang, X., Guo, Z., & Liu, W. (2024). The Disappearing Winners: An MAS Study of Community-Type Rivalry in Construction Markets. Buildings, 14(12), 3710.
  • Chen, M., Liu, R. X., & Hao, J. (2024). An agent-based real-time game model for forecasting the market penetration of vehicles in China. IEEE Access, 12, 24631-24643.
  • Chen, S., Du, X., & Wang, J. (2024). A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems. arXiv preprint arXiv:2401.10300.
  • Chen, W., Yuan, Y., & Lv, M. Educators' Networking Interacts with Digital Competence Heterogeneity to Enhance the Implementation of AIEd: A Mixed-Methods. In Big Data and Social Computing: 9th China National Conference, BDSC 2024, Harbin, China, August 8–10, 2024, Proceedings (p. 151). Springer Nature.
  • Chen, Y., Du, T., Zhang, Q., & Zhang, N. (2024). Analysis and Evaluation of Species Invasion. Advances in Engineering Technology Research, 9(1), 687-687.
  • Chen, Y. W., Ruan, X., & Jain, R. K. (2024). Anchorfree Logo Detection Framework. In Recent Advances in Logo Detection Using Machine Learning Paradigms: Theory and Practice (pp. 65-82). Cham: Springer International Publishing.
  • Chen, Y., Zhao, Y., & Wu, Y. (2024). Recent progress in air traffic flow management: A review. Journal of Air Transport Management, 116, 102573.
  • Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., ... & He, X. (2024). Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects. arXiv preprint arXiv:2401.03428.
  • Cherny-Shahar, T., & Yehudai, A. (2024). Multi-Lingual Development & Programming Languages Interoperability: An Empirical Study. arXiv preprint arXiv:2411.08388.
  • Chesney, T., Gruchman, T., Pasley, R., Dawson, A., & Gold, S. (2024). Agent modelling, statistical control, and the strength of middle knowledge. arXiv preprint arXiv:2409.17196.
  • Chesney, T., Jaffer, A., & Pasley, R. (2024). Examining inverse generative social science to study targets of interest. arXiv preprint arXiv:2407.13474.
  • Chesney, T., Pasley, R., & Jaffar, A. (2024). RIGSS-Inverse Generative Social Science using R. Software Impacts, 100689.
  • Choi, M. (2024). Navigating the Complexities of Pandemic Dynamics: An Integrated Agent-Based Modeling Framework for Spatially Explicit Risk Assessment and Mitigation at Multiple Scale (Doctoral dissertation, The University of Utah).
  • Chopra, A., Subramanian, J., Krishnamurthy, B., & Raskar, R. (2024, May). flame: A Framework for Learning in Agent-based ModEls. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (pp. 391-399).
  • Chueca Del Cerro, C. (2024). The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model. Applied Network Science, 9(1), 69.
  • Cloude, E. B., Kiili, K., & Dindar, M. The Intra-and Inter-Personal Effects of Emotions on Collaborative Problem-Solving Efficiency between Competitive and Non-Competitive Teams in a Multiplayer Digital Game. Available at SSRN 5021794.
  • Coates, G., Dugdale, J., & Hanachi, C. (2024). Simulation for crisis and disaster management. SIMULATION, 100(4), 333-334.
  • Coco, E., & Iovita, R. Agent-based simulations reveal multiple rapid northern routes for the second Neanderthal dispersal from Western to Eastern Eurasia.
  • Collard, P. (2024). Processionary Caterpillars at the Edge of Complexity. Artificial Life, 1-22.
  • Collignon, J. (2024). Multiscale Modeling and Deep Learning for Complex Microbial Colonies (Doctoral dissertation, University of California, Merced).
  • Collins, A. J., & Grigoryan, G. (2024). ABMSCORE: a heuristic algorithm for forming strategic coalitions in agent-based simulation. Journal of Simulation, 1-25.
  • Collins, A., Koehler, M., & Lynch, C. (2024). Methods that support the validation of agent-based models: An overview and discussion. Journal of Artificial Societies and Social Simulation, 27(1), 11.
  • Colon, S. A. (2024). Simulating the Spread of Information Among Non-Playable Characters in Video Games.
  • Contreras, M., Sobrino, I., & de la Fuente, J. (2024). Paratransgenic quantum vaccinology. Trends in Parasitology.
  • Cooksey, R. W. (2024). The Evolving Landscape of Choices for Navigating the ‘Data Triangle’. In Unity from Diversity: Pluralist Systemic Thinking for Social and Behavioural Research (pp. 67-164). Singapore: Springer Nature Singapore.
  • Corrêa Angeloni, M. P., Duque Medina, R., Marçal de Oliveira, K., Strugeon, G. L., & Tirnauca, C. (2024). A tertiary study on quality in use evaluation of smart environment applications.
  • Costa, C. J., Aparicio, J. T., & Aparicio, M. (2024). Socio-Economic Consequences of Generative AI: A Review of Methodological Approaches. arXiv preprint arXiv:2411.09313.
  • Cottineau, C., Batty, M., Benenson, I., Delloye, J., Hatna, E., Pumain, D., ... & Ubarevičienė, R. (2024). The role of analytical models and their circulation in urban studies and policy. Urban Studies, 00420980241237410.
  • Cox, S. J., Manavi, K. M., & Guerin, S. Developing a Computational Geospatial Curriculum for Community Decision. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 231). Springer Nature.
  • Crabtree, S. A., Wren, C. D., Dixit, A., & Levin, S. A. (2024). Influential individuals can promote prosocial practices in heterogeneous societies: a mathematical and agent-based model. PNAS Nexus, 3(7).
  • Crispim-Mendes, T., Roos, D., Ferreira, C. M., Paupério, J., Silva, J. P., Godinho, S., ... & Pita, R. (2024). Patch spatial attributes and time to disturbance affect the emergence of source local populations within ephemeral habitats. Ecological Modelling, 496, 110839.
  • Crosland, P., Marshall, D. A., Hosseini, S. H., Ho, N., Vacher, C., Skinner, A., ... & Hickie, I. B. (2024). Incorporating Complexity and System Dynamics into Economic Modelling for Mental Health Policy and Planning. PharmacoEconomics, 1-15.
  • Cui, P., Lu, J., Wu, Y., Tang, J., & Jiang, J. (2024). Effect of urban morphology on microclimate and building cluster energy consumption in cold regions of China. Sustainable Cities and Society, 105838.
  • Curry, J. (2024). ENERGY LAW.
  • Czuba, M., Nurek, M., Serwata, D., Qiu, Y. X., Jia, M., Musial, K., ... & Bródka, P. (2024). Network Diffusion--Framework to Simulate Spreading Processes in Complex Networks. arXiv preprint arXiv:2405.18085.
  • Dai, L., Han, Q., & de Vries, B. (2024). Simulating dynamical evolution of citizen participation leveraging agent-based modeling: Experiences from nature-based solutions in China. Cities, 151, 105145.
  • Darly, S. S., Kadhiravan, D., Hemachandran, K., & Rege, M. (2024). Simulation Strategies for Analyzing of data. Handbook of Artificial Intelligence and Wearables, 27-64.
  • D'Amico, A., Sparvoli, G., Bernardini, G., Bruno, S., Fatiguso, F., Currà, E., & Quagliarini, E. (2024). Behavioural-based risk of the built environment: Key performance indicators for sudden-onset disaster in urban open spaces. International Journal of Disaster Risk Reduction, 104328.
  • Dahshan, M., & Galanti, T. (2024). Teachers in the Loop: Integrating Computational Thinking and Mathematics to Build Early Place Value Understanding. Education Sciences, 14(2), 201.
  • Danan, C. (2024). Differences in commercial brand promotion due to Chinese and Russian culture.
  • Das, K., Dhal, G. C., & Kalamdhad, A. S. (2024). Integrated assessment for groundwater quality and flood vulnerability in coal mining regions. Environmental Science and Pollution Research, 1-16.
  • Das, S. (2024). Network-based modeling of infectious disease spillover (Doctoral dissertation).
  • DaSilva, D., & Yokley, K. A. (2024). Mathematical Approaches of Modeling Obesity Trends. The North Carolina Journal of Mathematics and Statistics, 10(1), 20-39.
  • Daugherty, M. K., & Carter, V. (2024). 19 Ways of thinking and STEM-based problem solving. Ways of Thinking in STEM-based Problem Solving: Teaching and Learning in a New Era, 263.
  • Davis, N., Dermody, B. J., Koetse, M., & van Voorn, G. A. (2024). Identifying personal and social drivers of dietary patterns: An agent-Based model of Dutch consumer behavior. Journal of Artificial Societies and Social Simulation, 27(1).
  • Davey, T. (2024). Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and Simulations. Entropy, 26(8), 683.
  • Deb, L. C., & Lanzas, C. In-feed antimicrobial treatment duration on liver abscess prevalence and gut enteric bacteria resistance level in feedlot cattle: An individual-based model.
  • de Borba, J. S., & Gonçalves, S. (2024). One predator and two prey: Coexistence of pumas, guanacos and sheep in Patagonia. arXiv preprint arXiv:2412.02936.
  • De Carvalho, L. F. B. S., de Souza, V. S., Bonatto, A. N., Perez, T. J. A. M., de Freitas, E. P., Barone, D. A. C., ... & de Araujo Fernandes, R. Q. (2024, July). A Multi-agent System Approach for Blue Force Tracking C2 Application Modeling. In Intelligent Systems Conference (pp. 161-181). Cham: Springer Nature Switzerland.
  • Dehkordi, M. A. E. (2024). Simulating Dynamics of Institutions (Doctoral dissertation, Delft University of Technology).
  • De Luque-Villa, M. A., & González-Méndez, M. (2024). Water Management as a Social Field: A Method for Engineering Solutions.
  • Demuth, S., Ed‐Driouch, C., Dumas, C., Laplaud, D., Edan, G., Vince, N., ... & Gourraud, P. A. (2024). Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data. European Journal of Neurology, e16363.
  • de Paulo, K. P., Estombelo-Montesco, C. A., & Tejada, J. (2024). New memory-one strategies of the Iterated Prisoner’s Dilemma: a new framework to programmed human-AI interaction. Discover Psychology, 4(1), 20.
  • Derigent, W., David, M., André, P., Cardin, O., & Najjar, S. (2024). WSN Energy Control by Holonic Dynamic Reconfiguration: Application to the Sustainability of Communicating Materials. Sustainability, 16(18), 8193.
  • Dey, A., & Pasupuleti, R. S. (2024). Interpreting the relevance of Prohibited and Regulated Areas: The case of Protected Monuments of Delhi. Advancing Heritage Innovations in India, 29.
  • Di Lucchio, L., & Modanese, G. (2024). Generation of Scale-Free Assortative Networks via Newman Rewiring for Simulation of Diffusion Phenomena. Stats, 7(1), 220-234.
  • Diaz-Mena, K. S., Kogan, L., & Stucchi, L. (2024). On how breaking traffic rules affects vehicle flow. Journal of Computational Social Science, 1-21.
  • Dieguez Cameroni, F. J., Varela Casadey, F., Boscana, M., Schinatto, F., & Bussoni, A. (2024). Advancing carbon neutrality in Silvopastoral systems: a case study applying agent-based modeling. Agroforestry Systems, 1-16.
  • Dindar, M., Cloude, E. B., & Kiili, K. (2024, September). An Experimental Study of Facial Expressions in Collaborative Teams that Quit
  • Ding, H., & Xie, L. (2024). The applicability of positive information in negative opinion management: An attitude-laden communication perspective. Physica A: Statistical Mechanics and its Applications, 129839.
  • Diouf, E. G., Brévault, T., Ndiaye, S., & Piou, C. (2024). Exploration of the potential of a boosted sterile insect technique to control fruit flies in mango orchards. Pest Management Science.
  • Dodd, E., & Van Limergen, D. (2024). Methods in Ancient Wine Archaeology: Scientific Approaches in Roman Contexts. Bloomsbury Publishing.
  • Dohn, N. B. (2024). Philosophical presuppositions in “Computational thinking”–old wine in new bottles?. Journal of Philosophy of Education, qhae016.
  • Domino, K., Sochan, A., & Miszczak, J. A. (2024). Analytical assessment of workers' safety concerning direct and indirect ways of getting infected by dangerous pathogen. arXiv preprint arXiv:2409.16809.
  • Dong, Q., Yan, R., Xiong, R., Xie, W., Pang, M., & Liang, L. (2024). A study on the decision-making mechanism and promotion policy of urban residents’ domestic waste-classification behavior: evidence from Sichuan province. Environment, Development and Sustainability, 1-37.
  • Dubey, P. K., Singh, B., Gautam, A. K., Singh, D., & Tripathi, M. N. (2025). A State of the Art of Recent Trends in Electric Vehicles Planning. Smart Electric and Hybrid Vehicles: Advancements in Materials, Design, Technologies, and Modeling, 159-176.
  • Duschl, R. A., Bateman, K. M., & Maeng, S. (2024). and Scott McDonald. Handbook of Research on Science Learning Progressions, 233.
  • Eakin, H., Bojórquez-Tapia, L. A., Miquelajauregui, Y., Grave, I., Aguilar, B. H., & Janssen, M. A. (2024). Using exploratory modeling to challenge narratives of risk governance in Mexico City. Proceedings of the National Academy of Sciences, 121(36), e2313191121.
  • Ege, E., & Ajibade, F. (2024). Evaluating the Impact of Barging Activity on Road Decongestion in Apapa Lagos Nigeria. UNILAG Journal of Transport and Logistics, 1(1), 73-82.
  • Ehret, M., Johnston, W. J., & Ritter, T. (2024). From buying centers to buying ecosystems: Advancing the B2B research journey. Industrial Marketing Management.
  • Ekanayake‐Weber, M., Mathew, N., Cunha, D., Payen, N., Grimm, V., & Koenig, A. (2024). It's about time: Feeding competition costs of sociality are affected more by temporal characteristics than spatial distribution. Ecology and Evolution, 14(4), e11209.
  • Ekström, H., Droste, N., & Brady, M. (2024). Modelling forests as social-ecological systems: A systematic comparison of agent-based approaches. Environmental Modelling & Software, 105998.
  • El‐Dalahmeh, M., El‐Dalahmeh, A., & Adeel, U. (2024). Analysing the performance of AODV, OLSR, and DSDV routing protocols in VANET based on the ECIE method. IET Networks.
  • Eloy, A., Wagh, A., Fuhrmann, T., de Deus Lopes, R., & Blikstein, P. (2024, July). Towards Convergence: Characterizing Students’ Design Moves in Computational Modeling Through Log Data with Video and Cluster Analysis. In International Conference on Artificial Intelligence in Education (pp. 413-421). Cham: Springer Nature Switzerland.
  • Elsheikh, A. (2024). Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences. arXiv preprint arXiv:2403.04417.
  • Emami, S., Dehghanisanij, H., & Hajimirzajan, A. (2024). Agent-based simulation model to evaluate government policies for farmers’ adoption and synergy in improving irrigation systems: A case study of Lake Urmia basin. Agricultural Water Management, 294, 108730.
  • Emami, S., & Dehghanisanij, H. (2024). Fault Tree Analysis of Trade-Offs between Environmental Flows and Agricultural Water Productivity in the Lake Urmia Sub-Basin Using Agent-Based Modeling. Water, 16(6), 844.
  • Encinas, D. O., Matto, J., Stella, J., Maccallini, L., & Romero, F. (2024). Dengue Dynamics: Modelling Spread and Environmental Interactions. Journal of Computer Science and Technology, 24(2), e14-e14.
  • Engel, A. (2024). Systems Science for Engineers and Scholars. John Wiley & Sons.
  • Engesvik, S. L., Lindstøl, A., & Meinich, A. H. (2024). Addressing agents' strategies in a cyberwar game (CyberAIMs) (Bachelor's thesis, NTNU).
  • Eramo, R., Nolletti, M., Pomante, L., Pasquale, L., & Pascucci, D. (2024). Model‐driven engineering for simulation models interoperability: A case study in space industry. Software: Practice and Experience.
  • Eshrati, B., Rimaz, S., Yaghoobi, M., Effati, S., Jabbari Nooghabi, M., & Tajzadeh, P. (2024). Modeling and Simulation of the possible routes of transmission of Acinetobacter spp. in the Intensive Care Units: An Agent-Based Computational Study. Iranian Journal of Medical Microbiology, 5-5.
  • Espinoza, F. (2024). Time as a Field: Envisioning Its Qualitative and Quantitative Aspects. In Educational Research and the Question (s) of Time (pp. 559-579). Singapore: Springer Nature Singapore.
  • Estrada-Jimenez, L. A., Kalateh, S., Nikghadam-Hojjati, S., & Barata, J. (2024). An Altruistic-based Framework to Support Collaborative Healing of Manufacturing Resources in a Self-organized Shop-floor. IEEE Access.
  • Fabrin, B. H., Ferrari, D. B., Arraut, E. M., & Neumann, S. (2024). Towards balancing efficiency and customer satisfaction in airplane boarding: An agent-based approach. Operations Research Perspectives, 100301.
  • Faccenda, G., Bogani, A., & Sacchi, S. (2024.) Modelli basati su agenti per lo studio del cambiamento climatico: L’apporto della ricerca psicologica. Sistemi intelligenti, 1-17.
  • Fan, G., Zhang, C., Wang, K., Li, Y., Chen, J., & Xu, Z. (2024). CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System. arXiv preprint arXiv:2406.19720.
  • Fan, W., Shan, C., Wang, S., Liu, T., Wang, F., Meng, X., ... & Liu, Y. (2024). Major Research Fruits. In Unconventional Emergency Management Research (pp. 41-135). Springer, Singapore.
  • Farris, A. V., & McLaughlin, G. (2024). Getting a grip on how we talk about computational practices in science in settings of teacher learning. Journal of Computer Assisted Learning.
  • Fernandes, R. S., & Miranda, J. G. V. (2024). An agent-based model for studying the temperature changes on environments exposed to magnetic fluid hyperthermia. Computers in Biology and Medicine, 170, 108053.
  • Fernandes, Y., & Abosata, N. (2024). Analysing India's Cyber Warfare Readiness and Developing a Defence Strategy. arXiv preprint arXiv:2406.12568.
  • Ferrando, A., Gatti, A., & Mascardi, V. (2024). Geometric and Spatial Reasoning in BDI Agents: a Survey.
  • Finger, R., Henningsen, A., Höhler, J., Huber, R., Rommel, J., & Grebitus, C. (2024). Open Science in Agricultural Economics. Q Open, qoae029.
  • Finnemann, A., Borsboom, D., Waldorp, L., Marsman, M., & van der Maas, H. L. (2024). A theory construction methodology for network theories in psychology.
  • Flandre, N. Y., & Giabbanelli¹, P. J. (2024). Can Large Language Models Learn Conceptual Modeling by Looking at Slide. In Advances in Conceptual Modeling: ER 2024 Workshops, AISA, CMLS, EmpER, QUAMES, JUSMOD, LLM4Modeling, Pittsburgh, PA, USA, October 28–31, 2024, Proceedings (p. 198). Springer Nature.
  • Fluri, L., Yilmaz, A., Bieri, D., Ankenbrand, T., & Perucca, A. (2024). Simulating Liquidity: Agent-Based Modeling of Illiquid Markets for Fractional Ownership. arXiv preprint arXiv:2411.13381.
  • Fogo, G. (2024). Investigating the Interplay of Mitochondrial Dynamics and Proteostasis during Neuronal Ischemia/Reperfusion Injury using Integrated Imaging and Modeling Approaches (Doctoral dissertation).
  • Fonseca, L. L., Böttcher, L., Mehrad, B., & Laubenbacher, R. C. (2024). Metamodeling and Control of Medical Digital Twins. arXiv preprint arXiv:2402.05750.
  • Fortu, Z. J. T. F., Guevarra, L. M. L., Ang, M. R. C. O., & Vergara, K. A. P. (2024). AGENT-BASED MODELING OF UP DILIMAN INTRACAMPUS PEDESTRIAN MOBILITY USING NETLOGO AND GIS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 235-241.
  • Foster, T., Clegg, R. J., Alden, K., & Smets, B. F. (2024). Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0.
  • Franzén, L. K., Lovaco, J., & Villas, F. (2024). SOTA Holistic SoS Engineering. Delft University of Technology (TUD), 23, 01.
  • French, M., Patel, A., Qureshi, A., Saxena, D., & Sengupta, R. (2024a). Agent-based modeling of COVID-19 transmission: A case study of housing densities in Sankalitnagar, Ahmedabad. ISPRS International Journal of Geo-Information, 13(6), 208.
  • Frydenlund, E., Martínez, J., Padilla, J. J., Palacio, K., & Shuttleworth, D. (2024). Modeler in a box: how can large language models aid in the simulation modeling process?. SIMULATION, 00375497241239360.
  • Fuentes, N., Schmutzler, J., & Vargas, A. M. (2024). Unpacking the Multilayered Nature of Entrepreneurial Ecosystems: A Conceptual Complex Adaptive System Model. International Journal of Innovation Studies.
  • Fuhrmann, T., Rosenbaum, L., Wagh, A., Eloy, A., Wolf, J., Blikstein, P., & Wilkerson, M. Right but wrong: How students' mechanistic reasoning and conceptual understandings shift when designing agent‐based models using data. Science Education.
  • Gadsden, G. I., Ferraro, K. M., & Harris, N. C. (2024). Energy efficient homes for rodent control across cityscapes. Environmental Research Letters, 19(8), 084027.
  • Gajewski, Z., McElmurray, P., Wojdak, J., McGregor, C., Zeller, L., Cooper, H., ... & Hopkins, S. (2024). Nonrandom foraging and resource distributions affect the relationships between host density, contact rates and parasite transmission. Ecology Letters, 27(3), e14385.
  • Garcia, J. M. V. (2024). Modelagem baseada em agentes (ABM) para estudo dos efeitos da fecundidade e da longevidade na diversidade genética de populações biológicas (Doctoral dissertation, Universidade de São Paulo).
  • Garibay, O. O., & Garibay, I. Agent-Based Modeling of C. Difficile Spread in Hospitals: Assessing Contribution of High-Touch vs. Low-Touch Surfaces and Inoculations’ Containment Impact. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 173). Springer Nature.
  • Gavidia-Calderon, C., Kordoni, A., Bennaceur, A., Levine, M., & Nuseibeh, B. (2024). The IDEA of Us: An Identity-Aware Architecture for Autonomous Systems. ACM Transactions on Software Engineering and Methodology.
  • Gervasi, V., & Guberti, V. (2024). The Effect of Partial and Temporary Vaccination on African Swine Fever Eradication Rates. Transboundary and Emerging Diseases, 2024.
  • Ghaitaranpour, A., Koocheki, A., & Mohebbi, M. (2024). Multi-agent simulation of doughnut deep fat frying considering two domain heating media and sample flipping. Current Research in Food Science, 100751.
  • Ghashghaei, M. T., Abad, A. A. M., & Taleghani, M. (2024). Presenting a model for explaining the effect of internal and external organizational decision components on the final price of industrial products with a factor-based approach. Journal of Value Creating in Business Management, 3(4), 225-270.
  • Ghoreishi, M., Das, A., & Lindenschmidt, K. E. (2024). Advancement in ice-jam flood risk management: Integrating dynamic adaptive behavior by an agent-based modeling in Fort McMurry, Canada. Journal of Hydrology, 131236.
  • Ghoreishi, M., & Lindenschmidt, K. E. (2024). Unlocking effective ice-jam risk management: Insights from agent-based modeling and comparative analysis of social theories in Fort McMurray, Canada. Environmental Science & Policy, 157, 103731.
  • Giabbanelli, P. J. (2024). Fuzzy Cognitive Maps: Best Practices and Modern Methods. Springer Nature.
  • Ginters, E., Dumpis, U., Liñán, L. C., Eroles, M. A. P., Nazemi, K., Matvejevs, A., & Estrada, M. A. R. (2024). A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks.
  • Glick, A. R., Jones, C., Martignetti, L., Blanchette, L., Tova, T., Henderson, A., ... & Li-Jessen, N. Y. (2024). An integrated empirical and computational study to decipher help-seeking behaviors and vocal stigma. Communications Medicine, 4(1), 228.
  • Golmaryami, S., Nunes, M. L., & Ferreira, P. (2024). The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach. Smart Energy, 100138.
  • Gomes, B. D. C. M., & Wehrmeister, M. A. (2024). Análise de Escalabilidade de Método de Exploração e Mapeamento Bioinspirado no Formato das colmeias de Abelha. Anais do Computer on the Beach, 15, 270-276.
  • Gomez, P., & Swarts, M. Spatiotemporal Modeling Vertically Integrated Project.
  • Gonzalez-Redin, J., Gordon, I. J., Polhill, J. G., Dawson, T. P., & Hill, R. (2024). Navigating Sustainability: Revealing Hidden Forces in Social–Ecological Systems. Sustainability, 16(3), 1132.
  • González-Silva, M. I., & González-Silva, R. A. (2024). Cooperation Dynamic through Individualistic Indirect Reciprocity Mechanism in a Multi-Dynamic Model. Computation, 12(2), 20.
  • Gooijer, N. J. C. Entity-based System Dynamics for Bridge Asset Management.
  • Graefe, C. M. (2024). New Spaces: On Territoriality in an Expansive Era (Doctoral dissertation, University of Minnesota).
  • Greybosh, C. (2024). Bringing Computational Modeling into the Classroom with Custom Block-Based Programming Languages in StarLogo Nova (Doctoral dissertation, Massachusetts Institute of Technology).
  • Grgicevic, L., Coates, E. M., Bye, R. T., Fossen, T. I., & Osen, O. L. (2024). Towards Decision Support in Vessel Guidance Using Multi-Agent Modelling.
  • Grisales, J. A. C., Ceballos, Y. F., Bastidas-Orrego, L. M., Gómez, N. I. J., & Cañola, E. C. (2024). Development of an Agent-Based Model to Evaluate Rural Public Policies in Medellín, Colombia. Sustainability, 16(18), 1-19.
  • Gritter, K., Dobbin, M., Merrill, E., & Lewis, M. (2024). An individual-based movement model for contacts between mule deer (Odocoileus hemionus). Ecological Complexity, 58, 101082.
  • Groeneveld, J., Martinovič, T., Rossi, T., Salamon, O., & Grimm, V. (2024). Prototype Biodiversity Digital Twin: Honey Bees in Agricultural Landscapes. ARPHA Preprints, 5, e124639.
  • Groeneveld, J., Odemer, R., & Requier, F. (2024). Brood indicators are an early warning signal of honey bee colony loss—a simulation-based study. PloS one, 19(5), e0302907.
  • Guesmi, H. A. (2024). A Trust-Driven Optimization of Role-Based Access Control in E-Health Cloud Systems. Journal of Computing & Biomedical Informatics.
  • Guo, Q., He, Y., Chen, Y., & Qiao, C. (2024). Design and application of computational modeling in science education research: a systematic review. Interactive Learning Environments, 1-24.
  • Guo, Z. (2024). Exploring urban mobility and sustainability from a complexity perspective.
  • Güngör, Ö., Günneç, D., Salman, S., & Yücel, E. (2024). Simulation of migration paths using agent-based modeling: The case of Syrian refugees en route to Turkey. Socio-Economic Planning Sciences, 102089.
  • Gürcan, Ö., Szczepanska, T., & Antosz, P. (2024). A Guide to Re-Implementing Agent-based Models: Experiences from the HUMAT Model. arXiv preprint arXiv:2405.03994.
  • Gwon, N., Jeong, W., Kim, J. H., Oh, K. H., & Jun, J. K. (2024). Effects of Intervention Timing on Health-Related Fake News: Simulation Study. JMIR Formative Research, 8(1), e48284.
  • Hahn, U., Assaad, L., & Burton, J. W. (2024). Opinion Averaging versus Argument Exchange. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).
  • Hahn, U., Merdes, C., & von Sydow, M. (2024). Knowledge through social networks: Accuracy, error, and polarisation. Plos one, 19(1), e0294815.
  • Haki, K., Tanriverdi̇, H., Safaei, D., Schmid, M., Aier, S., & Winter, R. (2024). Generativity and Profitability on B2B Innovation Platforms: A Simulation-based Theory Development. Management Information Systems Quarterly, 48(2), 583-612.
  • Hakim, G., Braun, R., & Lipman, J. (2024). Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks. Electronics, 13(11), 2072.
  • Hamann, H. The Two-Bridge Ant Experiment as an Interactive NetLogo Library Model. LNCS 14987 Swarm Intelligence, 255.
  • Han, J., Tan, Q., Ding, Y., & Liu, Y. (2024). Exploring the diffusion of low-carbon power generation and energy storage technologies under electricity market reform in China: An agent-based modeling framework for power sector. Energy, 133060.
  • Han, M., & Uhm, J. (2024). Enhancing Students’ Emotional, Epistemic, and Conceptual Aspects in Scientific Practice through Computational Modeling on a Food Web. International Journal of Science and Mathematics Education, 1-21.
  • Han, S., Lim, J., Hwang, M., & Heo, G. (2024). Enhancing Radiological Emergency Response through Agent-Based Model Case 1: Effectiveness of staged Evacuation.
  • Hare, M. P., Roxburgh, N., Salt, D., & Polhill, G. (2024). Barriers and Model Curation Issues Associated with Rapid Adaptation of Empirical Legacy ABM in Response to Urgent Policy Maker Queries. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 271). Springer Nature.
  • Hartman, C. R. A., Wilkinson, G. S., Razik, I., Hamilton, I. M., Hobson, E. A., & Carter, G. G. (2024). Hierarchically embedded scales of movement shape the social networks of vampire bats. Proceedings of the Royal Society B, 291(2021), 20232880.
  • Hashemi, S. M., Kazemi, M. A. A., Ashlaghi, A. T., & Minooie, M. (2024). The combination of genetic algorithm in the optimization of the stock portfolio in the financial decision of investors. Journal of Value Creating in Business Management, 3(4), 72-88.
  • Hassanpour, S., González, V. A., Zou, Y., Liu, J., & Cabrera-Guerrero, G. (2024). Agent-based post-earthquake evacuation simulation to enhance early-stage architectural layout and non-structural design. Automation in Construction, 165, 105541.
  • Hatlauf, J., Kunz, F., Griesberger, P., Sachser, F., & Hackländer, K. (2024). A stage-based life cycle implementation for individual-based population viability analyses of grey wolves (Canis lupus) in Europe. Ecological Modelling, 491, 110700.
  • Hay, T., & Signer, J. (2024) Simulationsbasierte Evaluierung von Dichteschätzern für unmarkierte Tiere Simulation-based evaluation of density estimators for unmarked animals. Wildbiologische Forschungsberichte, 215.
  • Hazari, T. A., Khalil Ullah, M. I., Pareno, S. I., & Assuad, C. S. A. (2024). A Study on Circular Fashion: Profitability and Environmental Impact Analysis. In EcoDesign for Sustainable Products, Services and Social Systems II (pp. 59-73). Singapore: Springer Nature Singapore.
  • Hazra, I., Weiner, M., Yang, R., Chattejee, A., Southgate, J., Groth, K., & Azarm, S. (2024). Prognostics and Health Management of Unmanned Surface Vessels: Past, Present, and Future. Journal of Computing and Information Science in Engineering, 1-19.
  • Head, B. (2024). Agents Modeling Agents: The Design and Analysis of Multi-level Agent-Based Models (Doctoral dissertation, Northwestern University).
  • He, S., Chen, J., Zhang, P., & Fu, Z. (2024, October). Multi-source Trust Model Based on Blockchain and IoT Edge Task Collaboration. In 2024 IEEE 49th Conference on Local Computer Networks (LCN) (pp. 1-7). IEEE.
  • Herbillon, F., Diouf, E. G., Brévault, T., Haramboure, M., Fellous, S., & Piou, C. (2024). Life history traits of the target pest and transmission routes of the biocide are critical for the success of the boosted Sterile Insect Technique. Current Research in Insect Science, 100101.
  • Higuera, J. R. B., Higuera, J. B., Montalvo, J. A. S., & Crespo, R. G. (2024). Unsupervised Approaches in Anomaly Detection. In Machine Learning for Cyber Physical System: Advances and Challenges (pp. 57-83). Cham: Springer Nature Switzerland.
  • Hofmann, H., & Deutsch, S. Web and App Engineering (T4INF1201). Modulhandbuch.
  • Hong, P. G. (2024). Analysis of Consumer Transaction Behavior on Secondhand Goods Trading Platforms Using an Agent-Based Model. 디지털콘텐츠학회논문지 (J. DCS), 25(7), 1823-1831.
  • Hong, Q. (2024). Development of planning support systems: A systematic review of the Chinese language literature. Transactions in Urban Data, Science, and Technology, 27541231241251409.
  • Hoskinson, J. S., Davison, D. R., Sanders, H., Jiménez-Marín, B., & Michod, R. E. (2024). Translating research on evolutionary transitions into the teaching of biological complexity. II. A NGSS-aligned framework for teaching the hierarchy of life. Evolution: Education and Outreach, 17(1), 1-20.
  • Hosseini, S. Z., Radfar, R., Nasiripour, A. A., & Ghatary, A. R. (2024). Machine Learning Algorithms to Prevent the Spread of Infectious Diseases based on Effective Features in the Diagnosis of Covid-19. Iranian Journal of Information Processing and Management, 39(2), 657-698.
  • Howell, W. J., Dong, Z., & Rojas-Cessa, R. (2024). EOS: Impact Evaluation of Electric Vehicle Adoption on Peak Load Shaving Using Agent-Based Modeling. Energies, 17(20), 5110.
  • Hrab, M. Action Using Agent-Based Modeling. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 56). Springer Nature.
  • Hsu, H. M., & Gourbesville, P. Cellular Automata and Agent-Based Modelling for Inundation Simulation and Disaster Management. Advances in Hydroinformatics—SimHydro 2023 Volume 1: New Modelling Paradigms for Water Issues, 333.
  • Hsu, Y. Y., & Han, H. (2024). Toward volumetric urbanism: Analysing the spatial-temporal dynamics of 3D floor space use in the built environment. Environment and Planning B: Urban Analytics and City Science, 23998083241286592.
  • Hu, L., & Wang, H. (2024). Unplugged Activities in the Elementary School Mathematics Classroom: The Effects on Students’ Computational Thinking and Mathematical Creativity. Thinking Skills and Creativity, 101653.
  • Hu, X., Hong, Y., Cui, Z., Xie, T., & Fu, W. (2024). Evaluating Classical Airplane Boarding Methods Focusing on Higher-Risk Passengers during Post-Pandemics. Transportation Research Record, 03611981241247179.
  • Hu, X., Yang, Z., Sun, J., & Zhang, Y. (2024). When should capital-constrained swap service providers partner with battery lessors?. Transportation Research Part E: Logistics and Transportation Review, 190, 103692.
  • Hua, L. (2024). The impact of environmental taxation on the structure and performance of industrial symbiosis networks: An agent-based simulation study. Heliyon, 10(3), e25675.
  • Huang, H., Sun, B., & Hu, L. (2024). A task Offloading Approach Based on Risk Assessment to Mitigate Edge DDoS Attacks. Computers & Security, 103789.
  • Huang, J., Wang, J., Gong, Y., Xu, N., Zhou, Y., Zhu, L., ... & Zhou, Y. Identification of Optimum Scopes of Environmental Drivers for Schistosome-Transmitting Oncomelania Hupensis Using Agent-Based Model in Dongting Lake Region, China.
  • Huang, K., De Long, J. R., Yan, X., Wang, X., Wang, C., Zhang, Y., ... & Guo, H. (2024). Why are graminoid species more dominant? T rait‐mediated plant–soil feedbacks shape community composition. Ecology, e4295.
  • Huber, R., Kreft, C., Späti, K., & Finger, R. (2024). Quantifying the importance of farmers' behavioral factors in ex-ante assessments of policies supporting sustainable farming practices. Ecological Economics, 224, 108303.
  • Hui, H., Feng, L., & Zhang, R. Chongqing University of Technology, Chongqing, China zrj@ cqut. edu. cn. In Spatial Data and Intelligence: 5th China Conference, SpatialDI 2024, Nanjing, China, April 25–27, 2024, Proceedings (p. 199). Springer Nature.
  • Huo, F. Y., Manrique, P. D., Restrepo, D. J., Woo, G., & Johnson, N. F. (2024). Simple fusion-fission quantifies Israel-Palestine violence and suggests multi-adversary solution. arXiv preprint arXiv:2409.02816.
  • Hussain, I., Elomri, A., Kerbache, L., & El Omri, A. (2024). Smart city solutions: Comparative analysis of waste management models in IoT-enabled environments using multiagent simulation. Sustainable Cities and Society, 105247.
  • Iapăscurtă, V. (2024). Agent-based modeling: revolutionizing tissue engineering. Cells and tissues transplantation. Actualities and perspectives. The 2-nd edition. Chisinau, March 29-30th 2024.
  • >li>IAPĂSCURTĂ, V. (2024). NetLogo integrated development environment for modeling physiological processes.
  • Ibrahimli, U., Hemmrich, S., Zauke, S., & Winkelmann, A. Overcoming Lemon Markets with Business Reputation Ecosystem–A Multi-agent Simulation on Monetary Ratings.
  • Idrissa, S. M., Kadri, C., Bachir, M. I., & Naroua, H. (2024). Computer Simulation models of infectious diseases: Advantages and Limits. International Journal of Science and Research Methodology, 27(7), 10–40.
  • Imanian Ardabily, M., Noghani Dokht Bahmani, M., & Asgharpour Masouleh, A. R. (2024). science production. Ferdowsi University of Mashhad Journal of Social Sciences.
  • Ismail, S., Mostafa, S. A., Baharum, Z., Erianda, A., Jaber, M. M., Jubair, M. A., & Adiya, M. H. (2024). Software Agent Simulation Design on the Efficiency of Food Delivery. JOIV: International Journal on Informatics Visualization, 8(1), 190-197.
  • Ivanjek, L., Perl-Nussbaum, D., Solvang, L., Yerushalmi, E., & Pospiech, G. (2024). Enhancing Mathematization in Physics Education by Digital Tools. In Physics Education Today: Innovative Methodologies, Tools and Evaluation (pp. 35-53). Cham: Springer Nature Switzerland.
  • Izmirlioglu, Y., Pham, L., Son, T. C., & Pontelli, E. (2024). A Survey of Multi-Agent Systems for Smartgrids. Energies (19961073), 17(15).
  • Izquierdo Millán, L. R., Izquierdo Millán, S. S., & Sandholm, W. H. (2024). Agent-Based Evolutionary Game Dynamics.
  • Jackson, M. H. (2024). Modeling of Small Groups in Computational Sciences: A Prospecting Review. Small Group Research, 10464964241279164.
  • Jager, W., & Wang, S. Simulations as a Dialogue Tool: Strengthening Community Engagement and Local Democratic Processes. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 107). Springer Nature.
  • Jalal, J. K., Al-sofy, K. M., Fadhil, F. F., & Mahmood, B. Adaptive routing strategy for emergency applications in smart campus.
  • Jalali, H., Aghamohammadi, H., Vahidnia, M. H., & Mirzahosseini, A. Modeling the effects of PM pollutant on people using a spatio-temporal dynamic model and GIS. Journal of Geomatics Science and Technology, 0-0.
  • Jamshidnejad, A., & De Schutter, B. (2024). A combined probabilistic-fuzzy approach for dynamic modeling of traffic in smart cities: Handling imprecise and uncertain traffic data. Computers and Electrical Engineering, 119, 109552.
  • Janssen, S. D., Viaene, K. P., Van Sprang, P., & De Schamphelaere, K. A. (2024). Modeling Full Life‐Cycle Effects of Copper on Brook Trout (Salvelinus fontinalis) Populations. Environmental Toxicology and Chemistry.
  • Jebelli, N. B. (2024). Computational Modeling of the Social Complexity in GÖBekli Tepe and Earliest Neolithic Communities (Doctoral dissertation, George Mason University).
  • Jetton, C., Hoyle, C., & Campbell, M. I. (2024). Overcoming Design Challenges in Coupled System Device Problems Using Agent-Based. Design Computing and Cognition’24: Volume 2, 259.
  • Jiang, J., Wu, H., Xu, R., Yang, H., Zhang, Y., & Li, J. A Node Protection Method For Improving The Resilience Of Combat System Under Continuous Attack.
  • 조혜인(Jo Hye-in). (2024). Aggregation Behavior in Polymorphic Foragers of the Japanese Carpenter Ant, Camponotus japonicus (Doctoral dissertation, 서울대학교 대학원).
  • Johnson, K., Vermeer, W., Hills, H., Chin-Purcell, L., Barnett, J., Burns, T., ... & Brown, C. H. (2024). Model-Driven Decision Support: A Community-Based Meta-Implementation Strategy to Predict Population Impact. Annals of Epidemiology.
  • Jonnala, R., Liang, G., Yang, J., & Alsmadi, I. (2024). Using Large Language Models in Public Transit Systems, San Antonio as a case study. arXiv preprint arXiv:2407.11003.
  • Joshi, A. R., Chappin, E. J. L., & Doorn, N. (2024). Equity, Urgency, Affordability.
  • Josserand, M., Allassonnière‐Tang, M., Pellegrino, F., Dediu, D., & de Boer, B. (2024). How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents. Cognitive Science, 48(4), e13439.
  • Jun, W., & Ran, X. Q. (2024). Dynamics in Digital Finance and Its Impact on SME Financing. Heliyon, 10(9), E30586.
  • Jung, Y., Qian, C., Barnett-Neefs, C., Ivanek, R., & Wiedmann, M. (2024). Developing an Agent-Based Model that Predicts Listeria spp. Transmission to Assess Listeria Control Strategies in Retail Stores. Journal of Food Protection, 100337.
  • Juretić, D., & Bonačić Lošić, Ž. (2024). Theoretical Improvements in Enzyme Efficiency Associated with Noisy Rate Constants and Increased Dissipation. Entropy, 26(2), 151.
  • Kafai, Y., & Morales-Navarro, L. (2024). Twenty Constructionist Things to Do with Artificial Intelligence and Machine Learning. arXiv preprint arXiv:2402.06775.
  • Kamali, A., Niksokhan, M. H., & Ardestani, M. (2024). Multi-agent system simulation and centralized optimal model for groundwater management considering evaluating the economic and environmental effects of varied policy instruments implemented. Journal of Hydroinformatics, jh2024152.
  • Kappenberger, J., & Stuckenschmidt, H. (2024). A framework for human-centered AI-based public policies. Human-Centered AI, 287.
  • Kang, B., Kumar, H., Lee, M., Chakraborty, B., & Mukhopadhyay, S. (2024). Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction. arXiv preprint arXiv:2404.06460.
  • Karalidis, K., Roumpos, C., Servou, A., Paraskevis, N., & Pavloudakis, F. (2024). Post-mining planning under the prism of land use alternatives: a cellular automata model. Environmental Earth Sciences, 83(13), 413.
  • Karalla, S. A., & Petropoulou, I. (2024). A review on educational robotics and creativity. Global Journal of Engineering and Technology Advances, 20(01), 078-084.
  • Karolemeas, C., Tsigdinos, S., Moschou, E., & Kepaptsoglou, K. (2024). Shared autonomous vehicles and agent based models: a review of methods and impacts. European Transport Research Review, 16(1), 1-45.
  • Karrenberg, C., Berglund, E. Z., & Edwards, E. C. An Agent-Based Modeling Approach to Assess the Social Equity Impacts of Dynamic Pricing Forurban Water Management. Available at SSRN 5002637.
  • Kassa, Y. W., James, J. I., & Belay, E. G. (2024). Cybercrime Intention Recognition: A Systematic Literature Review. Information, 15(5), 263.
  • Katsamakas, E., & Sanchez-Cartas, J. M. (2024). Congestion, network effects and platform competition. Journal of Economic Interaction and Coordination, 1-25.
  • Kaur, M., & Verma, V. K. (2024). Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things. Journal of Network and Computer Applications, 103872.
  • Kaur, P., Ciuti, S., Salter-Townshend, M., & Farine, D. R. (2024). Using an agent based model to inform sampling design for animal social network analysis. bioRxiv, 2024-05.
  • Kazymyr, V. (2024). Modeling of Defensive Drone Swarms with NetLogo. Mathematical Modeling and Simulation of Systems, 377.
  • Kemei, Z., Rotich, T., & Bitok, J. Modelling Population Dynamics Using Age-Structured System Of Partial Differential Equations.
  • Khairunnisa, G., Dewi, O. C., & Alkadri, M. F. (2024, September). Implementation of Green Behavior in Educational Building Through Human-based Retrofits Based on Agent-Based Modeling (ABM). In IOP Conference Series: Earth and Environmental Science (Vol. 1395, No. 1, p. 012023). IOP Publishing.
  • Khajeem Moghadam, A., Saghafian, B., Najarchi, M., & Delavar, M. (2024). Optimizing Stakeholder Behavior for Groundwater Management in Arid and Semi-arid Climates: A Multi-agent System Approach in Karaj Plain, Iran. Iranian Journal of Science and Techno
  • Khanna, A., Rousell, N., Davis, T., Zhang, Y., Sheeler, D., Cioe, P., ... & Kahler, C. (2024). Social network dynamics of tobacco smoking and alcohol use among persons involved with the criminal legal system (PCLS): A modeling study. International Journal of Alcohol and Drug Research.
  • Khasraghi, G. S., & Nejat, A. (2024). Utilizing Agent-Based Modeling for Optimization of Wayfinding in Hospital: A Case Study. HERD: Health Environments Research & Design Journal, 19375867241248593.
  • Khatami, S., & Frantz, C. (2024). Prompt Engineering Guidance for Conceptual Agent-based Model Extraction using Large Language Models. arXiv preprint arXiv:2412.04056.
  • Khazaei, S., & Najafiani, M. (2024). Evaluation of Emergency Evacuation in Residential High-Rise Buildings Communities (Case Study: Punak Town of Zanjan). Emergency Management.
  • Khazaii, J., Khazaei, A., Khayyam, H., & Jazar, R. N. (2024). Evolutionary Game Theory and Innovative Building Strategies. In Nonlinear Approaches in Engineering Application: Automotive Engineering Problems (pp. 283-294). Cham: Springer Nature Switzerland.
  • Kheirinejad, S., Bozorg-Haddad, O., Savic, D., Singh, V. P., & Loáiciga, H. A. (2024). Developing a National-Scale Hybrid System Dynamics, Agent-Based, Model to Evaluate the Effects of Dietary Changes on the Water, Food, and Energy Nexus. Water Resources Management, 1-26.
  • Kilic, O., & Tsang, A. (2024, May). Catfished! Impacts of Strategic Misrepresentation in Online Dating. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (pp. 1011-1019).
  • Kim, G., Park, J., & Heo, G. (2024). Enhancing Radiological Emergency Response through Agent-Based Model Case 2: Time Required for Staff Assemble.
  • Kim, H. (2024). Exploring mobile OTT service user's psychological resistance toward wireless mobile caching networks and telecommunication operator's incentive policy, using agent-based model scenarios.
  • Kim, J., Conte, M., Oh, Y., & Park, J. (2024). From Barter to Market: an Agent-Based Model of Prehistoric Market Development. Journal of Archaeological Method and Theory, 1-40.
  • Kim, S., Fattori, G., & Park, Y. (2024). A simple and efficient Distributed Trigger Counting algorithm based on local thresholds. ICT Express.
  • Kimberly, J. (2024). Simulation Software and Frameworks: There are various software tools and frameworks, such as NetLogo, Mesa.
  • Kimpton, L., Challenor, P., & Salter, J. (2024). Uncertainty Quantification for Agent Based Models: A Tutorial. arXiv preprint arXiv:2409.16776.
  • King, B. (2024) Using Systems Human Factors and Ergonomics to Identify and Mitigate the Risks Associated with Future Invasive Brain-Computer Interfaces.
  • Kirac, E., Shaltayev, D., & Wood, N. (2024). Evaluating the Impact of Citizen Collaboration with Government Agencies in Disaster Response Operations: An Agent-Based Simulation Study. International Journal of Disaster Risk Reduction, 104469.
  • Knapp, A. C., Cruz, D. A., Mehrad, B., & Laubenbacher, R. C. (2024). Ensemble Kalman filter methods for agent-based medical digital twins. bioRxiv, 2024-05.
  • Koning, E., & Gropp, W. Proposal for a Flexible Benchmark for Agent Based Models.
  • Kooijman, S. A. L. M. (2024). Ways to reduce or avoid juvenile-driven cycles in individual-based population models. Ecological Modelling, 490, 110649.
  • Kopels, M. C., & Ullah, I. I. (2024). Modeling post-Pleistocene megafauna extinctions as complex social-ecological systems. Quaternary Research, 1-14.
  • Kosak, O., Kastenmüller, P., Wanninger, C., & Reif, W. (2024, October). An Approach for Extended Swarm Formation Flight with Drones: PROTEASE 2.0. In International Symposium on Leveraging Applications of Formal Methods (pp. 263-280). Cham: Springer Nature Switzerland.
  • Köster, T., Reinhardt, O., Hinsch, M., Bijak, J., & Uhrmacher, A. M. (2024). A Fast Embedded Language for Continuous-Time Agent-Based Simulation. Journal of Artificial Societies and Social Simulation, 27(1), 10.
  • Kreig, J. A., Lenhart, S., Ponce, E., & Jager, H. I. (2024). Agent-based modeling to evaluate the effects of harvesting biomass and hunting on ring-necked pheasant (Phasianus colchicus) populations. Ecological Modelling, 492, 110705.
  • Kraeuter, K., Reith, C., Kolber, B. J., & Miller Neilan, R. (2024). Improved Efficiency and Sensitivity Analysis of 3-D Agent-based Model for Pain-related Neural Activity in the Amygdala. Spora: A Journal of Biomathematics, 10(1), 65-82.
  • Kramberger, T., & Abdelshafie, A. (2024) Simulation-optimization Model In Managing The Empty Container Movements Problem Through Repositioning Strategies.
  • Kritikou, A. I., Nikolaidi, M. E., & Chavianidis, T. (2024). The role of mobile applications in managing everyday stress through breathing. GSC Advanced Research and Reviews, 20(1), 283-299.
  • Kruijning, D. (2024). From policy to action: residents’ responses to a top-down rainwater retention policy in Groningen (Doctoral dissertation).
  • Kuipers, E. (2024). Integrated field-& agent-based modelling: understanding the dynamics of the common barbel in the Meuse river (Master's thesis).
  • Kuljanin, G., Braun, M. T., Grand, J. A., Olenick, J. D., Chao, G. T., & Kozlowski, S. W. (2024). Advancing Organizational Science With Computational Process Theories. The Leadership Quarterly, 101797.
  • Kumar, H., Chakraborty, B., Kang, B., & Mukhopadhyay, S. (2024). Studying the Impact of Stochasticity on the Evaluation of Deep Neural Networks for Forest-Fire Prediction. arXiv preprint arXiv:2402.15163.
  • Kumar, V., Mistri, A., Jain, V., & Ghosh, M. (2024). Computational Engineering for 3D Bioprinting: Models, Methods, and Emerging Technologies. 3D Bioprinting from Lab to Industry, 301-322.
  • Kumari, A. (2024). Fashion Business and Digital Transformation (Impact of Digital Marketing When Styles Meet Screens). BFC Publications.
  • Kuo, P. F., Wen, T. H., Chuang, T. W., Chiu, C. S., Ye, Y. J., & Putra, I. G. B. (2024). Comparing micro-level and macro-level models for epidemic diffusion in the metro system. Journal of Simulation, 1-14.
  • Kürschner, T., Scherer, C., Radchuk, V., Blaum, N., & Kramer‐Schadt, S. (2024). Resource asynchrony and landscape homogenization as drivers of virulence evolution: The case of a directly transmitted disease in a social host. Ecology and Evolution, 14(2), e11065.
  • Kurchyna, V. (2024). Check for updates Can (and Should) Automated Surrogate Modelling Be Used for Simulation Assistance? In Multi-Agent-Based Simulation XXIV: 24th International Workshop, MABS 2023, London, UK, May 29–June 2, 2023, Revised Selected Papers (p. 3). Springer Nature.
  • Kyrychok, T., Klymenko, T., & Bardovskyi, B. (2024, January). Nanoscale fractal analysis of watermarked paper surface topography studied by atomic force microscopy. In Sixteenth International Conference on Correlation Optics (Vol. 12938, pp. 174-177). SPIE.
  • Laili, Y., Gong, J., Hu, K., Zhang, L., & Wang, F. (2024, August). Model Calibration for Agent-Based Simulation Using a Pattern Clustering Network. In Asia Simulation Conference (pp. 152-164). Singapore: Springer Nature Singapore.
  • Lais, R. S., Fitzner, J., Lee, Y. K., & Struckmann, V. (2024). Open-sourced modeling and simulating tools for decision-makers during an emerging pandemic or epidemic–Systematic evaluation of utility and usability: A scoping review update. Dialogues in Health, 100189.
  • Lakmali, R. G. N., Genovese, P. V., & Abewardhana, A. A. B. D. P. (2024). Evaluating the Efficacy of Agent-Based Modeling in Analyzing Pedestrian Dynamics within the Built Environment: A Comprehensive Systematic Literature Review. Buildings, 14(7), 1945.
  • Lammers, D., Groeneveld, J., Grimm, V., Requier, F., & Focks, A.  (2024). Using the Beehave Honey Bee Model Across Climates: Emerging Instead of Imposed Queen Egg-Laying Rate and the Influence of Weather Conditions, Pollen Storage and Brood Size. Pollen Storage and Brood Size.
  • Lan, H., Yang, Y., Fu, H., Liao, H., Liao, L., Huang, S., & Li, X. (2024). Dynamic disaster risk assessment of urban waterlogging on pedestrian flow by intelligent simulation of hydrodynamics coupled with agent-based models in Chao-yang river basin of Nanning, China. Stochastic Environmental Research and Risk Assessment, 1-24.
  • Langbeheim, E., Saba, J., & Levy, S. T. (2024). Invented models–relating students’ constructions of computational models to their learning gains. Interactive Learning Environments, 1-16.
  • Larson Jr, J. R., Cornell, C. A., & Aramovich, N. P. (2024). Building Better Theories: Prediction Intervals as a Tool for Theory Testing and Improvement. Basic and Applied Social Psychology, 1-23.
  • Lavaud, R., Marn, N., Domingos, T., Filgueira, R., Lika, K., Rakel, K., & Klanjšček, T. (2024). Metabolic organization across scales of space and time. Ecological Modelling, 110951.
  • Leal Martinez, D. (2024). Design and optimization of a decentralized multi-robot exploration behavior taking into account energy constraints.
  • Lee, H. S., Gweon, G. H., & Pallant, A. (2024). SupportingSimulation-MediatedScientific InquirythroughAutomatedFeedback. Uses of Artificial Intelligence in STEM Education, 198.
  • Lee, I., Perret, B., DeLisi, J., Minocha, S., Peterson, K., & Malyn-Smith, J. (2024). The Decoding Approach For Integrating Computational Thinking Into High School Science Classrooms. In Edulearn24 Proceedings (pp. 5058-5067). IATED.
  • Lee, L. Y. (2024). Taxing Tobacco Companies To Close The Interracial Wealth Gap: An Alternative Policy to Address Compensation for Racial Discrimination.
  • Ledda, M., Pluchino, A., & Ragusa, M. (2024). Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach.
  • Lemmen, C., Hokamp, S., Örey, S., & Scheffran, J. (2024). Viable North Sea (ViNoS): A NetLogo Agent-based Model of German Small-scale Fisheries. Journal of Open Source Software, 9(95), 5731.
  • Leonard-Duke, J., Agro, S. M., Csordas, D. J., Bruce, A. C., Eggertsen, T. G., Tavakol, T. N., ... & Peirce, S. M. (2024). Multi-Scale Computational Model of Microvascular Remodeling in Idiopathic Pulmonary Fibrosis. bioRxiv, 2024-03.
  • Li, N. (2024). Analyzing the Complexity of Public Opinion Evolution on Weibo: A Super Network Model. Journal of the Knowledge Economy, 1-36.
  • Li, J., Feng, Z., Zhang, W., Zhu, D., & Huang, Z. (2024). Evolution of green travel behaviour on dynamic social networks. Travel Behaviour and Society, 37, 100866.
  • Li, J., Niu, J., & Wang, J. (2024). Agent-based modelling and simulation of elderly residents’ dynamic usage patterns in neighbourhood squares during summer. Building and Environment, 111598.
  • Li, J., Yuan, P., Liang, L., & Cao, J. (2024). Enhancing Supply Chain Resilience in Prefabricated Buildings: The Role of Blockchain Technology in Volatile, Uncertain, Complex, and Ambiguous Environments. Buildings, 14(9), 3006.
  • Li, X., Xu, Y., Zhang, Y., & Malthouse, E. C. (2024). Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures. arXiv preprint arXiv:2410.13909.
  • Liang, H., Qian, Y., Zhu, M., & Chen, Y. (2024). Exploring the role of social networks in modeling drivers’ route choice behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 104, 154-169.
  • Liang, L., Phan, H., & Giabbanelli, P. J. (2024). Experimental evaluation of a machine learning approach to improve the reproducibility of network simulations. SIMULATION, 00375497241229753.
  • Lim, J., Vogel-Heuser, B., & Kovalenko, I. (2024). Large Language Model-Enabled Multi-Agent Manufacturing Systems. arXiv preprint arXiv:2406.01893.
  • Lin, C. Y., OA, M., Dhakal, S., Zipper, S., & Marston, L. (2024). Pychamp: A Crop-Hydrological-Agent Modeling Platform for Groundwater Management. Available at SSRN 4814225.
  • Lin, H., Zheng, H., & Wang, F. (2024). The influence of school climate on Chinese primary school students’ bystander behavior in bullying: A variable-and person-centered dual-perspective analysis. Psychology of Violence.
  • Lin, J. H., Quan, Y. J., & Han, B. P. (2024). MetaIBM: A Python-based library for individual-based modelling of eco-evolutionary dynamics in spatial-explicit metacommunities. Ecological Modelling, 492, 110730.
  • Lin, L., Wei, X., & Chen, Y. (2024). Evolution of Policy-Driven Ecosystem of Original Innovation Talents. International Journal of Innovation Studies.
  • Lin, S., Wang, K., & Liu, X. Y. (2024, November). Analyzing Cascading Outbreak of GameStop Event: A Practical Approach Using Network Analysis and Large Language Models. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 428-436).
  • Listopad, S., Matsoula, V., & Luchko, A. (2024). Modeling reflection in artificial intelligence systems: state of art and prospects. In ITM Web of Conferences (Vol. 59, p. 04005). EDP Sciences.
  • Little, C. J., Banville, P. E., Ford, A. T., & Germain, R. M. (2024). Spatial fingerprint of consumer body size and habitat preference on resource distribution. bioRxiv, 2024-06.
  • Liu, J. (2024). Marketing strategy matching algorithm under Artificial Intelligence. 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE).
  • Liu, K., Jiang, G., Cheng, X., Hu, X., & Che, C. (2024). Strategic Cooperation in Ride-Hailing: Analyzing Operational Efficiency in San Francisco.
  • Liu, S. H., Weber, E. S., Manz, K. E., McCarthy, K. J., Chen, Y., Schüffler, P. J., ... & Tracy, M. (2024). Assessing the Impact and Cost-Effectiveness of Exposome Interventions on Alzheimer’s Disease: A Review of Agent-Based Modeling and Other Data Science Methods for Causal Inference. Genes, 15(11), 1457.
  • Liu, W., Meng, Q., Zhi, H., Li, Z., & Hu, X. (2024). A review of agent-based modeling in construction management: an analytical framework based on multiple objectives. Journal of Civil Engineering and Management, 30(3), 200-219.
  • Liu, X., Tan, Z., Liang, L., & Li, G. (2024). A Multidimensional Trust Evaluation Mechanism for Improving Network Security in Fog Computing. IEEE Transactions on Industrial Informatics.
  • Liu, X., Zhang, L., Zhen, J., & Wang, W. (2024). Planning for service space of medium-and long-term shelters based on multi-agent evacuation simulation. Natural Hazards, 1-28.
  • Liu, Y., Zhou, Y., Yang, L., & Xin, Y. (2024). Simulating staff activities in healthcare environments: An empirical multi-agent modeling approach. Journal of Building Engineering, 108580.
  • Liu, Z., Gearty, Z., Richard, E., Orrill, C. H., Kayumova, S., & Balasubramanian, R. (2024). Bringing computational thinking into classrooms: a systematic review on supporting teachers in integrating computational thinking into K-12 classrooms. International Journal of STEM Education, 11(1), 51.
  • Liu, Z., Wang, Z., Chen, L., Tang, T., & Shen, Z. (2024). An Agent-Based Approach to Simulating Evacuation in Elderly Care Facilities with Disabled Older Adults. International Journal of Disaster Risk Reduction, 104815.
  • Lopate, M. Z. (2024). Complexity and Great Power Decline (Doctoral dissertation, The Ohio State University).
  • López, A. B., Pastor-Galindo, J., & Ruipérez-Valiente, J. A. (2024). Frameworks, Modeling and Simulations of Misinformation and Disinformation: A Systematic Literature Review. arXiv preprint arXiv:2406.09343.
  • López-Vargas, J. C., Meisel, J. D., Cárdenas-Aguirre, D. M., & Medina, P. (2024). Coordination mechanisms applied to logistical systems for local disaster preparedness: a Latin American case. Journal of Humanitarian Logistics and Supply Chain Management.
  • Lopolito, A., Caferra, R., Nigri, A., & Morone, P. (2024). An evaluation of the impact of mitigation policies on health and the economy by managing social distancing during outbreaks. Evaluation and Program Planning, 103, 102406.
  • Louis, V., Page, S. E., Tansey, K. J., Jones, L., Bika, K., & Balzter, H. (2024). Tiger Habitat Quality Modelling in Malaysia with Sentinel-2 and InVEST. Remote Sensing, 16(2), 284.
  • Lovaco, J., Munjulury, R. C., Staack, I., & Krus, P. (2024). LARGE LANGUAGE MODEL-DRIVEN SIMULATIONS FOR SYSTEM OF SYSTEMS ANALYSIS IN FIREFIGHTING AIRCRAFT CONCEPTUAL DESIGN. In 34th Congress of the International Council of the Aeronautical Sciences, 2024.
  • Lu, H. C., & Lee, H. W. (2024). Agents of Discord: Modeling the Impact of Political Bots on Opinion Polarization in Social Networks. Social Science Computer Review, 08944393241270382.
  • Lu, X. S., Guo, R. Y., Huang, H. J., & Wang, S. H. (2024). Potential-based dynamic parking navigation for autonomous vehicles: Near-priority vs. distant-priority. Transport Policy.
  • Luo, X., & Du, L. (2024, July). Energy consumption simulations of rural residential buildings considering differences in energy use behavior among family members. In Building Simulation (pp. 1-24). Tsinghua University Press.
  • Lynch, A. R., Bradford, S., Zhou, A. S., Oxendine, K., Henderson, L., Horner, V. L., ... & Burkard, M. E. (2024). A survey of chromosomal instability measures across mechanistic models. Proceedings of the National Academy of Sciences, 121(16), e2309621121.
  • Ma, Y., Hu, S., Li, X., Wang, Y., Liu, S., & Cheong, K. H. (2024). Students Rather Than Experts: A New AI For Education Pipeline To Model More Human-Like And Personalised Early Adolescences. arXiv preprint arXiv:2410.15701.
  • Mack, D. October Surprises: Threshold Dynamics. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 77). Springer Nature.
  • Maddah, N., & Heydari, B. (2024). Building back better: Modeling decentralized recovery in sociotechnical systems using strategic network dynamics. Reliability Engineering & System Safety, 110085.
  • Mahajan, S. (2024). Navigating the cohesion-diversity trade-off: understanding the role of facilitators in co-creation using agent-based modelling. Philosophical Transactions A, 382(2285), 20240093.
  • Mahajana, S. Navigating the Cohesion-Diversity Tradeoff: Understanding the Role of Facilitators in Co-Creation Using ABM.
  • Maharani, S. N., & Rahmawati, S. A. (2024). Financial Revolution through Agent-based Artificial Simulation Computational Models for Predicting Market Behavior. KnE Social Sciences, 624-640.
  • Maiwald, J., & Schütte, T. (2024). Prosumer Impact on Cellular Power Systems. Energies, 17(9), 2195.
  • Makarenya, T. A., Mannaa, A. S., Kalinichenko, A. I., & Petrenko, S. V. Cognitive Modeling as a Forecasting Tool. International Journal on Smart Sensing and Intelligent Systems, 17(1).
  • Malanson, G. P., Dullinger, S., Pauli, H., Winkler, M., Saccone, P., & Jiménez‐Alfaro, B. (2024). Species richness responds to buffer effectiveness and competitive priorities in simulations of alpine microrefugia. Journal of Biogeography.
  • Malikov, M. Rationality in Mixed Environments. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 243). Springer Nature.
  • Malliaros, P., Pacheco-Jaramillo, A., & Robertson-Dean, M. (2024). Are in-kind transfer vouchers as flexible and efficient as cash?.
  • Mangold-Döring, A., Buddendorf, W. B., van den Brink, P. J., & Baveco, J. M. (2024). How relevant are temperature corrections of toxicity parameters in population models for environmental risk assessment of chemicals?. Ecological Modelling, 498, 110880.
  • Mannan, S., Fukuda, M., Stiber, M., & Si, D. (2024). Analysis and Improvement of MASS-based GIS.
  • Mansury, Y. (2024). I Won’t Get Caught: An Agent-Based Model of Corruption with Incomplete Information. In The Indonesian Economy and the Surrounding Regions in the 21st Century: Essays in Honor of Iwan Jaya Azis (pp. 277-298). Singapore: Springer Nature Singapore.
  • Manzi, D., & Calderoni, F. (2024). The resilience of drug trafficking organizations: Simulating the impact of police arresting key roles. Journal of Criminal Justice, 91, 102165.
  • Manzo, G. (2024). Antecedents of generative thinking in analytical sociology: the contribution of Tom Fararo1. The Journal of Mathematical Sociology, 1-22.
  • Marcum-Dietrich, N. I., Bruozas, M., Becker-Klein, R., Hoffman, E., & Staudt, C. (2024). Precipitating Change: Integrating Computational Thinking in Middle School Weather Forecasting. Journal of Science Education and Technology, 1-18.
  • Marino Carvalho, V. (2024). Towards a Reconciliation Between Simulational, Ludic, and Historiographical Practices in Research. Simulation & Gaming, 10468781241248705.
  • Marques, A. T., Crispim-Mendes, T., Palma, L., Pita, R., Moreira, F., & Beja, P. (2024). Using individual-based demographic modelling to estimate the impacts of anthropogenic mortality on territorial predators. Ecological Modelling, 493, 110752.
  • Masaeli, Y., & Ahmadi, A. (2024). Assessing the suitability of groundwater resources based on farmers’ predicted intentions and human–water feedbacks. Journal of Hydrology, 132057.
  • Mashood, K. K., & Chandrasekharan, S. (2024). The learning of modeling. In The Routledge Handbook of Philosophy of Scientific Modeling (pp. 412-426). Routledge.
  • Maslova, I. I., Manolov, A. I., Glushchenko, O. E., Kozlov, I. E., Tsurkis, V. I., Popov, N. S., ... & Ilina, E. N. (2024). Limitations in creating artificial populations in agent-based epidemic modeling: a systematic review. Journal of microbiology, epidemiology and immunobiology, 101(4), 530-545.
  • Matsuda, M., & Nishi, T. (2024). Enterprise E-Profiles for Construction of a Collaborative Network in Cyberspace. In Enterprise Interoperability X: Enterprise Interoperability Through Connected Digital Twins (pp. 75-86). Cham: Springer International Publishing.
  • Matsui¹, K., & Hashimoto, T. Construction of a Simulation Model Regarding People's Reactions to the Stay-at-Home Request Policy During the COVID-19 Pandemic in Japan and Evaluation Using Linear Regression Analysis. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 89). Springer Nature.
  • Matto, J., Stella, J., Maccallini, L., Romero, F., & Encinas, D. (2024). Dengue dynamics: modeling spread and environmental interactions. Journal of Computer Science & Technology, 24.
  • Matyjaszkiewicz, A., & Sharpe, J. (2024). LimbNET: collaborative platform for simulating spatial patterns of gene networks in limb development. bioRxiv, 2024-08.
  • Maupin, C. K., Mohan, G., Choudhury, A., & Deepak, P. Network-based approaches to leadership: An organizing framework, review, and recommendations.
  • Mazzetto, S. (2024). Interdisciplinary Perspectives on Agent-Based Modeling in the Architecture, Engineering, and Construction Industry: A Comprehensive Review. Buildings, 14(11), 3480.
  • Medina-Labrador, M., Marroquin-Ciendua, F., Bonelo, J. G., Gómez, D. M., & Betancourt, M. V. P. Obstacles, Social Norms and Communication to Improve Service in Mass Transport. An Analysis in Bogotá’s Bus Rapid Transit System.
  • Meister, H., Meister, G., & Grycko, E. (2024). Does the Nash Solution Concept persist for Intelligent Agents in a Dynamic Game Environment?.
  • Meseguer, G. O., & Serrano, J. L. (2024). Implementation and training of primary school teachers in computational thinking: a systematic review. Revista Iberoamericana de Educación a Distancia, 27(1), 255-281.
  • Meskini, F. Z., & Aboulaich, R. (2024). Insurance Based on Waqf and Blockchain Technology: A Strong Social Impact & Efficiency. WSEAS Transactions on Business and Economics, 21, 741-752.
  • Mesquita, R. P., Leal, F., & De Queiroz, J. A. (2024). DIGITAL TWINS IN THE RETAIL INDUSTRY: A SYSTEMATIC LITERATURE REVIEW.
  • Metzcar, J. (2024). Toward a General Framework of Multicellular Systems Biology with Application to Cellular Motility and Phenotype Control (Doctoral dissertation, Indiana University).
  • Micheletti, T., Wimmler, M. C., Berger, U., Grimm, V., & McIntire, E. J. (2024). Beyond guides, protocols and acronyms: Adoption of good modelling practices depends on challenging academia's status quo in ecology. Ecological Modelling, 496, 110829.
  • Michelini, M., Polizzi, E., & Vilone, D. (2024). Bad Polarization in Structurally Cohesive Communities.
  • Miller Neilan, R. (2024). BODINE: Enhancing NetLogo3D Simulations: Computational Efficiency and Online Accessibility of a Pain Model.
  • Milne, R. J., Cotfas, L. A., Delcea, C., Crăciun, L., & Molănescu, A. G. (2024). Airplane Seating Assignment Greedy Algorithms that Separate Passengers Likely to be Susceptible to Infectious Disease from those Likely to be Infectious. IEEE Access.
  • Mlika, F., Karoui, W., & Romdhane, L. B. (2024). Blockchain solutions for trustworthy decentralization in social networks. Computer Networks, 110336.
  • Mo, C. Y. J. (2024). Design of Reinforcement Learning Control in Agent-Based Modeling: An Investigation of Systemic Risks in Interbank Lending Market (Doctoral dissertation, Stevens Institute of Technology).
  • Mobinizadeh, M., Mohammadshahi, M., Aboee, P., Fakoorfard, Z., Olyaeemanesh, A., & Mohamadi, E. (2024). The Application of System Simulation in the Health Sector: A Rapid Review. Decision Making in Healthcare Systems, 11-17.
  • Moehler, M., & Thrasher, J. (2024). New Approaches to Social Contract Theory: Liberty, Equality, Diversity, and the Open Society. Oxford University Press.
  • Mohamed, Y. I., Khaleed, M., Ehab, S., Hassan, O. A., Fares, H. M., & Mohamed, O. A. Modeling and simulation of tumor growth.
  • Mohammadi, M., & Gharakhani, S. (2024). Simulating Institutional Change for Governing Natural Common-Pool Resources: An Agent-Based Model Approach. Research Square.
  • Mohands, N., Bayhan, S., Sanfilippo, A., & Rub, H. A. (2024). Decentralized PV Energy Trading: A Case Study of Residential Households in Qatar. IEEE Access.
  • Moncada, J. A., Sanjab, A., & Delarue, E. The framing of decision-making under uncer-tainty may shape the adoption patterns of solar PV panels.
  • Montgomery, V. A., Wood‐Yang, A. J., Styczynski, M. P., & Prausnitz, M. R. (2024). Feasibility of engineered Bacillus subtilis for use as a microbiome‐based topical drug delivery platform. Bioengineering & Translational Medicine, e10645.
  • Morales, D., Canessa, E., & Chaigneau, S. E. (2024). An Agent-Based Model of Foraging in Semantic Memory. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).
  • Moreira, F., Dias, F. S., Dertien, J., Hasse, A. C., Borda-de-Água, L., Carvalho, S., ... & Fernández, N. (2024). Guidelines for connectivity conservation and planning in Europe. ARPHA Preprints, 5, e129021.
  • Mosiakova, I., Levchenko, F., Kalinina, L., Rogoza, V., & Pelekh, V. (2024). Transforming education for the STEM era. Amazonia Investiga, 13(79), 123-140.
  • Muhammad, R. F., & Kasahara, S. (2024). Agent-based simulation of fake news dissemination: the role of trust assessment and big five personality traits on news spreading. Social Network Analysis and Mining, 14(1), 75.
  • Muir, E. J. (2024). Exploring the Swimming Behavior of a Hydromechanical Copepod in the Context of Positive Density Dependence (Doctoral dissertation, University of South Florida).
  • Mühlenbernd, R. (2024). How to use Evolutionary Game Theory to study evolutionary aspects of grammar. Language change for the worse, 281.
  • Mulder, K., Heierhoff, H., Lee, S. M., Tsou, J. J. Y., & Chen, W. (2024). Simulation of Polymer Fractal Formation Using a Triangular Network Growth Model. Langmuir.(2024). S
  • Murgatroyd, P., & Gaffney, V. Modelling the Logistics of Mantzikert.
  • Murphy, D., Duncan, R. G., Chinn, C. A., Danish, J., Hmelo-Silver, C. E., Ryan, Z., ... & Stiso, C. Students’ Prioritization of Initial and Direct Causal Mechanisms During Model Evaluation.
  • Musaeus, L. H., & Musaeus, P. (2024). Computational Thinking and Modeling: A Quasi-Experimental Study of Learning Transfer. Education Sciences, 14(9), 980.
  • Muttaqien, I. Y., Tanugraha, S., Puspitaningrum, J. E., & Manik, H. A. W. (2024). Evaluation of Emergency Access Evacuation Routes Using Agent-Based Model Application. Journal of Artificial Intelligence in Architecture, 3(2), 71-80.
  • Nabinejad, S., & Schüttrumpf, H. (2024). Agent-Based Modeling for Household Decision-Making in Adoption of Private Flood Mitigation Measures: The Upper Kan Catchment Case Study. Water, 16(14), 2027.
  • Nallur, V., Aghaei, P., & Finlay, G. (2024). Different Facets for Different Experts: A Framework for Streamlining The Integration of Qualitative Insights into ABM Development. arXiv preprint arXiv:2408.15725.
  • Nallur, V., Aghaei, P., & Finlay, G. (2024). Systematic Experimentation Using Scenarios in Agent Simulation: Going Beyond Parameter Space. arXiv preprint arXiv:2407.16294.
  • Naraghi, N., Feng, Z., Lovreglio, R., Vishnupriya, V., Wilkinson, S., & Daemei, A. B. (2024). Simulating and visualising indoor seismic damage: A systematic literature review. International Journal of Disaster Risk Reduction, 104979.
  • Neef, N. E., Egner, L. E., & Klöckner, C. A. (2024). Subsidy free-riding is positively correlated to the development of energy efficiency in the housing stock. Environment, Development and Sustainability, 1-17.
  • Nemichel, M., Timouyas, M., Regragui, Y., Bezza, H., & Laboratery, I. M. I. (2024). Dynamic Agent-Based Model for COVID-19 Epidemic Spread Using Social Network Information. In International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023): Advanced Intelligent Systems on Digital Health Technology, Volume 2 (p. 436). Springer Nature.
  • Nguyen, D. T. (2024). Hybrid Simulation-based Lean Management Methodology to Improve the Sustainability of the Construction Phase (Doctoral dissertation, University of Kassel).
  • Nica, I. (2024). Bibliometric mapping in the landscape of cybernetics: insights into global research networks. Kybernetes.
  • Nicholson, J., & Ridgway, J. (2024). New viruses are inevitable; pandemics are optional—Lessons for and from statistics. Teaching Statistics.
  • Niehorster-Cook¹, L., Marghetis¹, T., & Smaldino, P. Rational Voter Theory: An Agent-Based. In Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas (p. 258). Springer Nature.
  • Niki, M., & Kito, T. (2024). Evaluation of Micro-Level Circularity Indicators Using Agent-Based Modelling. In EcoDesign for Sustainable Products, Services and Social Systems II (pp. 205-222). Singapore: Springer Nature Singapore.
  • Nikounam Nezami, M., Toloie Eshlaghy, A., & Iranban, S. J. (2024). Modelling and simulation of supply chain resilience for unnecessary perishable foods using an agent-based modeling approach. Journal of Industrial Engineering and Management Studies, 84-97.
  • Nilsson Bruno, A., & Schüssler, M. (2024). Determining evacuation times of different stadium layouts using agent-based modelling.
  • Niu, T., Huang, H., Du, Y., Zhang, W., Shi, L., & Zhao, R. (2024). General Automatic Solution Generation of Social Problems. arXiv preprint arXiv:2401.13945.
  • Niu, T., Zhang, W., & Zhao, R. (2024). Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning. arXiv preprint arXiv:2402.02388.
  • Niu, F. Urban Spatial Evolution Simulation: Theory, Method, and Practice. Springer Nature.
  • Nourali, Z., Shortridge, J. E., Bukvic, A., Shao, Y., & Irish, J. L. (2024). Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding. Water, 16(2), 263.
  • Ntomora, P. I., & Petrakos, K. (2024). Leveraging social network analysis in education through social robots: A review. Global Journal of Engineering and Technology Advances, 20(01), 055-066.
  • Nurulloyev, F. (2024). МЕТОДИКА ОБУЧЕНИЯ ШКОЛЬНИКОВ СОВРЕМЕННЫХ ПРОГРАММНЫХ СРЕДСТВ. ЦЕНТР НАУЧНЫХ ПУБЛИКАЦИЙ, 45(45).
  • Oesterling, N., Ambrose, G., & Kim, J. (2024). Understanding the Emergence of Computational Institutional Science: A Review of Computational Modeling of Institutions and Institutional Dynamics. International Journal of the Commons, 18(1).
  • Ojha, N. K., Dubey, S. K., & Vaish, A. (2024). Computational Thinking Tools: Review and Current Status. Revolutionizing Curricula Through Computational Thinking, Logic, and Problem Solving, 151-166.
  • Oliveira, H. S. (2024). Optimizing Crowd Evacuation: Evaluation of Strategies for Safety and Efficiency.
  • Olmez, S., Heppenstall, A., Ge, J., Elsenbroich, C., & Birks, D. (2024). Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support. Journal of Simulation, 1-19.
  • Olsen, M., Kuhn, D. R., & Raunak, M. S. (2024). Explaining the impact of parameter combinations in agent-based models. Journal of Computational Science, 102342.
  • Onai, A., & Tanaka, N. (2024). Categorizing resident evacuation behaviors within a flood-protected area. International Journal of River Basin Management, 1–16.
  • Onsomu, O. N., Terciyanlı, E., & Yeşilata, B. (2024). Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources. Engineering Reports, e12995.
  • Opalski, M., Szufel, P., Kamiński, B., Mashatan, A., & Pralat, P. (2024). Optimal roadworks schedule in multi-agent transportation models. European Journal of Transport and Infrastructure Research, 24(4), 41-61.
  • Orduña Moncada, A. F. (2024). Assessing the influence of communication in an agglomeration bonus model as a strategy for land conservation (Doctoral dissertation, BTU Cottbus-Senftenberg).
  • Orrick, K., Sommer, N., Rowland, F., & Ferraro, K. Predator–prey interactions across hunting mode, spatial domain size, and habitat complexities. Ecology, e4316.
  • Osborne, J. M. (2024). An adaptive numerical method for multi–cellular simulations of tissue development and maintenance. Journal of Theoretical Biology, 111922.
  • Osorio, C., Fuster, N., Chen, W., Men, Y., & Juan, A. A. (2024). Enhancing Accessibility to Analytics Courses in Higher Education through AI, Simulation, and e-Collaborative Tools. Information, 15(8), 430.
  • Ouattara, Y., & Lang, C. (2024). Enhancing Connectivity and Energy Efficiency in Mobile Wireless Sensor Networks with SHEM. Journal of Advances in Computer Networks, 12(2).
  • Ouhssini, M., Afdel, K., Akouhar, M., Agherrabi, E., & Abarda, A. (2024). Advancements in detecting, preventing, and mitigating DDoS attacks in cloud environments: A comprehensive systematic review of state-of-the-art approaches. Egyptian Informatics Journal, 27, 100517.
  • Ouhssini, M., Afdel, K., Akouhar, M., Agherrabi, E., & Abarda, A. Egyptian Informatics Journal.
  • Palacios, J. L. D. (2024). MATHEMATICAL MODELING OF PHAGE-BACTERIA POPULATION DYNAMICS.
  • Palmeiro, L. L., Marín, A. A. L., & De las Heras Pérez, M. (2025). Strengths and Opportunities of Artificial Intelligence in Modeling, Argumentation, and Inquiry in the Experimental Science Classroom. In Utilizing ICT for Didactics of Social and Experimental Sciences (pp. 73-94). IGI Global.
  • Palmer, S., Ciubotariu, I., Ofori, R., Saenz, M., Ellison, B., & Prescott, M. P. (2024). School Nutrition Stakeholders Find Utility in MealSim: An Agent-Based Model. Journal of Nutrition Education and Behavior.
  • Papadimitriou, F. (2024). Complexity, Non-Locality and Riddledness in Landscape Dynamics. In Modelling Landscape Dynamics: Determinism, Stochasticity and Complexity (pp. 119-133). Wiesbaden: Springer Fachmedien Wiesbaden.
  • Pan, H., & Qian, J. (2024). Blockchain-Driven Music Copyright Distribution: An Agent-Based Modeling Perspective. International Journal of Global Economics and Management, 4(1), 594-600.
  • Pan, M., Zhao, X., & Li, Z. (2024). Diffusion mechanism of green building in industrial clusters: An agent-based modeling approach. Developments in the Built Environment, 100504.
  • Pang, J., Shi, Y., & Dai, J. (2024). An Intelligent Evaluation Method of Supply-Demand Matching Degree Based on ELECTRE III and VIKOR in Cloud Manufacturing Platform. IEEE Access.
  • Panizza, E. (2024). Modeling Flood Displacement Dynamics: Integrating Household Characteristics and Assessing Policy Implications. A study in the Khartoum State, Sudan.
  • Parra-Sanchez, Á., Zorrilla-Muñoz, V., Martinez-Navarrete, G., & Fernandez, E. (2024, August). Agent-Based Modeling: Investigating Technological Perception’s Impact on Quality of Life in Older Adults with Age-Related Macular Degeneration. In The International Conference on Innovations in Computing Research (pp. 631-640). Cham: Springer Nature Switzerland.
  • Park, D., Hong, J., & Ryu, D. (2024). Heterogeneous expectations in the housing market: a sugarscape agent-based model. Journal of Housing and the Built Environment, 1-25.
  • Patel, J. (2024). Algorithmic approaches to simulating animal movement using agent-based models.
  • Pathak, S., & Balakrishnan, P. V. (2024). The paradox of product scarcity: Catalyzing the speed of innovation diffusion. Journal of the Academy of Marketing Science, 1-21.
  • Patrick, S. M. (2024). Agent-Based Modelling for the Cost-Benefit Analysis of Adaptation Strategies: A Case Study from Inuit Nunangat. Journal of Computer Applications in Archaeology, 7(1).
  • Pardo, G. E., Cuevas, L. B., Pacheco‐Otalora, L. F., & Oruro, E. M. (2024). Altered Patterns of Maternal Behavior Transitions in Rats Exposed to Limited Bedding and Nesting Material Paradigm. Brain and Behavior, 14(10), e70113.
  • Parviziomran, E., Elliot, V., & Bergqvist, R. (2024). Financing dynamics in sustainable heavy-duty road transport: An agent-based modeling approach. Transportation Research Part D: Transport and Environment, 132, 104258.
  • Pecoraro, F., Accordino, F., Cecconi, F., & Paolucci, M. ABM for Simulating the Access to Elective Surgery Services: The Issue of Patient Mobility in Italy. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 73). Springer Nature.
  • Pektas, E. (2024). Second Grade Student Engagement in Computational Thinking.
  • Pettrachin, A., Gabrielli, L., Kim, J., & Ludwig-Dehm, S. (2024). Did exposure to asylum seeking migration affect the. Computational Research in Ethnic and Migration Studies, 193.
  • Phadke, A., Medrano, F. A., Sekharan, C. N., & Chu, T. (2024). An Analysis of Trends in UAV Swarm Implementations in Current Research: Simulation Versus Hardware. Drone Systems and Applications.
  • Phadke, A., Medrano, F. A., Chu, T., Sekharan, C. N., & Starek, M. J. (2024). Modeling Wind and Obstacle Disturbances for Effective Performance Observations and Analysis of Resilience in UAV Swarms. Aerospace, 11(3), 237.
  • Pierron, M., Sueur, C., Shimada, M., MacIntosh, A. J., & Romano, V. (2024). Epidemiological Consequences of Individual Centrality on Wild Chimpanzees. American Journal of Primatology, e23682.
  • Plakantara, S. P., Karakitsiou, A., & Mantzou, T. Managing Risks in Smart Warehouses from the Perspective of Industry 4.0 Check for updates. Disruptive Technologies and Optimization Towards Industry 4.0 Logistics, 1.
  • Podworny, S., Dvir, M., Frischemeier, D., & Ben-Zvi, D. (2024). Reasoning with data models and modeling in the big data era. Universitätsbibliothek Paderborn.
  • Polhill, G., McCormick, B. J., Roxburgh, N., Assefa, S., & Matthews, K. A'Theory of the Middle Range to Support Food Security and Circular Economy Value Chain Scenario Analysis. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 177). Springer Nature.
  • Ponsiglione, A. M., Zaffino, P., Ricciardi, C., Di Laura, D., Spadea, M. F., De Tommasi, G., ... & Amato, F. (2024). Combining simulation models and machine learning in healthcare management: strategies and applications. Progress in Biomedical Engineering, 6(2), 022001.
  • Poostforoush, M. H., Monajemi, A., Daei-Karimzadeh, S., & Samadi, S. (2025). Comparative Comparison of the Efficiency of Hybrid Model of an Agent-based & Recursive Neural Network in Automating Algorithmic Trading Strategies in Global Financial Markets. International Journal of Finance & Managerial Accounting, 10(37), 209-234.
  • Popescu, D. C., & Dumitrache, I. (2024). Software Platform Based on the hLARM Formalism for Modeling Complex Systems. Romanian Journal of Information Science and Technology, 27(1), 65-80.
  • Prajapati, S. P., Yadav, S., & Dhabolkar, S. Designing a constructionist epidemiology curriculum with Agent-based Models: An exploratory study.
  • Prasad, S. (2024). Investigating the Impact of Pandemic Scenarios on the Anesthesiology Department Healthcare Workers Availability: An Agent-Based Modeling Approach (Master's thesis, The University of North Carolina at Charlotte).
  • Prisse, M. M. G. C. Coupling for Multi-Models.
  • Pujiono, E., Sadono, R., Imron, M. A., Sutomo, Januar, H. I., Kuswandi, R., ... & Hani, A. (2024). An agent-based model of agricultural land expansion in the mountain forest of Timor Island, Indonesia. Journal of Mountain Science, 21(7), 2263-2282.
  • Purwani, A., Budijati, S. M., & Asih, H. M. (2024). Management of battery waste recycling by electric motorbike workshops: A literature review. OPSI, 17(1), 216-233.
  • Qolomany, B., Calay, T. J., Hossain, L., Mulahuwaish, A., & Bou Abdo, J. (2024). CCTFv2: Modeling Cyber Competitions. Entropy, 26(5), 384.
  • Qin, H., Liang, Q., Chen, H., & De Silva, V. (2024). A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences. Environmental Modelling & Software, 106166.
  • Qiu, J., Tan, H., Yuan, S., Lv, C., Wang, P., Cao, S., & Zhang, Y. (2024). Selection of Urban Flood Shelter Locations Based on Risk Assessment. Water-Energy Nexus.
  • Qu, A., SHANG, Y., HAO, C., Tan, Q., & Yu, Z. The Effectiveness and Strategy Optimization of Air Pollution Control Policies Based on Abm. Available at SSRN 4974812.
  • Rabb, N. (2024). Modeling Media Influence on the Formation of Misinformed Attitudes (Doctoral dissertation, Tufts University).
  • Ragonis, N., Rosenberg-Kima, R. B., & Hazzan, O. (2024). A computational thinking course for all preservice K-12 teachers: implementing the four pedagogies for developing computational thinking (4P4CT) framework. Educational technology research and development, 1-29.
  • Rahn, S. A. (2024). Modeling and simulation of SARS-CoV-2 transmission in dynamic crowds (Doctoral dissertation, Technische Universität München).
  • Raimbault, J. (2024, July). Spatial sensitivity of the evolutionary swarm chemistry model. In ALIFE 2024: Proceedings of the 2024 Artificial Life Conference. MIT Press.
  • Rajasekaran, U., Malini, A., & Murugan, M. (2024). Artificial Intelligence in Autonomous Vehicles—A Survey of Trends and Challenges. Scrivener Publishing LLC.
  • Rajendran, V., & Ramasamy, R. K. (2024). Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things.
  • Rajkovic, K. (2024). Evaluating the performance of a self-organizing industrial symbiosis compared to a planned one: An agent-based modeling approach (Doctoral dissertation, University of Graz).
  • Raley, J. (2024). The Impact of Mathematical Justifications on Conceptual Understanding (Doctoral dissertation, University of South Carolina).
  • Ramadan, M. (2024). Solid Waste Processing Facility Improvement Using Lean Principles and Simulation Modeling.
  • Razakatiana, M., Kolski, C., Mandiau, R., & Mahatody, T. (2024). Human-Agent Team Based on Decision Matrices: Application to Road Traffic Management in Participatory Simulation. Human-Centric Intelligent Systems, 1-15.
  • Reed, M., Berglund, E., & Montoya, B (2024). An Agent-Based Modeling Perspective of Bio-Mediated Ureolysis. In Geo-Congress 2024 (pp. 446-455).
  • Ren, S. (2024). Optimization of English Classroom Interaction Models Incorporating Machine Learning. Journal of Electrical Systems, 20(6s), 1669-1681.
  • Rensink, J. (2024). What Are the Impacts of Leader Political Skill on Organizational Change? (Doctoral dissertation, Case Western Reserve University).
  • Régis, C., Denis, J. L., Axente, M. L., & Kishimoto, A. (2024). Human-Centered AI: A Multidisciplinary Perspective for Policy-Makers, Auditors, and Users. CRC Press. https://doi.org/10.1201/9781003320791.
  • Reulier, R., Bonnet, G., & L’Homme, F. (2024). Comparison of execution times of three multi-agent platforms (Netlogo/GAMA/Repast simphony) based on the LASCAR model. Journal of Simulation, 1-11.
  • Ribeiro-Rodrigues, E., & Bortoleto, A. P. (2024). A systematic review of agent-based modeling and simulation applications for analyzing pro-environmental behaviors. Sustainable Production and Consumption.
  • Rich, P., & Genot, E. (2024). Mutual Expected Rationality in Online Sharing: An Agent-Based Model Study. Topoi, 1-15.
  • Rieder, A., Chakraborty, S., Goyal, S., & Berndt, D. J. (2024). A critical realist approach to agent-based modeling: Unlocking prediction in non-positivist paradigms. Journal of Information Technology, 02683962241280657.
  • Robayo, C. F. L., Albán, L. M. V., Sánchez, M. M. S., Paredes, J. P. S., Morales, M. A. T., Zavala, E. F. T., ... & Lagla, J. P. S. (2024). Transformación geométrica con Scratch: impacto en formación de profesores de Educación Básica. Polo del Conocimiento, 9(1), 1063-1082.
  • Roderick, S., Damelin, D., & Stephens, A. L. (2024). 7 Students’ systems modeling. Ways of Thinking in STEM-based Problem Solving: Teaching and Learning in a New Era, 87.
  • Rojas-Domínguez, A., Martínez-Vargas, I. U., & Alvarado, M. (2024). Modeling and Simulation of Genotypic TMB and Phenotypic immunogenicity Biomarkers in Cancer Immunoediting with Ising-Hamiltonian Characterization.
  • Rojas Scheffer, V. (2024). Application of hydrogeological models coupled with agent-based models to address sustainable groundwater management in Latin America. Hydrogeology Journal, 1-15.
  • Romano, V., Puga-Gonzalez, I., MacIntosh, A. J., & Sueur, C. (2024). The role of social attraction and social avoidance in shaping modular networks. Royal Society Open Science, 11(2), 231619.
  • Roos, M. (2024). Complexity Economics. In Principles of Complexity Economics: Concepts, Methods and Applications (pp. 115-146). Cham: Springer International Publishing.
  • Roos, M. (2024). Feedback, Circular Causality, and System Dynamics. In Principles of Complexity Economics: Concepts, Methods and Applications (pp. 253-338). Cham: Springer International Publishing.
  • Rusakov, A., & Maksimova, E. Assessment Dynamics Risks Infrastructural. In AISMA-2023: International Workshop on Advanced Information Security Management and Applications (p. 221). Springer Nature.
  • Russo, V., Andrighetto, G., Maretti, M., Polizzi, E., & Cecconi, F. (2024). Homophily and Insularity Dynamics in an Echo Chambers: Computational Models for the Study of the Conspiracy Subculture in Facebook. Social Indicators Research, 1-25.
  • Sagar, S. Towards Resilient Social IoT Sensors and Networks: A Trust Management Approach. Springer Nature.
  • Sagar, S., Mahmood, A., & Sheng, Q. Z. (2024). Understanding the Trustworthiness Management in the SIoT Network. Towards Resilient Social IoT Sensors and Networks: A Trust Management Approach, 11-49.
  • Sager, J., & Schrage, R. (2024). Mango. jl: A Julia-Based Multi-Agent Simulation Framework. Journal of Open Source Software, 9(102), 7098.
  • Saig, R., & Hershkovitz, A. (2024). Expanding Digital Literacies Beyond the Digital: Infusing Computational Thinking into Unplugged Pedagogical Tools-Two Case Studies from Mathematics Education. International Journal of Child-Computer Interaction, 100703.
  • Salas-Peña, A., & García-Palomares, J. C. (2024). Simulation of knowledge transfer in complex networks by coupling ABM and GIS: a local road freight transport system as a case study.
  • Salazar, A. T., Medrano, M., Medina, M. D., Roa, J., & Pesantez, J. E. (2024). Enhancing Evacuation Warning Responsiveness: Exploring the Impact of Social Interactions through an Agent-Based Model Approach.
  • Salazar-Serna, K., Cadavid, L., & Franco, C. (2024). Analyzing Transport Policies in Developing Countries with ABM. arXiv preprint arXiv:2404.19745.
  • Salazar-Serna, K., Cadavid, L., & Franco, C. J. (2024). Modeling Urban Transport Choices: Incorporating Sociocultural Aspects. arXiv preprint arXiv:2407.21307.
  • Saleem, K., Wang, L., Bharany, S., Ouahada, K., Rehman, A. U., & Hamam, H. (2024). Intelligent multi-agent model for energy-efficient communication in wireless sensor networks. EURASIP Journal on Information Security, 2024(1), 9.
  • Saley, A., & Idrissa, M. (2024) Computer Simulation Models of Infectious Diseases: Advantages and Limits. International Journal of Science and Research Methodology, 27(7).
  • Samad, N. A., Osman, K., & Nayan, N. A. (2024). Computational Thinking Through the Engineering Design Process in Chemistry Education. International Journal of Educational Methodology, 9(issue-4-november-2023), 771-785.
  • Samhitha, J. S. S., Sagar, K. A., Yaswanth, J. S., & Haritha, K. (2024, March). Early Forest Fire Prediction System Using Wireless Sensor Network. In 2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) (pp. 232-237). IEEE.
  • Samimi, K., Zarei, E., & Pouyakian, M. (2024). Agent-Based Modeling and Simulation in System Safety and Risk Management. In Safety Causation Analysis in Sociotechnical Systems: Advanced Models and Techniques (pp. 405-432). Cham: Springer Nature Switzerland.
  • Samoylova, K., & Zamyatina, E. Design of Robust Business Processes Using Multimodel Approach. Available at SSRN 4801736.
  • Sánchez-Maroño, I. N. EGAR: Environment Generator for Agent-Based Research. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection: 22nd International Conference, PAAMS 2024, Salamanca, Spain, June 26–28, 2024, Proceedings (p. 217). Springer Nature.
  • Sano, H. (2024). Impacts of public disclosure on tax compliance using agent-based modeling. Journal of Economic Interaction and Coordination, 1-33.
  • Santucci, J. F., Capocchi, L., Ören, T., Szabo, C., & Neto, V. V. G. Synergies of Soft Computing and M&S 12. Body of Knowledge for Modeling and Simulation, 287.
  • Sanyal, M., Sengupta, P., Banerjee, P., Dutta, S., & Ozacar, B. H. (2024). River crossings of our childhood: Nezuko and Yasmine dis/orient computing education.
  • Sapienza, A., & Falcone, R. (2024). Flood Risk and Preventive Choices: A Framework for Studying Human Behaviors. Behavioral Sciences, 14(1), 74.
  • Sapienza, A., & Falcone, R. (2024). Exploring the Dynamics of Learned, Pre-existing, and Partial Knowledge in Dependence Networks within Multi-Agent Systems.
  • Saranya, A., Naresh, R., Karuppiah, S., & Jenifer, M. (2024). Development of trust-based authorization and authentication framework for secure electronic health payment in cloud environment. Soft Computing, 1-16.
  • Saravia, L. A., Balza, U., & Momo, F. (2024). Why there are more species in several small patches versus few large patches: A multispecies modelling approach. Functional Ecology.
  • Sarkar, M., Kweon, O., Kim, B. I., Choi, D. G., & Kim, D. Y. (2024). Synergizing Autonomous and Traditional Vehicles: A Systematic Review of Advances and Challenges in Traffic Flow Management With Signalized Intersections. IEEE Transactions on Intelligent Transportation Systems.
  • Sasaki, M., Monaco, C. J., Booth, D. J., & Nagelkerken, I. (2024). Ocean warming and novel species interactions boost growth and persistence of range‐extending tropical fishes but challenge that of sympatric temperate species in temperate waters. Journal of Biogeography.
  • Saula, A. Y., Rowlatt, C., & Bowness, R. (2024). Use of Individual-Based Mathematical Modelling to Understand More About Antibiotic Resistance Within-Host. In Antibiotic Resistance Protocols (pp. 93-108). New York, NY: Springer US.
  • Scharf, I. (2024). Search patterns, resource regeneration, and ambush locations impact the competition between active and ambush predators. Annals of the New York Academy of Sciences.
  • Schnieder, M., Hinde, C., & West, A. (2024). Digital Twin Concept in Last Mile Delivery and Passenger Transport (A Systematic Literature Review). Enterprise Interoperability X: Enterprise Interoperability Through Connected Digital Twins, 135-145.
  • Schöppl, K., & Hahn, U. (2024). Exploring Effects of Self-Censoring through Agent-Based Simulation. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).
  • Schlüter, M., Hertz, T., Klein, A., & Wijermans, N. Disentangling the entangled in productive ways: modelling SES from a process-relational perspective.
  • Secchi, D., Gahrn-Andersen, R., & Neumann, M. (2024). Complexity in Systemic Cognition: Theoretical Explorations with Agent-Based Modeling. Systems, 12(8), 287.
  • Seex, L. (2024). The Self-Organisation of Lemur Social Systems. (Doctoral dissertation, University of Groningen).
  • Sego, T. J., König, M., Fonseca, L. L., Fain, B., Knapp, A. C., Tiwari, K., ... & Malik-Sheriff, R. S. (2024). EFECT--A Method and Metric to Assess the Reproducibility of Stochastic Simulation Studies.
  • Selje, T., Islam, R., & Heinz, B. (2024). An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change.
  • Sen, S. (2024). Polarisation and Homophily: Experiments with a Contagion-Repulsion Combination Opinion Dynamics Model.
  • Senanayake, G. P., Kieu, M., Zou, Y., & Dirks, K. (2024). Agent-based simulation for pedestrian evacuation: A systematic literature review. International Journal of Disaster Risk Reduction, 104705.
  • Sergeyuk, A., Golubev, Y., Bryksin, T., & Ahmed, I. (2024). Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward. arXiv preprint arXiv:2406.07765.
  • Seuru, S., Burke, A., & Perez, L. (2024). Evidence of an age and/or gender-based division of labor during the Last Glacial Maximum in Iberia through rabbit hunting. Journal of Archaeological Science: Reports, 56, 104560.
  • Shadkam, E., & Irannezhad, E. A Comprehensive Review of Simulation Optimization Methods in Agricultural Supply Chains and Transition Towards an Agent-Based Intelligent Digital Framework for Agriculture 4.0. Available at SSRN 4791793.
  • Shah, D., Belshe, R., Speyer, G., & Yalim, J. (2024). Measuring the Impact of Centralized High-Performance Computing with Research Collaboration Networks. SN Computer Science, 5(5), 600.
  • Shahouni, R., Abbasi, M., Dibaj, M., & Akrami, M. (2024). Utilising Artificial Intelligence to Predict Membrane Behaviour in Water Purif
  • Sharmila, L., Shelke, M. P., Venkatesan, R., Ramya, R., Umadevi, G., & Ishwarya, M. V. (2024). Computer Modeling Using Visualization of Measuring Method. Wireless Personal Communications, 1-14.
  • Sharypin, A., Galushko, O., Stavroiani, S., Stavroyany, S., & Kravchenko, I. (2024). The prospective evolution of social networks from the perspective of neurophenomenology. Telos: Revista de Estudios Interdisciplinarios en Ciencias Sociales, 26(2), 595-613.
  • Shen, Y., Hu, X., Wang, X., Zhang, M., Deng, L., & Wang, W. (2024, July). Integrated framework for space-and energy-efficient retrofitting in multifunctional buildings: A synergy of agent-based modeling and performance-based modeling. In Building Simulation (pp. 1-22). Tsinghua University Press.
  • Shili, M., & Anwar, S. (2024). Leveraging Agent-Based Modeling and IoT for Enhanced E-Commerce Strategies. Information, 15(11), 680.
  • Shin, H. (2024). Quantifying Population Exposure to Long-term PM10: A City-wide Agent-based Assessment. arXiv preprint arXiv:2402.05029.
  • Shinde, S., P Kurhekar, M., Gulhane, M., & K Pikle, N. (2024). Design of a Novel Enhanced Machine Learning Model for Early Prediction of Cerebral Stroke. International Journal of Computing and Digital Systems, 16(1), 1-22.
  • Siebe, H. (2024). Modelling the prevalence of hidden profiles with complex argument structures. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46).
  • Sihombing, D. J. C. (2024). Development of Membership Management Application for Fitness Center using Extreme Programming Methodology. Jurnal Ekonomi, 13(02), 736-744.
  • Silva, C., & Xue, S. (2024). Situating spatial determinism in urban design and planning for sustainable walkability: a simulation of street morphology and pedestrian behaviour. Discover Sustainability, 5(1), 1-20.
  • Simmonds, J., Gómez H, J. A., & Ledezma, A. (2024). Testing the Feasibility of an Agent-Based Model for Hydrologic Flow Simulation. Information, 15(8), 448.
  • Singh, S. (2024). A Qualitative Approach for Learning and Detection of Emergent Behaviors (Doctoral dissertation, Northeastern University).
  • Singh, S. K., Sharma, C., Mahadeva, R., Patole, S. P., & Maiti, A. (2024). Predicting forward osmosis performance with synthesized polyamide-based membrane: An integrated machine learning (MATLAB and ANN) and economic analysis framework. Journal of Cleaner Production, 141285.
  • Sinha, G. K., & Purwar, A. K. (2024). Optimization Models in Water Resources Management and Security: A Critical Review. International Journal of Mathematical, Engineering & Management Sciences, 9(1), 129-146.
  • Sinha, R., Swanson, H., & Chandrasekharan, S. (2024). Epistemic Games at the Frontier: A Characterization of Emerging Science and Engineering Practices. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 1822-1825. International Society of the Learning Sciences.
  • Smith, M. M., & Pauli, J. N. (2024). Small but connected islands can maintain populations and genetic diversity under climate change. Ecography, e07119.
  • Soler-Navarro, D. J., Giménez, A., Pérez-Ibarra, I., Janssen, M. A., & Tenza-Peral, A. Environmental Strategies Increase the Resilience of Extensive Livestock Systems to Adverse Climate Conditions. Available at SSRN 5002017.
  • Soliman, K. S. (Ed.). (2024). Artificial Intelligence and Machine Learning: 41st IBIMA International Conference, IBIMA-AI 2023, Granada, Spain, June 26-27, 2023, Revised Selected Papers (Vol. 2101). Springer Nature.
  • Song, S., Wang, S., Jiao, C., & Ibarra, E. J. M. (2024). ABSESpy: An agent-based modeling framework for social-ecological systems. Journal of Open Source Software, 9(96), 6298.
  • Sorel, M., Gay, P. E., Vernier, C., Cissé, S., & Piou, C. (2024). Upwind flight partially explains the migratory routes of locust swarms. Ecological Modelling, 489, 110622.
  • Sosa, A. Z., & Rodríguez, L. G. R. (2024). Las Redes Sociales No Digitales en la Acción Colectiva: Modelo Basado en Agentes. Psicumex, 14, 1-32.
  • Souidi, M. E. H., Ledmi, M., Maarouk, T. M., Ledmi, A., & Laassami, F. (2024). IMAP-QL: an improved multi-agent pursuit path-planning based on Q-learning. International Journal of Systems, Control and Communications, 15(2), 159-178.
  • Spring, N. (2024). Multi-Agent Systems in Vehicular Edge Computing: A Communication-Centric Approach to Task Handovers (Doctoral dissertation, Technische Universität Wien).
  • Squadrani, L., Wert-Carvajal, C., Müller-Komorowska, D., Bohmbach, K., Henneberger, C., Verzelli, P., & Tchumatchenko, T. (2024). Astrocytes enhance plasticity response during reversal learning. Communications Biology, 7(1), 1-10.
  • Stacy, L. (2024). Agent-Based Modeling and Air Sampling Methods for Studying Aerosol Transmission.
  • Steinberg, S., Gresalfi, M., Vogelstein, L., & Brady, C. (2024). Coding choreography: Understanding student responses to representational incompatibilities between dance and programming. Journal of Research on Technology in Education, 56(3), 314-331.
  • Steinmann, P. (2024). Quantifying resilience under deep uncertainty (Doctoral dissertation, Wageningen University).
  • Stephan, S., Galland, S., Narsis, O. L., Shoji, K., Vachenc, S., Gerart, S., & Nicolle, C. (2024). Agent-based approaches for biological modeling in oncology: A literature review. Artificial Intelligence in Medicine, 102884.
  • Stolk, M. GroupRugs: Visualizing Group Motion.
  • Stöppler, F. How Abstract Mechanisms Come Alive: Modelling Network Path Dependence Using Qualitative Data. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 435). Springer Nature.
  • Strasburg, M., & Christensen, S. (2024). Evaluating the Interaction of Emerging Diseases on White-Tailed Deer Populations Using an Agent-Based Modeling Approach. Pathogens, 13(7), 545.
  • Suchak, K., Kieu, M., Oswald, Y., Ward, J. A., & Malleson, N. (2024). Coupling an Agent-Based Model and Ensemble Kalman Filter for Real-Time Crowd Modelling. Royal Society Open Science.
  • Sueur, C., Lombard, J., Capra, O., Beltzung, B., & Pelé, M. (2024). Exploration of the creative processes in animals, robots, and AI: who holds the authorship?. Humanities and Social Sciences Communications, 11(1), 1-12.
  • Sulaimany, S., Bidkhori, G., & Khoshnaw, S. H. (2024). Causal loop and Stock-Flow Modeling of Signal Transduction Pathways. arXiv preprint arXiv:2409.01267.
  • Sultan, H. M. K. M. M., & Abbas, A. F. Enhance Penetration Testing Techniques to Improve Cybersecurity with NetLogo, Nmap, and Wireshark. Journal of Natural and Applied Sciences URAL, 100.
  • Sun, C., Li, G., Zhou, K., Huang, D., & Luo, Q. (2024). Towards a Sustainable Structure of an Urban Water–Energy–Food Nexus: Based on Network and Hierarchy Analysis. Water, 16(15), 2074.
  • Sun, H., Han, G., Zhang, X., & Ruan, X. (2024). Grasping Emergency Dynamics: A Review of Group Evacuation Techniques and Strategies in Major Emergencies. Journal of Safety Science and Resilience.
  • Sun, H., Yan, Y., & Han, Y. (2024). The influencing factors of green technology innovation in renewable energy companies based on hyper-network. Energy Informatics, 7(1), 1-33.
  • Sun, Y. T., Lee, H. C., Yu, Y. C., Wu, T. F., Althamary, I., & Huang, C. W. (2024). A Multi-Layer Blockchain Simulator and Performance Evaluation of Social Internet of Vehicles with Multi-Connectivity Management. arXiv preprint arXiv:2411.14000.
  • Sunday, A. O., Agbo, F. J., & Suhonen, J. (2024). Co-design Pedagogy for Computational Thinking Education in K-12: A Systematic Literature Review. Technology, Knowledge and Learning, 1-56.
  • Suro, F., Michel, F., & Stratulat, T. (2024). Integration of memory systems supporting non-symbolic representations in an architecture for lifelong development of artificial agents. Artificial Intelligence, 104228.
  • Swanson, H. & Wilensky, U. (2024). Meta-theoretic competence for computational agent-based modeling. In Proceedings of the 2nd Annual ACM Symposium on Learning, Design and Technology, (pp. 38-46) New York: ACM. https://doi.org/10.1145/3663433.3663458
  • Sznajd-Weron, K., & Kamińska, B. (2024). Impact of Cognitive Dissonance on Social Hysteresis: Insights from the Expressed and Private Opinions Model. Available at SSRN 4814235.
  • Talebi, A. (2024). Simulation in discrete choice models evaluation: SDCM, a simulation tool for performance evaluation of DCMs. arXiv preprint arXiv:2407.17014.
  • Tan, F., Wang, J., De Steur, H., & Fan, S. (2024). Unveiling the predictive factors influencing consumers purchase intention towards biofortified products: A PLS-SEM model with agent-based simulation.
  • Tang, X., Lindgren, R., & Lira, M. (2024). Haptic Experiences Shape Student-Generated Gestures While Learning with Computational Environments. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 218-225. International Society of the Learning Sciences.
  • Tang, X., & Lira, M. (2024). Drawing and Talk Reveal Dynamic Shifts in Students' Developing Knowledge of Complex Systems. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 2123-2124. International Society of the Learning Sciences.
  • Tang, W., Yang, H., & Pi, J. (2024). Dynamics and Control Strategies for SLBRS Model of Computer Viruses Based on Complex Networks. International Journal of Intelligent Systems, 2024.
  • Tian, F., Zhang, L., Zhu, L., Zhao, M., Liu, J., Dong, Q., & Zhao, Q. (2024). Advancements in Affective Disorder Detection: Using Multimodal Physiological Signals and Neuromorphic Computing Based on SNNs. IEEE Transactions on Computational Social Systems.
  • Timberlake, T. P., Tew, N. E., & Memmott, J. (2024). Gardens reduce seasonal hunger gaps for farmland pollinators. Proceedings B, 291(2033), 20241523.
  • Tiwary, A., & Mukhopadhyay, S. Research Supervisors: Mr. Robert Gotwals3 and Dr. Sarah Shoemaker3 Raleigh Charter High School, Cary, North Carolina. Email: atiwary@ raleighcharterhs. org 2 North Carolina Central University, Biomedical and Biotechnology Research Institute. Email: smukhopadhyay@ nccu. edu.
  • Thomas, A. (2024). Grasp planning techniques for harvesting in a novel greenhouse simulation environment (Doctoral dissertation, University of Guelph).
  • Thomas, S. R., & Aurora, S. R. (2024). The dynamic effects of transformational leadership on employee retention and employability over time: an agent-based model. Computational and Mathematical Organization Theory, 1-31.
  • Thompson, B. K. (2024). Quantitative Modeling Tools for Invasive Species Management Decisions (Doctoral dissertation, University of Washington).
  • Topraklı, A. Y. (2024). A Review and Implementation Guide for Basic Cellular Automata Models in Pedestrian Evacuation Simulation. Gazi University Journal of Science Part B: Art Humanities Design and Planning, 12(3), 517-530.
  • Topuz, E., & Yücel, G. Analyzing the Emergence and Dynamics of Pluralistic Ignorance with Agent-Based Models. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 423). Springer Nature.
  • Torres, L. M. L., Meythaler, F. S. V., Muñoz, J. L. C., & Soria, S. L. G. (2024). Análisis de las intersecciones aledañas al terminal terrestre de la ciudad de Tena-Ecuador. HOLOPRAXIS. Revista de Ciencia, Tecnología e Innovación, 8(1), 117-147.
  • Tran¹, L., Son¹, T. C., Flynn, D., & Balduccini, M. A Simulation for Supply Chains Contract. In Logic Programming and Nonmonotonic Reasoning: 17th International Conference, LPNMR 2024, Dallas, TX, USA, October 11–14, 2024, Proceedings (p. 331). Springer Nature.
  • Tran, N. B., Nguyen, T. L., Nguyen, Q. V., & Nguyen, D. T. (2024). Optimizing Municipal Solid Waste (MSW) Collection and Transportation Network Using GIS and Agent-based Modeling Approach: A Case Study in Hung Yen City, Vietnam. VNU Journal of Science: Earth and Environmental Sciences.
  • Traore, C. A. D. G., Delay, E., Diop, D., & Bah, A. (2024). Agent-based model for analyzing the impact of movement factors of sahelian transhumant herds. Human-Centric Intelligent Systems, 1-19.
  • Tsintsaris, D., Tsompanoglou, M., & Ioannidis, E. (2024). Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business. Mathematics, 12(8), 1141.
  • Tuekam Kakeu, S. V., Fotsing, E., Kameni, E. D., & Antonio Nkenlifack, M. J. (2024). An agent architecture for expressive spatial knowledge and reasoning in land use modeling and simulations. SIMULATION, 00375497241247040.
  • Tung, E., Mostafavi, A., Li, M., Li, S., Rasheed, Z., & Shafique, K. (2024). A System for Critical Facility and Resource Optimization in Disaster Management and Planning. arXiv preprint arXiv:2410.02956.
  • Tzouras, P. G., Mitropoulos, L., Karolemeas, C., Stravropoulou, E., Vlahogianni, E. I., & Kepaptsoglou, K. (2024). Agent-based simulation model of micro-mobility trips in heterogeneous and perceived unsafe road environments. Journal of Cycling and Micromobility Research, 100042.
  • Umlauft, M., Gojkovic, M., Harshina, K., Majbour, K., & Schranz, M. (2024). Bees, Bats and Glowworms: Swarm Algorithms for Optimizing Industrial Plants from the Bottom-Up. In Agents and Artificial Intelligence: 15th International Conference, ICAART 2023, Lisbon, Portugal, February 22–24, 2023, Revised Selected Papers (p. 3). Springer Nature.
  • Umlauft, M., & Schranz, M. Using NetLogo to Simulate Large Production Plants: Simulation Performance: A Case Study.
  • van Bruggen, A. R., Hoekstra, J., Claassen, L., Princen, M., ter Hoeven, E., van Burgsteden, M., & te Brömmelstroet, M. (2024). Transformative Modelling for Sustainable Mobility and Healthy Neighborhood Spatial Design: The Case of Enka. Available at SSRN 4744940.
  • van den Hurk, M., Dechesne, M., & Dignum, F. Exploring the Stepwise Process and Consequences of Identity Fusion in Different Groups: An ABM. In Advances in Social Simulation: Proceedings of the 18th Social Simulation Conference, Glasgow, UK, 4–8 September 2023 (p. 397). Springer Nature.
  • van Dongen, M. (2024). The Teaching Methods and Strategies of Programming Education within High Schools in The Netherlands.
  • van Nes, E. H., Pujoni, D. G., Shetty, S. A., Straatsma, G., de Vos, W. M., & Scheffer, M. (2024). A tiny fraction of all species forms most of nature: Rarity as a sticky state. Proceedings of the National Academy of Sciences, 121(2), e2221791120.
  • van Oort, P. A. J., Fonteijn, H. M. J., & Hengeveld, G. M. (2024). DARTS: Modelling effects of shocks on global, regional, urban and rural food security. PLOS Complex Systems, 1(2), e0000006.
  • van Zinnicq Bergmann, M. P., Griffin, L. P., Bodey, T. W., Guttridge, T. L., Aarts, G., Heithaus, M. R., ... & Papastamatiou, Y. P. (2024). Intraguild processes drive space‐use patterns in a large‐bodied marine predator community. Journal of Animal Ecology.
  • Varela, L., Batallés, M., Tambusso, P. S., Costoya, G., & Fariña, R. A. (2024). The Megafauna3D Educational Environment: Harnessing the Combination of New and Traditional Technologies to Improve Geoscience Education and Outreach. Geosciences, 14(12), 321.
  • Velandia Lara, V. M. Propuesta de lineamientos de usos de la malla vial basado en el análisis espacial y simulación basada en agentes para la localidad de Chapinero (upz el Refugio y Chicó Lago) en la ciudad de Bogotá (Master's thesis, Universidad de La Sabana).
  • Veldhuis, A., Johry, A., Bekker, T., Jetten, M., Cronin, E. B., & Works, D. Design Futures. PROCEEDINGS OF CONSTRUCTIONISM/FABLEARN 2023, 30.
  • Verhoog, M. (2024) How Marketing and Sales can Push and Accelerate Residential Refurbishment.
  • Verma, P., Sood, S. K., Kaur, H., Kumar, M., Wu, H., & Gill, S. S. (2024). Data Driven Stochastic Game Network-Based Smart Home Monitoring System Using IoT-Enabled Edge Computing Environments. IEEE Transactions on Consumer Electronics.
  • Vestad, A., & Yang, B. (2024). Human Factors in Cybersecurity, Vol. 127, 2024, 83-93 AHFE. Human Factors in Cybersecurity, 83.
  • Vidal-Lamolla, P., Molinos-Senante, M., Oliva-Felipe, L., Alvarez-Napagao, S., Cortés, U., Martínez-Gomariz, E., ... & Poch, M. (2024). Assessing urban water demand-side management policies before their implementation: An agent-based model approach. Sustainable Cities and Society, 105435.
  • Vidler, A., & Walsh, T. (2024). Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation Study. arXiv preprint arXiv:2405.02849.
  • Vigna, I., Millington, J., Ascoli, D., Comino, E., Pezzoli, A., & Besana, A. (2024). A picit jeu: Agent-based modelling with serious gaming for a fire-resilient landscape. Journal of Environmental Management, 370, 122529.
  • Vijayan, M., Patil, A., & Kapse, V. (2024). An agent-based computational model on household electricity consumption in Indian cities. Journal of Green Building, 19(1), 235-260.
  • Voß, J. N. (2024). Konzeption und Entwicklung einer didaktischen Modellierungs-und Simulationsplattform für MARS (Doctoral dissertation, Hochschule für Angewandte Wissenschaften Hamburg).
  • Vulic, J., Jacobson, M. J., & Levin, J. A. (2024). Exploring Education as a Complex System: Computational Educational Research with Multi-Level Agent-Based Modeling. Education Sciences, 14(5), 551.
  • Wagh, A., Rosenbaum, L. F., Fuhrmann, T., Eloy, A., Blikstein, P., & Wilkerson, M. (2024). Toward Ontological Alignment: Coordinating Student Ideas with the Representational System of a Computational Modeling Unit for Science Learning. Cognition and Instruction, 1-32.
  • Wagh, A., Vogelstein, L., & Champion, D. (2024). Fused Representations: Linking Choreographic and Digital NetLogo Modeling through Intermodal Inquiry. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 714-721. International Society of the Learning Sciences.
  • Wan, L., Lv, Y., Wang, Z., & Tian, Y. (2024). The synergistic evolution of supply-demand composite system for airport green development: A case study in Guangzhou Baiyun International Airport, China. PloS one, 19(4), e0302303.
  • Wang, C., Zhou, Z., & Zheng, G. (2024). Efficient weighted multi-source trust aggregation scheme for edge computing offloading. Social Network Analysis and Mining, 14(1), 33.
  • Wang, H. (2024). Impacts of Altruism and Uncertainty on Consumption: Three Essays (Doctoral dissertation, University of Guelph).
  • Wang, H., Li, F., Zou, Y., Yang, M., Chen, Z., & Nie, X. (2024). How do smallholder farmers run towards agricultural modernization under multiple stressors in China's coastal zones: an agent-based modeling approach. Environment, Development and Sustainability, 1-27.
  • Wang, H., Lu, Q., Zhang, Z., Lin, J., & Wu, Q. (2024). Study on separation of CO2 condensation from natural gas based on cellular automaton method. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 46(1), 3663-3683.
  • Wang, M., Wang, H. H., Koralewski, T. E., Grant, W. E., White, N., Hanan, J., & Grimm, V. (2024). From known to unknown unknowns through pattern-oriented modelling: Driving research towards the Medawar zone. Ecological Modelling, 497, 110853.
  • Wang, S., Chang, J., Xue, J., Sun, H., Zeng, F., Liu, L., ... & Li, X. (2024). Coupling behavioral economics and water management policies for agricultural land-use planning in basin irrigation districts: Agent-based socio-hydrological modeling and application. Agricultural Water Management, 298, 108845.
  • Wang, W., Sawleshwarkar, S., & Piraveenan, M. (2024). Computational Approaches of Modelling Human Papillomavirus Transmission and Prevention Strategies: A Systematic Review. arXiv preprint arXiv:2404.19235.
  • Wang, W., & Wu, F. (2024). Dynamic simulation for reclaimed water reuse under multi-intervention policies in China. Heliyon, 10(3).
  • Wang, X., Jin, L., & Wang, S. (2024). Research on Network Public Opinion in War Damage Incident of Major Water Conservancy Projects. IEEE Access.
  • Wang, X., He, X., Sun, X., Qin, M., Pan, R., & Yang, Y. (2024, January). The Diffusion Path of Distributed Photovoltaic Power Generation Technology driven by Individual Behavior. In Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29, 2023, Tianjin, China.
  • Wang, Z., Yue, C., & Wang, J. (2024). Evaluating parameter inversion efficiency in Heterogeneous Groundwater models using Karhunen-Loève expansion: a comparative study of genetic algorithm, ensemble smoother, and MCMC. Earth Science Informatics, 1-17.
  • Wang, Z. Z., Xu, S. Q., & Guan, Y. J. (2024). Impact of the innovation promotion strategy on digital technology diffusion in regional innovation ecosystems. Technology Analysis & Strategic Management, 1-17.
  • Wanniarachchi, C., Wimalaratne, P., & Karunanayaka, K. (2024). Formation Control Algorithms for Drone Swarms and The Single Point of Failure Crisis: A Review. In 2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE) (pp. 1-6). IEEE.
  • Wasesa, H. A., & Prasetio, E. A. Prediction of Battery Electric Vehicle (BEV) Adoption in Indonesia Using Agent-Based Modelling and Simulation: An Integrated Model Implementation.
  • Wasesa, M., Rizaldi, A., Stam, A., Zuidwijk, R., & Van Heck, E. (2024). Advancing Smart Sustainable Seaports: Auction-based Truck Appointment System for Automated Container Terminal. IEEE Access.
  • Weitzel, E. M., Wilson, K. M., Spake, L., Schaffnit, S. B., Lynch, R., Sear, R., ... & Sosis, R. (2024). Cost structures and socioecological conditions impact the fitness outcomes of human alloparental care in agent-based model simulations. Evolution and Human Behavior, 45(5), 106613.
  • Wen, C. T., Liu, C. C., Li, C. Y., Chang, M. H., Chiang, S. H. F., Lin, H. M., ... & Biswas, G. (2024). The learning analytics of computational scientific modeling with self-explanation for subgoals and demonstration scaffolding. Computers & Education, 105043.
  • Wen, F., Chen, Y., & Pan, C. (2024, May). Cooperative tracking control of cross-domain heterogeneous unmanned systems via adaptive neural backstepping. In 2024 7th International Symposium on Autonomous Systems (ISAS) (pp. 1-6). IEEE.
  • Weng, X., Pan, D., & Jin, Y. (2024). An Adaptive Distributed Consumer Trust Model for Social Commerce. Applied Artificial Intelligence, 38(1), 2385857.
  • Westbury, S. O. (2024). RULER: Prebugging with Proxy-Based Programming.
  • Westerveld, J., & Verbraeck, A. A. Recurring COVID-19 Vaccinations.
  • Wiegel, E., & Yorke-Smith, N. (2024). An Agent-Based Market Analysis of Urban Housing Balance in The Netherlands. Real Estate, 1(1), 80-135.
  • Wiggins, D. E., & Solomonides, A. E. 2024. Modeling a Viral Epidemic With a Concurrent “Misinfodemic”.
  • Williams, A., Mesoudi, A., Williams, A. J., & Mesoudi, A. Testing the effect of circumscription on the evolution of social complexity in the Valley of Oaxaca, Mexico, using 2 agent-based models 3.
  • Wong, J., Nack, E., Steelman, Z., Erway, S., & Bastian, N. D. (2024, June). A methodology for representing and assessing artificial intelligence decision aids within modeling and simulation. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI (Vol. 13051, pp. 143-167). SPIE.
  • Woo, K., & Falloon, G. (2024). Integrating coding across the curriculum: a scoping review. Computer Science Education, 1-22.
  • Wood, T., An, G., Bryant, C. E., & Ferguson, B. J. (2024). Modelling molecular differences in the innate immune system responses of chickens and ducks to highly pathogenic avian influenza virus. bioRxiv, 2024-07.
  • Wu, J. (2024). Evaluation of Social Network Influence. In Social Network Computing (pp. 453-490). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Homogeneity in Social Networks. In Social Network Computing (pp. 159-188). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Introduction to Social Network Computing. In Social Network Computing (pp. 3-43). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Modeling and Simulation of Social Networks. In Social Network Computing (pp. 551-573). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Power Laws in Social Networks. In Social Network Computing (pp. 257-284). Singapore: Springer Nature Singapore.
  • Wu, J. (2024). Small World in Social Networks. In Social Network Computing (pp. 219-256). Singapore: Springer Nature Singapore.
  • Wu, L., Sun, Z., Liu, J., Shan, D., Ma, X., & Zhu, T. (2024). Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach. Computers and Electrical Engineering, 119, 109500.
  • Wu, Y. (2024, November). Research on product quality control based on multiagent modeling. In International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) (Vol. 13395, pp. 340-344). SPIE.
  • Wurth, B., & Kazakov, R. Combining SD and ABM: Fram
  • Xiang, L., Mirakhur, Z., Pilny, A., & Krall, R. (2024). Measuring and Comparing High School Teachers’ and Undergraduate Students’ Knowledge of Complex Systems. Education Sciences, 14(8), 837.
  • Xie, S., Gan, C., & Lawniczak, A. T. (2024). Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models. Mathematics, 12(23), 3768.
  • Xu, J., & Bi, Y. (2024). An Agent-Based Modeling Approach for the Diffusion Analysis of Electric Vehicles With Two-Stage Purchase Choice Modeling. Journal of Computing and Information Science in Engineering, 24(6), 064502.
  • Xu, S., Hsu, S. C., Du, E., Song, L., Lam, C. M., Liu, X., & Zheng, C. (2024). Agent-Based Modeling in Water Science: From Macroscale to Microscale. ACS ES&T Water.
  • Xu, W., & Zhang, K. (2024). Ant Colony Optimization for Urban Flood Rescue Routing: Integrating Agent-Based Simulation and Multi-Center Vehicle Routing. Available at SSRN 4998292.
  • Xu, Z., Ban, F., & Fotia, P. (2024). Efficient QoS processing for internet of medical things using non-cooperative game theory: resource allocation in cloud framework. Annals of Operations Research, 1-16.
  • Yalabadi, A. K., Yazdani-Jahromi, M., Abdidizaji, S., Garibay, I., & Garibay, O. O. (2024). Controlling the Misinformation Diffusion in Social Media by the Effect of Different Classes of Agents. arXiv preprint arXiv:2401.11524.
  • Yan, Z., & Yeo, J. (2024). Competing Mechanisms in Bacterial Invasion of Human Colon Mucus Probed with Agent-based Modeling. Biophysical Journal.
  • Yang, Q., Tang, Y., & Gao, D. (2024). Agent-based evolutionary game dynamics uncover the dual role of resource heterogeneity in the evolution of cooperation. Journal of Theoretical Biology, 111952.
  • Yang, X., & Zeng, X (2024). Combined with the Internet technology of college student training mode innovation and student management mechanism optimization. Applied Mathematics and Nonlinear Sciences, 9(1).
  • Yates, J. W. (2024). Integrated pharmacokinetic-pharmacodynamic and agent-based modelling in drug development: Current status and future perspectives. Advanced Drug Delivery Reviews, 115474.
  • Yin, L., Yin, F., & Silverman, R. M. (2024). Rethinking demolition plans to fight neighborhood blight in shrinking cities: Applying agent-based policy simulations. Cities, 150, 105035.
  • Yin, X., Xiao, S., Zhang, X., & Ruan, F. (2024). Social Network Analysis of a Norovirus Outbreak at a Primary School—Zhuhai City, Guangdong Province, China, 2023. China CDC Weekly, 6(44), 1142-1146.
  • Yirenkyi, E. (2024). An agent-based modeling approach to household adaptation for flooding and coastal erosion at Channel-Port aux Basque. Earth ArXiv.
  • You, X., & Zhou, Z. (2024). A Multi-attribute Large Group Decision-making Method Based on Interval-valued Pythagorean Fuzzy Number.
  • Young, E. (2024). Where the Smoke Was Coming From: Risk Assessment, Social Ties, and Expanding Roles in the Evacuation of the Beverly Hills Supper Club Fire (Doctoral dissertation, University of Delaware).
  • Youssefi, K. A., Gojkovic, M., & Schranz, M. (2024). Artificial Bee Colony Algorithm: Bottom-Up Variants for the Job-Shop Scheduling Problem.
  • Yu, C. L. (2024). Developing a Theory of Mind: A Multi-method Perspective (Doctoral dissertation).
  • Yu, J. H., & Chie, B. T. (2024). Interplay and Implications of Urban Renewal under Dual Laws-An Agent-Based Simulation. Advances in Management and Applied Economics, 14(5).
  • Yu, Y., Lu, Q., & Fu, Y. (2024). Dynamic Trust Management for the Edge Devices in Industrial Internet. IEEE Internet of Things Journal.
  • Yuan, X., Wang, J., Hu, S., Cheung, A., & Lu, Z. (2024). KoroT-3E: A Personalized Musical Mnemonics Tool for Enhancing Memory Retention of Complex Computer Science Concepts. arXiv preprint arXiv:2409.10446.
  • Yust, A. E. (2024). BODINE: Exploring the Versatility of Agent-Based Modeling in NetLogo: Lessons from Education and Research.
  • Yunping, Z. H. A. N. G., Jianping, L. I. N., Yimin, H. U. A. N. G., Zebin, C. H. E. N., Chenhui, Z. H. U., & Hao, Y. U. A. N. (2024). Delineation of urban growth boundary based on FLUS model under the perspective of land use evaluation in hilly mountainous areas. Journal of Mountain Science, 21(5), 1647-1662.
  • Zaffar, M. A., Kumar, R., & Zhao, K. (2024). Competitive dynamics in a multi-sided mobile payment platform market: an agent-based modeling perspective. International Journal of Bank Marketing, (ahead-of-print).
  • Zagorskas, J., & Turskis, Z. (2024). Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach. Sustainability, 16(7), 2994.
  • Zambonelli, F., Bergenti, F., Mariani, S., & Monica, S. (2024). Islands of cooperation emerge by stigmergic interactions in iterated spatial games. PLOS ONE, 19(7), e0306915.
  • Zambrano García, P. A. (2024). Sostenibilidad ambiental de la cadena de suministro de la franquicia: un modelo basado en agentes para el análisis de la influencia de los mecanismos de gobernanza en la adopción de prácticas verdes (Doctoral dissertation, Universidad Nacional de Colombia).
  • Zedadra, O., Guerrieri, A., Seridi, H., Benzaid, A., & Fortino, G. (2024). Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem. Big Data and Cognitive Computing, 8(2), 18.
  • Zedadra, O., Zedadra, A., Guerrieri, A., Seridi, H., & Ghelis, D. (2024). A New Multi-Robots Search and Rescue Strategy based on Penguin Optimization Algorithm. Scalable Computing: Practice and Experience, 25(5), 4428-4441.
  • Zeinali Kalkhoran, A., Platt, L. S., Iovine, N., Brown, S., & Pollitt, B. (2024). An agent-based modeling approach for infection prevention predesign: simulating the spread of pathogens between humans and the environment in an Intensive Care Unit. Architectural Engineering and Design Management, 1-22.
  • Zellner, M. L., & Massey, D. (2024). Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning. Heliyon.
  • Zhang, C., Wu, X., Zhao, S., Madani, H., Chen, J., & Chen, Y. (2024). Multi-agent simulation of the effects of Japanese electricity market policies on the low-carbon transition. Energy Strategy Reviews, 52, 101333.
  • Zhang, G., Wang, X., Wang, G., Suo, X., Qiu, Y., Luo, R. H., ... & Li, Y. (2024). Nanoparticles insert a three dimensional cavity structure of proteins for function inhibition: The Case of CeO2 and SARS-CoV-2. Nano Today, 55, 102183.
  • Zhang, J. (2024). Exploring the intra-platoon planning problem for heterogeneous train traffic under virtual coupling in the railway network via simulation. Journal of Rail Transport Planning & Management, 30, 100442.
  • Zhang, J., Yang, Y. E., Abeshu, G. W., Li, H., Hung, F., Lin, C. Y., & Leung, L. R. (2024). Exploring the food-energy-water nexus in coupled natural-human systems under climate change with a fully integrated agent-based modeling framework. Journal of Hydrology, 131048.
  • Zhang, J., Rong, L., & Gong, Y. (2024). An Agent-Based Transmission Model of Major Infectious Diseases Considering Places: Forecast and Control. Mathematics, 12(6), 811.
  • Zhang, R., & Bing, S. U. N. (2024). Complex adaptive system theory, agent-based modeling, and simulation in dominant technology formation. Journal of Systems Engineering and Electronics, 35(1), 130-153.
  • Zhang, X., Pitera, K., & Wang, Y. (2024). Exploring parking choices under the coexistence of autonomous and conventional vehicles. Physica A: Statistical Mechanics and its Applications, 129542.
  • Zhang, Y. (2024). An Investigation of the Housing Market and the Supply Modelling Process (Doctoral dissertation, University of Toronto (Canada)).
  • Zhang, Y., Ma, B., Cao, L., & Liu, Y. (2024). A Data-Driven Pandemic Simulator with Reinforcement Learning. Electronics, 13(13), 2531.
  • Zhang, Y., Zhang, B., & Hou, J. (2024). Simulation Study on Student Residential Energy Use Behaviors: A Case Study of University Dormitories in Sichuan, China. Buildings, 14(5), 1484.
  • Zhang, Y., Zhang, X., & Song, J. (2024). Enterprise intelligent financial sharing mechanism in the security environment of the internet of things. International Journal of Information and Computer Security, 24(1-2), 80-97.
  • Zhang, Z., Tang, Y., Tao, C., Zhang, J., Dong, F., Liu, S., ... & Wang, X. (2024). Mesoscopic ring element growth and deformation induced biofilm streamer evolution in microfluidic channels. Water Science & Technology, wst2024168.
  • Zhao, L. (2024, June). Foregrounding Agency in Participatory Design with Children. In Proceedings of the 23rd Annual ACM Interaction Design and Children Conference (pp. 994-996).
  • Zhao, Y., Zhu, Z., Zhang, Y., Yang, Y., Guo, Y., & Zhou, W. (2024). Unveiling the evolution of urban rail transit network: considering ridership attributes. Transportation Letters, 1-12.
  • Zheng, Y. (2024). Uncovering Learning in Maker Education: Employing Collective Documentation and Natural Language Processing to Identify Knowledge Construction in Complex Open-Ended Learning Environments (Doctoral dissertation, Teachers College, Columbia University).
  • Zhong, Y. (2024). Rural Small Business Entrepreneurship Strategy Analysis with Optimization in Cooperation Industry Using Evolutionary Game Theory.
  • Zhou, B. (2024). A Cautionary Note on the Application of GIS in Spatial Optimization Modeling. Journal of Geographic Information System, 16(01), 89-113.
  • Zhou, X., Zhou, Y., Gong, Y., Cai, Z., Qiu, A., Xiao, Q., ... & Bai, Z. (2024, June). " Bee and I need diversity!" Break Filter Bubbles in Recommendation Systems through Embodied AI Learning. In Proceedings of the 23rd Annual ACM Interaction Design and Children Conference (pp. 44-61).
  • Zhu, J. (2024). The impact of merchant-induced reviews on consumers’ shopping behavior from the perspective of the digital economy. Current Psychology, 1-16.
  • Zhu, J., Zhang, C., Zhang, M., & Yuan, J. (2024). Modeling and Simulation of Technological Innovation Diffusion of High-performance Construction Materials in Transportation Engineering. Case Studies in Construction Materials, e03838.
  • Zhuang, T., & Lin, Z. (2024).The why, what, and how of AI-based coding in scientific research.
  • Zosh, C., Dhameja, N., Ren, Y., & Pape, A. (2024). A Guide and General Method for Estimating Parameters and their Confidence Intervals in Agent-Based Simulations with Stochasticity.
  • Al Aziz, R. R., & Oktavianto, D. (2024). Perancangan Model Simulasi Sistem Parkir Berbasis Agen Untuk Peningkatan Efisiensi Dan Pengalaman Pengguna. Jurnal Gembira: Pengabdian Kepada Masyarakat, 2(03), 700-708.
  • Alaminos, A., & Alaminos-Fernández, A. (2024). Teoría de juegos y Modelo Bifactorial-Inercia Incertidumbre.
  • Almenar Vicente, B. (2024). Diseño y programación de bots de inteligencia artificial para juegos de gestión de recursos (Doctoral dissertation, Universitat Politècnica de València).
  • Alvarado Maldonado, Z. K. (2024). Seguridad y Salud ocupacional de los trabajadores de obras en una empresa constructora de la región San Martín–2024.
  • Arvizu, C. C. Modelo complejo de la cadena de suministro en NetLogo.
  • Ballesteros Bahamón, A. L. (2024). Análisis de las dinámicas de interacción en la intermediación y su incidencia en las Capacidades de Absorción Potenciales en un Sistema de Innovación Agrícola.
  • Bazzanella, E., Barros, M. M., & Santos, F. (2024). Refatoraçao da Extensao NetLogo de Aprendizagem por Reforço para Integraçao com a Biblioteca BURLAP. In Este documento reúne os trabalhos apresentados nas sessões técnicas da 18ª edição do Workshop-Escola de Sistemas de Agentes, Seus Ambientes e Aplicações–Wesaac 2024, realizado na Universidade de Brasília, entre 14 e 16 de agosto de 2024. Durante as sessões técnicas, foram apresentados 21 artigos que (p. 51).
  • Blasco Sevilla, E. (2024). Integración de Godot y SPADE para la construcción de entornos 3D para la navegación de robots (Doctoral dissertation, Universitat Politècnica de València).
  • Cabuya-Padilla, D. E., & Castaneda-Marroquin, C. A. (2024). Marco de referencia para el modelamiento y simulación de la ciberdefensa marítima-MARCIM: estado del arte y metodología. DYNA, 91(231), 169-179.
  • Cárdenas Benavides, E. S. Optimizando el diseño instruccional mediante algoritmos genéticos (Doctoral dissertation, Universidad Nacional de Colombia).
  • Castillo Losada, L. T. (2024). La educación ambiental y las TIC en la educación secundaria y media en Colombia: una revisión de literatura.
  • Castillo Orellana, M. N. (2024). Medición de la nerviosidad en un plan maestro de producción mediante el uso de sistema multi agentes. Dirección de Bibliotecas, Universidad de Concepción.
  • CAZA, O., & RUDY, R. (2024). MODELO DE DIRECCIONAMIENTO VIAL MEDIANTE ALGORITMO DE COLONIA DE HORMIGAS Y TEORIA DE GRAFOS EN LA TRANSITABILIDAD VIAL DE EXTRANCA RIO SECO (Doctoral dissertation).
  • Correia, L. G., & Mena-Chalco, J. P. A INFLUÊNCIA DE AUTOCITAÇÕES NO FATOR DE IMPACTO: UMA COMPARAÇÃO DE DUAS POLÍTICAS DE CÁLCULO.
  • de Tutorías, H., Miras, R. M. S., Morales, J. M. R., Calderón, C. A., Stanek, M., & Riviére, J. Guía docente de Epistemología y ciencias sociales computacionales (MD6/56/1).
  • dos Santos Filho, E. C. (2024). Avaliação de Políticas Públicas de Segurança no Brasil: aplicando o Modelo Baseado em Agentes. Editora Dialética.
  • Ferreira12, E., Rete, O., & de Janeiro–RJ–Brasil, R. (2024). Simulaçao de Rotatitividade de Pessoal de TI através de um Sistema Multiagente: Desenvolvimento e Aplicaçoes. In Este documento reúne os trabalhos apresentados nas sessões técnicas da 18ª edição do Workshop-Escola de Sistemas de Agentes, Seus Ambientes e Aplicações–Wesaac 2024, realizado na Universidade de Brasília, entre 14 e 16 de agosto de 2024. Durante as sessões técnicas, foram apresentados 21 artigos que (p. 73).
  • Flores Briceño, F. (2024). Variantes del modelo de Deffuant para dinámica de opinión en poblaciones estructuradas (Master's thesis).
  • García-Bouza García, L. A. (2024). Causas del hundimiento de Albert Rivera tras las elecciones del 10-N (2019): ideología, discurso y estrategia política del líder de ciudadanos en twitter.
  • Garzón López, M. M. Metaheurística inspirada en Inteligencia Colectiva humana para problemas de optimización numérica continua.
  • Gaudou, B., & Adam, C. (2024, November). Une enquête sur les perceptions de la mobilité pour informer un simulateur multi-agent de choix modal. In Les 32èmes Journées Francophones sur les Systèmes Multi-Agents (JFSMA 2024).
  • Grande Avalos, F. J. Modelado basado en agentes para el estudio de la dinámica poblacional de Ctenocephalides felis en ambientes domésticos. Renałi.
  • Herrera Salgado, C. A. (2024). Impacto del liderazgo y transformacional en seguridad y el coaching sobre la cultura de seguridad en el sector minero en Antioquia.
  • Hidalgo, Á. L. N., Valbuena, S. B., & Robayo, D. Á. (2024). Las organizaciones como proceso evolutivo de conocimiento: perspectivas para su aplicación en el estudio del narcotráfico. Revista Criminalidad, 66(2).
  • Huanachin Ttito, H. L. (2024). Implementación de comunidades de aprendizaje profesionales en CETPRO de la UGEL 06: percepciones y desafíos.
  • JIMÉNEZ, N. H., AGUDELO, A. M. M., & BOTERO, S. L. HÉCTOR OLASOLO BEATRIZ GOENA VIVES.
  • Junior, G. T., de Macedo, W. C., Santos, B. L., Coelho, J. V. R., & Neto, V. V. G. Modelagem e Simulaçao de Evacuaçoes de Emergência em Estádios: Uma Abordagem Baseada em Agentes utilizando NetLogo.
  • Lengruber, H. (2024). Estratégias pedagógicas e recursos educacionais para o ensino de computação para alunos com deficiência visual: uma revisão sistemática de literatura.
  • Lanza, E. Predicción de Latencia en Microservicios con Modelos de Deep Learning. CACIC2024.
  • López Rivera, Y. M. Un modelo estocástico en cadenas de suministro agroalimentaria resilientes con alcance de exportación. Caso de estudio aguacate Hass (Doctoral dissertation, Universidad Nacional de Colombia).
  • Neves, J. E. D. A. (2024). Mineração de dados aplicada a simulação de cenários complexos em sistemas multiagentes (Doctoral dissertation).
  • Pobuda, P. (2024). El gemelo digital de la economía: Herramienta propuesta para el diseño y la evaluación de políticas. Propuestas para el Desarrollo, 8(VIII), 133-144.
  • Rebelato, J. G. B., Tacla, C. A., & Lugo, G. G. Abordagens multiagentes para resiliência e reestabelecimento do fornecimento de energia elétrica: Uma perspectiva logística na colaboração entre bases de atendimento durante eventos climáticos severos. In Este documento reúne os trabalhos apresentados nas sessões técnicas da 18ª edição do Workshop-Escola de Sistemas de Agentes, Seus Ambientes e Aplicações–Wesaac 2024, realizado na Universidade de Brasília, entre 14 e 16 de agosto de 2024. Durante as sessões técnicas, foram apresentados 21 artigos que (p. 27).
  • Roddeck, W. (2024). Wirtschaftssysteme und stochastische Prozesse. In Die Welt der Modelle und Simulationen: Wie der Mensch seit alters her versucht die Zukunft vorherzusagen (pp. 137-158). Wiesbaden: Springer Fachmedien Wiesbaden.
  • Rodríguez, R. A. L. (2024). Antropología y medición demográfica en el Estado multicultural colombiano. Tabula Rasa, (52), 13-43.
  • Rouchier, J. (2024). L’éthique des modèles de simulation: une question d’interprétation?. L'Année sociologique, 74(2), 495-527.
  • SALAS, D. U. LA LÚDICA Y SIMULACIÓN COMO HERRAMIENTAS PARA LA EDUCACIÓN AMBIENTAL ESTUDIO DE CASO: PROBLEMÁTICA DE PEQUEÑOS PRODUCTORES AGRÍCOLAS DE COLOMBIA.
  • Salinas, Á. J. M. (2024). Algunas implicaciones en la lógica sociológica al usar la inteligencia artificial.
  • Santos, A. R., & Santos, F. Introductory Guide to Agent-Based Simulation Development on the GAMA Platform 2. In Este documento reúne os trabalhos apresentados nas sessões técnicas da 18ª edição do Workshop-Escola de Sistemas de Agentes, Seus Ambientes e Aplicações–Wesaac 2024, realizado na Universidade de Brasília, entre 14 e 16 de agosto de 2024. Durante as sessões técnicas, foram apresentados 21 artigos que (p. 96).
  • Sosa, A. Z., & Rodríguez, L. G. R. (2024). Las Redes Sociales No Digitales en la Acción Colectiva: Modelo Basado en Agentes. Psicumex, 14, 1-32.
  • Vargas-Alejo, V., Montero-Moguel, L., & Reyes-Rodríguez, A. (2024). Evolución de Sistemas Conceptuales Durante un Programa de Actualización Docente Basado en Modelación Matemática. Bolema: Boletim de Educação Matemática, 38, e230198.
  • Zonas, C., & De, M. D. E. Agenda Ambiental Para Focalizar Acciones De Intervención en Las Ciudades Que Componen Las Tres Zonas Metropolitanas Del Estado De Hidalgo: Pachuca, Tula Y Tulancingo.
  • Correia, L. G. A influência de autocitações de periódicos no Fator de Impacto: uma análise a partir da simulação de redes sociais acadêmicas.
  • Cukla, A. R., & Mota, F. P. Visão computacional em ação e sistemas multiagentes.
  • de Azevedo, G. T. (2024). Sólidos de Revolução e Produção de Sorvetes Geométricos: Formação em Matemática e Pensamento Computacional. REMATEC, 19(47), e2024038-e2024038.
  • Gispert Segués, S. (2024). Modelització i estudi de patrons de mobilitat amb agents. Cas aplicat a Terrassa (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • امامی, & دهقانی سانیج. (2024). بررسی مسیر رفتار و آسیب‌شناسی بهره‌برداری از منابع آب دشت میاندوآب با استفاده از رویکرد ABM-FTA. پژوهش آب ایران.‎
  • خزاعی، صفا و نجفیانی. (2024). ارزیابی تخلیه اضطراری مجتمع‌های مسکونی با ساختمان‌های بلند (مطالعه موردی: شهرک پونک زنجان). مجله مدیریت اورژانس.
  • يوسف بن فراج الفراج. (2024). برنامج تطور مهني قائم على النمذجة البيئية ودوره في تعزيز تصورات معلمي العلوم نحو منهجية STEAM: دراسة نوعية. مجلة العلوم التربوية و النفسية, 17(2), 597-563.
  • 陈正宁, & 吴亮. (2024). 两栖登陆垂直投送指挥控制效能评估模型研究. 舰船电子工程, 44(4), 24-27.
  • 胡景博, 杨克巍, 蒋平, & 姜江. (2024). 基于复杂适应性理论的无人机作战试验交互协作问题研究. 舰船电子工程, 44(4), 6-10.
  • 宮崎正也. (2024). 普及速度と交友関係: 高齢者のイノベーション採用はなぜ遅いのか. 研究 技術 計画, 38(4), 476-493.
  • 曾荣燊, 李弼程, 陈刚, & 熊尧. (2024). 基于线上线下超网络模型的舆论演化仿真分析. Application Research of Computers/Jisuanji Yingyong Yanjiu, 41(2), 507-514.
  • 树建马, 淑媛杨, 瑛滕, & 佳贺张. (2024). 供应链金融中区块链技术的扩散模型仿真研究——以核心企业为例. 财经与管理· 国际学术论坛, 3(7).
  • Burghardt, M., Ruth, N., & Niekler, A. Einleitung: Zur Rolle von Tools in den Digital Humanities.
  • Mudrinić, V. (2024). Primjena sustava Replit u osnovnoškolskoj nastavi informatike u domeni računalnog razmišljanja i programiranja (Doctoral dissertation, University of Pula. Faculty of Informatics in Pula).
  • Černý, J. H. K. (2024). Agentově orientované modelování revolučních procesů1. Sociológia, 56(3), 189-219.
  • 大須賀昭彦. (2024). エージェント指向言語の過去・現在・未来. 人工知能, 39(3), 335-341.
  • 佐々木夏輝, & ササキナツキ. (2024). マルチエージェントシミュレーション用フレームワークの開発と評価. 人間情報学研究科年誌, (29), 82-85.
  • 相澤景, & 上野玄太. (2024). エージェントベースモデルにおけるエージェントの異質性を表現する確率分布の推定. 人工知能学会論文誌, 39(5), A-N82_1.
  • 양윤영, & 김재근. (2024). 생태학 NetLogo 실습 프로그램 개발을 위해 예비 생물교사들이 겪은 어려움 및 개선점 탐색. 생물교육 (구 생물교육학회지), 52(1), 13-31.
  • Diallo, A. O., Doniec, A., Lozenguez, G., & Mandiau, R. (2024). Approche centrée agent pour l’intermodalité basée sur des données réelles. Revue Ouverte d'Intelligence Artificielle, 5(1), 95-129.
  • DIARRA, A., KONE, M., & BOUHALOUAN, D. (2024). Outil Intelligent Distribué pour l’élaboration de pronostics médicaux-MedProMas (Doctoral dissertation).
  • Геренко, С. (2024). Когнітивно-функціональний потенціал штучного інтелекту в графічному дизайні. Scientific Collection «InterConf», 192, 123-125.
  • Зінов’єва, О. Г. (2024). Імітаційне моделювання в освітньому процесі підготовки ІТ-спеціалістів.
  • Афанасьев, А. П., & Колмогорова, С. С. ВОЗМОЖНОСТЬ ПРИМЕНЕНИЯ NETLOGO В ЗАДАЧЕ СБОРА ДАННЫХ. Санкт-Петербургский государственный лесотехнический университет им. СМ Кирова КОНФЕРЕНЦИЯ: АКТУАЛЬНЫЕ ВОПРОСЫ ЛЕСНОГО ХОЗЯЙСТВА Санкт-Петербург, 09–10 ноября 2023 года Организаторы: Санкт-Петербургский государственный лесотехнический университет им. СМ Кирова.
  • Власов, М. А., & Большаков, Р. С. (2024). Обзор Современного Состояния Разработок В Области Моделирования Железнодорожных Станций. Электронный Научный Журнал" Молодая Наука Сибири", (2 (24)).
  • Симавонян, К. Ю. ПРОЕКТИРОВАНИЕ ИНФОРМАЦИОННЫХ СИСТЕМ. ПРОЕКТИРОВАНИЕ WEB-САЙТА, КАК ИНФОРМАЦИОННОЙ СИСТЕМЫ.
  • Гайнанов, Д. А., Мигранова, Л. И., & Сайфуллина, Л. Д. (2024). Управление поведением индивидов на региональном рынке труда: подходы, модели и перспективы. Ars Administrandi (Искусство управления), 459-480.
  • Гостенина, В. И., Мельников, С. Л., Лапыко, Т. П., Юдина, Е. Н., & Лимонова, М. А. (2024). Разработка мультиагентных симуляционных моделей для изучения взаимодействия стейкхолдеров в сфере образования и обучения в угольной промышленности. Уголь, 1180(5), 115-123.
  • Набиева, А. И., & Исхаков, А. Р. МУЛЬТИАГЕНТНОЕ МОДЕЛИРОВАНИЕ ЭМЕРДЖЕНТНОГО ПОВЕДЕНИЯ ТЕПЛОВЫХ ЖУКОВ. ББК 30+ 22.1 Т38, 32.
  • Калягина, Д. А., Илларионова, В. И., & Шарнин, И. А. (2024) Проектный Подход К Управлению Логистическими Процессами В Условиях Цифровой Экономики.
  • Кузнецов, А. В., Серов, Н. А., & Адодина, М. М. (2024). Разработка платформы для многоагентного моделирования исторических сообществ. Вестник ВГУ. Серия: Системный анализ и информационные технологии, (3), 17-32.
  • Маслобоев, А. В. (2024). Обобщенная Методология Построения Мультиагентных Систем Управления Жизнеспособностью Критических Инфраструктур. Надежность И Качество Сложных Систем, (2 (46)), 134-146.
  • Маркіна, Л. М., Гуменюк, Л. О., Сацик, В. О., & Гуменюк, П. О. ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В МОДЕЛЮВАННІ В’ЯЗКОСТІ ЗАМІСУ СПИРТОВОГО ВИРОБНИЦТВА НА ОСНОВІ МУЛЬТИАГЕНТНИХ СИСТЕМ. МИ РАДІ ВІТАТИ ВАС У ЛНТУ!, 11.
  • Михайлович, Н. Р. К. С., & Симавонян, К. Ю. (2024). ПРОЕКТИРОВАНИЕ ИНФОРМАЦИОННЫХ СИСТЕМ. ПРОЕКТИРОВАНИЕ WEB-САЙТА, КАК ИНФОРМАЦИОННОЙ СИСТЕМЫ. Столыпинский вестник, 6(10).
  • Молодецька, К., & Веретюк, С. (2024). Імітаційна модель поширення вірусу в мережі з пріоритетним приєднанням. Організатор конференції, 39.
  • Оспанова, А. К. (2024). ББК 74.3 Ц42.
  • Савенкова, Д. А. (2024) Разработка скоринговой системы банка. ББК 1 Н 34, 43.
  • Сагимов, М. К. (2024). УПРАВЛЕНИЕ МОТИВАЦИЕЙ ТРУДА ПЕРСОНАЛА ГОСУДАРСТВЕННОЙ СЛУЖБЫ.
  • Шагалін, К. А. (2024). Вдосконалення методів захисту від атак на системи інтернету речей: кваліфікаційна (бакалаврська) робота 125 «Кібербезпека».
  • Καρυπίδου, Α. (2024). Τεχνολογικές καινοτομίες στην εκπαίδευση: προκλήσεις που προκύπτουν με τη χρήση των τεχνολογιών πληροφορίας και επικοινωνιών (ΤΠΕ) στην πρωτοβάθμια εκπαίδευση.
  • รุ่ง ทิวา เดชะ ปรากรม, & ชัยวัฒน์ ใบไม้. (2024). การ ประยุกต์ ใช้ แบบ จำลอง จาก ตัวแทน กับ งาน วิจัย ทาง ธุรกิจ. วารสาร เกษตรศาสตร์ ธุรกิจ ประยุกต์, 18(28), 119-142.
  • Grospietsch, F., Stinken-Rösner, L., Renger, A., Krell, M., & Lenzer, S. (2024). Barrieren beim Betreiben naturwissen-schaftlicher Erkenntnisgewinnung und wie ihnen mit Konzepten partizipations-förderlicher Digitalisierung begegnet werden kann. MNU Journal, 4.
  • Ainurohmah, S. (2024). Desain Ui/ux Aplikasi Sistem Lampu dan Suhu Otomatis pada Rumah Cerdas untuk Lansia (Doctoral dissertation, Universitas Islam Indonesia).
  • Ardyantha, D. R., & Abidin, M. R. I. (2024). Perancangan Identitas Visual Sebagai Representasi Cv Mulya Tempe Surabaya. Barik-jurnal S1 Desain Komunikasi Visual, 6(1), 192-206.
  • Farrand, J., Mulyatno, I. P., & Adietya, B. A. (2024). ANALISA EVAKUASI PENUMPANG DENGAN METODE PENDEKATAN SIMULASI BERDASARKAN ATURAN IMO MSC. 1/CIRC. 1533 PADA KAPAL PENYEBRANGAN PENUMPANG 5000 GT. Jurnal Teknik Perkapalan, 12(2).
  • Hardjana, A. C. (2024). Analisis Tipografi Dalam Konten Instagram@ Disbudparkotasemarang Sebagai Penguatan City Branding Semarang Simpul Ekonomi Jawa. Nirmana, 43-49.

2023

  • Abadi, B., & Haghaninia, M. (2023). Drivers of Forecasting the Behavioral Intention and Acceptance Behavior of the Hail Canon Technology (HCT): Using Logistic and System Dynamics Modeling. Chinese Geographical Science, 1-16.
  • Abdelshafie, A., Rupnik, B., & Kramberger, T. (2023). Simulated Global Empty Containers Repositioning Using Agent-Based Modelling. Systems, 11(3), 130.
  • Abdolhosseini, S., Ghandehari, M., Ansari, A., & Roozmand, O. (2023). Joint pricing and inventory management in a competitive market using reinforcement learning: a combination of the agent-based and simulation-optimization approaches. International Journal of Management Science and Engineering Management, 18(2), 77-87.
  • Adam, C. (2023). Simulating the impact of cognitive biases on the mobility transition. arXiv preprint arXiv:2302.03554.
  • Adams, J. W., Duprey, M., Khan, S., Cance, J., Rice, D. P., & Bobashev, G. (2023). Examining buprenorphine diversion through a harm reduction lens: an agent-based modeling study. Harm Reduction Journal, 20(1), 150.
  • Addido, J., Borowczak, A. C., & Walwema, G. B. (2023). Teaching Newtonian physics with LEGO EV3 robots: An integrated STEM approach. Eurasia Journal of Mathematics, Science and Technology Education, 19(6), em2280.
  • Adu-Kankam, K. O., & Camarinha-Matos, L. M. (2023). Modeling Collaborative Behaviors in Energy Ecosystems. Computers, 12(2), 39.
  • Adzinets, D., & Alooeff, E. (2023). Field Service Management (FSM) Simulation Model. International Scientific Journal “Industry 4.0”, 8(6), 321-325.
  • Aghamohammadghasem, M., Azucena, J., Hashemian, F., Liao, H., Zhang, S., & Nachtmann, H. (2023, December). System simulation and machine learning-based maintenance optimization for an inland waterway transportation system. In 2023 Winter Simulation Conference (WSC) (pp. 267-278). IEEE.
  • Agnelli, J. P., Buffa, B., Knopoff, D., & Torres, G. (2023). A Spatial Kinetic Model of Crowd Evacuation Dynamics with Infectious Disease Contagion. Bulletin of Mathematical Biology, 85(4), 23.
  • Ahedo, V., Santos, I., Galán, J. M., & Izquierdo, L. R. (2023). La identificación de enlaces ausentes como competición Kaggle para la enseñanza de teoría de redes. Dirección y Organización, (79), 18-28.
  • Ahmad, F., Shah, Z., & Al-Fagih, L. (2023). Applications of Evolutionary Game Theory in Urban Road Transport Network: A State of the Art Review. Sustainable Cities and Society, 104791.
  • Aksu, B., & Aksu, M. V. (2023). Organizational Mnemonics of Gray Collar Workers: Implementing SNA, ABM, and ANT. In Management and Organizational Studies on Blue-and Gray-collar Workers: Diversity of Collars (pp. 105-117). Emerald Publishing Limited.
  • Al-Bazi, A., Madi, F., Monshar, A. A., Eliya, Y., Adediran, T., & Khudir, K. A. (2023). Modelling the impact of non-pharmaceutical interventions on COVID-19 exposure in closed-environments using agent-based modelling. International Journal of Healthcare Management, 1-15.
  • Alam, A. (2023, March). Leveraging the Power of ‘Modeling and Computer Simulation’for Education: An Exploration of its Potential for Improved Learning Outcomes and Enhanced Student Engagement. In 2023 International Conference on Device Intelligence, Computing and Communication Technologies,(DICCT) (pp. 445-450). IEEE.
  • Albo Ismail, W. K. F., & Ucan, O. N. (2023). VANET PERFORMANCE EVALUATION IN TERMS OF NODES DISTRIBUTION, MOBILITY MODELS, AND ROUTING PROTOCOLS. Technium, 8, 32-45.
  • Alghamdi, A. A. (2023). A novel intelligent agent-based framework for appropriate stream selection from perceptive of career counseling. PeerJ Computer Science, 9, e1256.
  • Alkanjr, B., & Mahgoub, I. (2023). A Novel Deception-Based Scheme to Secure the Location Information for IoBT Entities. IEEE Access, 11, 15540-15554.
  • Ali, A. T., Leucker, M., Schuldei, A., Stellbrink, L., & Sachenbacher, M. (2023, September). A Comparative Analysis of Multi-agent Simulation Platforms for Energy and Mobility Management. In European Conference on Multi-Agent Systems (pp. 295-311). Cham: Springer Nature Switzerland.
  • Ali, G. A., Abubakar, H., Alzaeemi, S. A. S., Almawgani, A. H., Sulaiman, A., & Tay, K. G. (2023). Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation. Plos one, 18(9), e0286874.
  • Allison, A., Stephens, S., Blackett, P., Lawrence, J., Dickson, M. E., & Matthews, Y. (2023). Simulating the Impacts of an Applied Dynamic Adaptive Pathways Plan Using an Agent-Based Model: A Tauranga City, New Zealand, Case Study. Journal of Marine Science and Engineering, 11(2), 343.
  • Almaguer, D., Islas, C., Padilla, P., Prado, M. A., & Vizuet, D. F. Manejo biológico de una plaga usando un modelo multiagentes. Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI, 10, 140-146.
  • Alsammak, I. L. H., Mahmoud, M. A., Gunasekaran, S. S., Ahmed, A. N., & AlKilabi, M. (2023). Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption. IEEE Access, 11, 50962-50983.
  • Altuwariki, S. (2023). Modelling land use using demographic forecasting and local optimisation: A case study of general education provision in Riyadh, Saudi Arabia (Doctoral dissertation, UCL (University College London)).
  • Alves, D. B., de Souza Junior, A. J., & da Motta Jafelice, R. S. (2023). Intelligence technologies in Mathematics Education: AnyLogic for the production of learning objects. Revista Internacional de Pesquisa em Educação Matemática, 13(3), 1-23.
  • Amakama, N. J., Dusserre, G., Cadiere, A., Schuette, R. W., & Zacharewicz, G. (2023, April). Risk Management and Disaster Response In the Oil and Gas Industry: Modelling and Implementation of Interoperable Healthcare Systems Solution for Disaster Response in the Oil and Gas Industry. In Colloque IMT 2023:«Sécurité et Résilience».
  • Amakama, N. J., Dusserre, G., Cadiere, A., & Schuette, R. W. (2023, September). Assessing the Impact of Wait Times on Patient Mortality Outcomes in a Hypothetical Oil and Gas Industry Disaster Scenario: An Agent-Based Modeling Approach Using NetLogo. In QPSS 2023-Qatar Process Safety Symposium.
  • Amissah, M. (2023). Modelling and analysis of heterogeneous data to improve process flow in the emergency department (Doctoral dissertation, University of Warwick).
  • Ammoneit, R., Reudenbach, C., & Peter, C. (2023). Developing geographic computer modeling competencies in higher education. Journal of Geography in Higher Education, 1-23.
  • Amparore, E., Beccuti, M., Castagno, P., Pernice, S., Franceschinis, G., & Pennisi, M. (2023). From compositional Petri Net modeling to macro and micro simulation by means of Stochastic Simulation and Agent-Based models. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 9(1), 1-30.
  • Amrita, S., & Sankaran, S. (2023, April). Modeling the Impact of Fake Data Dissemination During Covid-19. In International Symposium on Intelligent Informatics: Proceedings of ISI 2022 (pp. 471-486). Singapore: Springer Nature Singapore.
  • An, Y., & Park, S. (2023). Developing an Agent-Based Model to Mitigate Famine Risk in North Korea: Insights from the “Artificial North Korean Collective Farm” Model. Land, 12(4), 735.
  • An, Y., Lin, X., Li, H., & Wang, Y. (2023). Sandpile-simulation-based graph data model for MVD generative design of shield tunnel lining using information entropy. Advanced Engineering Informatics, 57, 102108.
  • Andelfinger, P., & Uhrmacher, A. M. (2023). Synchronous speculative simulation of tightly coupled agents in continuous time on CPUs and GPUs. SIMULATION, 00375497231158930.
  • Annette, H. (2023). Modellieren und Forschen: Zum Inhalt. Unterricht Biologie, 2023(482), 4-48.
  • Anusha, C. D., & Raju, K. G. (2023, November). RWA for multi-domain optical network using OBGP. In AIP Conference Proceedings (Vol. 2587, No. 1). AIP Publishing.
  • Apostolidis-Afentoulis, V., & Sakellariou, I. (2023). Teleo-Reactive Agents in a Simulation Platform. Proceedings of the 15th International Conference on Agents and Artificial Intelligence, 1, 26–36.
  • Appiagyei, B. D., Belhoucine-Guezouli, L., Bessah, E., & Morsli, B. (2023). Simulating land use and land cover change in a semi-arid region from 1989 to 2039: the case of Hafir-Zariffet forest, Tlemcen, Algeria. GeoJournal, 1-15.
  • Arcón, V., Caridi, I., Pinasco, J. P., & Schiaffino, P. (2023). Segregation patterns for non-homogeneous locations in Schellings model. Communications in Nonlinear Science and Numerical Simulation, 120, 107140.
  • Ardon, L., Vann, J., Garg, D., Spooner, T., & Ganesh, S. (2023, May). Phantom-A RL-driven Multi-Agent Framework to Model Complex Systems. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (pp. 2742-2744).
  • Aringhieri, R., Di Cunzolo, M., Dutto, M., Genga, L., Guastalla, A., Locatelli, M., ... & Boccuzzi, A. (2023). Simulation, optimization, and process mining: practicals applications in healthcare. In Proceedings of the 3rd National Conference on Artificial Intelligence (Ital-IA 2023) (pp. 1-6). CEUR Workshop Proceedings.
  • Ariza Angarita, Y. (2023). Auto organización para la innovación curricular: una mirada desde las universidades (Doctoral dissertation, Universidad Simón Bolívar, Venezuela).
  • Armitage, J., & Magnusson, T. (2023). Agential Scores: Exploring Emergent, Self-Organising and Entangled Music Notation. In Proceedings of the 8th International Conference on Technologies for Music Notation and Representation (Northeastern University, Boston, Massachusetts, USA, 2023).
  • Arnold, E. G., Burroughs, E. A., Burroughs, O., & Carlson, M. A. (2023). Using physical simulations to motivate the use of differential equations in models of disease spread. International Journal of Mathematical Education in Science and Technology, 1-14.
  • Assayed, S., & Maheshwari, P. (2023). Agent-Based Simulation for University Students Admission: Medical Colleges in Jordan Universities. Computer Science & Engineering: An International Journal (CSEIJ), 13(1).
  • Ataeizadeh, F. (2023). Different Fairness Perceptions in Different Fairness Problems: Algorithmic Decisions and Strategic Interactions (Doctoral dissertation, Carleton University).
  • Ávila, D. Spin-glass y la mano invisible de Adam Smith (Doctoral dissertation, Universidad Nacional de Colombia).
  • Awad, M. (2023). Analysis and modeling of what honey bees (Apis mellifera L.) bring back to the hive and how that affects the health of the hive and humans (Doctoral dissertation, Colorado State University).
  • Azari, B. (2023). Developing a New Three-Dimensional Finite-Difference Explicit in Time Solver Package for MODFLOW (Doctoral dissertation, The University of Memphis).
  • Azin, B. (2023). Integrated Charging Facility Developments and Incentive-Based Demand Management for Electric Vehicles Using Agent-Based Simulation (Doctoral dissertation, The University of Utah).
  • Baccino, L., & Villata, S. (2023, May). How Does a Minority Opinion Spread?: An Agent-Based Model on the Opposition Between a Silent Majority and a Loud Minority. In The International FLAIRS Conference Proceedings (Vol. 36).
  • Baden-Böhm, F., Dauber, J., & Thiele, J. (2023). Biodiversity measures providing food and nesting habitat increase the number of bumblebee (Bombus terrestris) colonies in modelled agricultural landscapes. Agriculture, Ecosystems & Environment, 356, 108649.
  • Bai, Y (2023). Research on Civil Engineering Cost Prediction Based on Decision Tree Algorithm. Academic Journal of Architecture and Geotechnical Engineering, 5(1), 39-44.
  • Bai, Y., Deng, X., Weng, C., Hu, Y., Zhang, S., & Wang, Y. (2023). Investigating climate adaptation in semi-arid pastoral social-ecological system: A case in Hulun Buir, China. Environmental and Sustainability Indicators, 100321.
  • Baldauf, T. (2023). sfctools-A toolbox for stock-flow consistent, agent-based models. Journal of Open Source Software, 8(87), 4980.
  • Banerjee, D. (2023). Knowledge and Innovation on the Road to Adoption of Green Infrastructure Technology: Stormwater Management in Fast-Growing, Arid-Climate Utah Cities and Towns (Doctoral dissertation, The University of Utah).
  • Barrett, M. C. (2023). Lithics in Perspective: Indeterminacy, Simulation, and the Formation of Lithic Assemblages (Doctoral dissertation, The University of Auckland, New Zealand).
  • Barricelli, B. R., Fischer, G., Fogli, D., Mørch, A., Piccinno, A., & Valtolina, S. (2023). Cultures of Participation in the Digital Age-Artificial and/or Human Intelligence: Nurturing Computational Fluency in the Digital Age.
  • Barth, L., Schweiger, L., Benedech, R., & Ehrat, M. (2023). From data to value in smart waste management: Optimizing solid waste collection with a digital twin-based decision support system. Decision Analytics Journal, 100347.
  • Batta, E., & Stephens, C. R. (2023). Evolutionary success of the thrifty genotype depends on both behavioral adaptations and temporal variability in the food environment. Scientific Reports, 13(1), 7975.
  • Batzke, M., & Ernst, A. (2023). Conditions and Effects of Norm Internalization. Journal of Artificial Societies and Social Simulation, 26(1).
  • Bayram, A. (2023). Hybrid LCA–ABM of dairy farming systems including nonlinear optimization under environmental, technical and economic constraints (Doctoral dissertation, University of Luxembourg,​​ Luxembourg).
  • Becote, B. (2023). Defining a Cyber Operations Performance Framework via Computational Modeling (Doctoral dissertation, Dakota State University).
  • Beerman, J. T. (2023). To Err Is Human: The Effect of Mistakes in Social Simulations (Doctoral dissertation, Miami University).
  • Beerman, J. T., Beaumont, G. G., & Giabbanelli, P. J. (2023). A framework for the comparison of errors in agent-based models using machine learning. Journal of computational science, 72, 102119.
  • Belcore, O. M., Di Gangi, M., & Polimeni, A. (2023). Connected Vehicles and Digital Infrastructures: A Framework for Assessing the Port Efficiency. Sustainability, 15(10), 8168.
  • Béler, C., Zacharewicz, G., Bisgambiglia, P. A., Poggi, B., Poux, F., & Thierry, A. S. (2024). Towards (An Aggregated) Territorial Digital Twin: From Smart-village to Smart-territory via the Territorial System of Digital Twin. In Concepts in Smart Societies (pp. 328-356). CRC Press.
  • Bell, A. R., Rakotonarivo, O. S., Bhargava, A., Duthie, A. B., Zhang, W., Sargent, R., ... & Kipchumba, A. (2023). Financial incentives often fail to reconcile agricultural productivity and pro-conservation behavior. Communications Earth & Environment, 4(1), 27.
  • Bellvé, A. M. (2023). Reconstructing animal-vectored nutrient fluxes in paleoenvironments: A case-study of Aotearoa New Zealand’s burrowing procellariiforms (Doctoral dissertation, ResearchSpace@ Auckland).
  • Bemthuis, R., Govers, R., & Lazarova-Molnar, S. (2023, October). Using process mining for face validity assessment in agent-based simulation models: an exploratory case study. In International Conference on Cooperative Information Systems (pp. 311-326). Cham: Springer Nature Switzerland.
  • Benham, S. S. (2023). Landscape Genetics of the Gulf Coast Tick, Amblyomma maculatum (Doctoral dissertation, Old Dominion University).
  • Bennai, M. T., Guessoum, Z., Mazouzi, S., Cormier, S., & Mezghiche, M. (2023). Multi-agent medical image segmentation: A survey. Computer Methods and Programs in Biomedicine, 107444.
  • Berger, T., Bonte, T., Idel Mahjoub, Y., & Sallez, Y. (2023). Proposition of a software-assisted methodology to solve safety issues in reconfigurable assembly systems in a short time. International Journal of Computer Integrated Manufacturing, 1-26.
  • Bernardini, G., D’Orazio, M., & Quagliarini, E. (2023, September). Coupled Multi-risk Mitigation in Historical Urban Outdoor Built Environment: Preliminary Strategies Evaluation Through Typological Scenarios. In International Conference on Structural Analysis of Historical Constructions (pp. 1212-1226). Cham: Springer Nature Switzerland.
  • Bhat, S., Godse, R., Mestry, S., & Naik, V. (2023). Studying the Impact of Transportation During Lockdown on the Spread of COVID-19 Using Agent-Based Modeling. In ICAART (1) (pp. 80-92).
  • Biondo, A. E., Mazzarino, L., & Pluchino, A. (2023). Noise and Financial Stylized Facts: A Stick Balancing Approach. Entropy, 25(4), 557.
  • Bischoff, R. J., & Padilla-Iglesias, C. (2023). A description and sensitivity analysis of the ArchMatNet agent-based model. PeerJ Computer Science, 9, e1419.
  • Bjørnås, K. L., Railsback, S., & Piccolo, J. (2023). Modifying and parameterizing the individual-based model inSTREAM for Atlantic salmon and brown trout in the regulated Gullspång River, Sweden. MethodsX, 102243.
  • Blake-Westa, J. C., & Bersa, M. U. (2023). ScratchJr design in practice: Low floor, high ceiling. International Journal of Child-Computer Interaction, 100601.
  • Blakely, B., Horsthemke, W., Evans, N., & Harkness, D. (2023). Case Study A: A Prototype Autonomous Intelligent Cyber-Defense Agent. In Autonomous Intelligent Cyber Defense Agent (AICA) A Comprehensive Guide (pp. 395-408). Cham: Springer International Publishing.
  • Bogani, A., Faccenda, G., Riva, P., Richetin, J., Pancani, L., & Sacchi, S. (2023). The near-miss effect in flood risk estimation: A survey-based approach to model private mitigation intentions into agent-based models. International Journal of Disaster Risk Reduction, 103629.
  • Bolea Pérez, D. (2023). Integration of social values in a multi-agent platform running in a supercomputer (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Boman, B. M., Dinh, T. N., Decker, K., Emerick, B., Modarai, S., Opdenaker, L., ... & Schleiniger, G. (2023). Beyond the Genetic Code: A Tissue Code?. bioRxiv, 2023-03.
  • Bommi, R. M., Rajeev, S. V. S., Navya, S., Teja, V. S., & Supriya, U. (2023). Smart Health Care Waste Segregation and Safe Disposal. Mathematics and Computer Science, Volume 2, 205.
  • Borah, D. K., Zhang, H., Zellner, M., Ahmadisharaf, E., Babbar-Sebens, M., Quinn, N., ... & Lott, C. (2023). Total Maximum Daily Load Implementation Modeling, Planning, and Design: A Synthesis of Resources for Watershed Stakeholders. In World Environmental and Water Resources Congress 2023 (pp. 1298-1312).
  • Bort, J., Wiklund, J., Crawford, G. C., Lerner, D. A., & Hunt, R. A. (2023). The Strategic Advantage of Impulsivity in Entrepreneurial Action: An Agent-Based Modeling Approach. Entrepreneurship Theory and Practice, 10422587231178882.
  • Boss, L. N. (2023). Exploring Decentralized System Architectures and Their Influence on Performance and Robustness (Doctoral dissertation, The George Washington University).
  • Bourceret, A., Amblard, L., & Mathias, J. D. (2023). How do farmers’ environmental preferences influence the efficiency of information instruments for water quality management? Evidence from a social-ecological agent-based model. Ecological Modelling, 478, 110300.
  • Bowen, G. M., Wiseman, D., Shanahan, M. C., Khan, S., Gonsalves, A., Sengupta, P., ... & Carter, A. (2023). STEM in Canadian Teacher Education: An Overview. Reforming Science Teacher Education Programs in the STEM Era: International and Comparative Perspectives, 53-70.
  • Bozzi, A., Jimenez, J. F., Hernandez-Rodriguez, C., Gonzalez-Neira, E. M., & Trentesaux, D. (2023, July). Platoon-Based Distributed Control for Automated Material Handling Systems. In 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 2257-2262). IEEE.
  • Brazier, F. (2023, January). The Influence of National Culture on Evacuation Response Behaviour and Time: An Agent-Based Approach. In Multi-Agent-Based Simulation XXIII: 23rd International Workshop, MABS 2022, Virtual Event, May 8–9, 2022, Revised Selected Papers (Vol. 13743, p. 41). Springer Nature.
  • Brewer, L. L. (2023). Distruptions in Supply Chain: An Agent-Based Model Simulation to Measure Resiliency and Performance During Disasters (Doctoral dissertation, The University of North Carolina at Charlotte).
  • Breitwieser, L., Hesam, A., Rademakers, F., Luna, J. G., & Mutlu, O. (2023, February). High-performance and scalable agent-based simulation with BioDynaMo. In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (pp. 174-188).
  • Briggs, T. W. (2023). Essays in Computational Social and Organization Science: Manager-Subordinate Proximity, Informal Networks, and the Flynn Effect (Doctoral dissertation, George Mason University).
  • Brocardo, J., Vale, I., & Menezes, L. (2022). A investigação em resolução de problemas, raciocínio, comunicação e modelação: Uma análise de 30 anos de publicações na revista Quadrante. Quadrante, 31(2), 63-93.
  • Brodsky, J. The Role of Astrobiology in Systems Thinking Education. In Guidebook for Systems Applications in Astrobiology (pp. 210-222). CRC Press.
  • Broitman, D., & Czamanski, D. (2023). Resilience in a noisy urban system. Regional Science Policy & Practice.
  • Broutin, L. (2023). Prospecteurs des dunes et d'ailleurs: une étude géographique de l'expertise antiacridienne en Mauritanie (Doctoral dissertation, Université Paris-Nanterre).
  • Brudney, E. M. (2023). Building More Inclusive University Makerspaces for Students with Disabilities (Doctoral dissertation, The University of North Carolina at Chapel Hill).
  • Bryndin, E. (2023). Development of Artificial Intelligence of Ensembles of Software and Hardware Agents by Natural Intelligence on the Basis of Self-Organization. Journal of Research in Social Science and Humanities, 2(10), 13-22.
  • Bui, H., Sakurahara, T., Reihani, S., Kee, E., & Mohaghegh, Z. (2023). Probabilistic Validation: Computational Platform and Application to Fire Pra of Nuclear Power Plants. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 1-42.
  • Burrow, A. K., McEntire, K. D., & Maerz, J. C. (2023). Estimating the potential drivers of dispersal outcomes for juvenile gopher frogs (Rana capito) using agent-based models. Frontiers in Ecology and Evolution, 11, 1026541.
  • Bwire, C., Mohan, G., Karthe, D., Caucci, S., & Pu, J. (2023). A Systematic Review of Methodological Tools for Evaluating the Water, Energy, Food, and One Health Nexus in Transboundary Water Basins. Environmental Management, 1-16.
  • Calabrò, G., Le Pira, M., Giuffrida, N., Fazio, M., Inturri, G., & Ignaccolo, M. (2023). A spatial agent-based model of e-commerce last-mile logistics towards a delivery-oriented development. Transportation Research Interdisciplinary Perspectives, 21, 100895.
  • Calafat Montes, M. (2023). A Ssimulation model of passenger flow at the airport security system (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Calay, T. J., Qolomany, B., Mulahuwaish, A., Hossain, L., & Abdo, J. B. (2023, September). CCTFv1: Computational Modeling of Cyber Team Formation Strategies. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 199-208). Cham: Springer Nature Switzerland.
  • Calero Valdez, A., Nakayama, J., Vervier, L., Nunner, H., & Ziefle, M. (2023, July). Using Agent-Based Modeling to Understand Complex Social Phenomena-A Curriculum Approach. In International Conference on Human-Computer Interaction (pp. 368-377). Cham: Springer Nature Switzerland.
  • Cao, Z., Zhu, J., Tang, B., & Chen, T. (2023). System dynamics simulation of occupational health and safety management causal model based on NetLogo. Heliyon.
  • Caprioli, C., Bottero, M., & De Angelis, E. (2023). Combining an agent-based model, hedonic pricing and multicriteria analysis to model green gentrification dynamics. Computers, Environment and Urban Systems, 102, 101955.
  • Carrillo, C. C., Charbonneau, B. R., Altman, S., Keele, J. A., Pucherelli, S. F., Passamaneck, Y. J., ... & Swannack, T. M. (2023). Patterns of dreissenid mussel invasions in western US lakes within an integrated gravity model framework. Journal of Environmental Management, 332, 117383.
  • Castañón–Puga, M., Tirado–Ramos, A., Khatchikian, C., Suarez, E. D., Palafox–Maestre, L. E., & Gaxiola–Pacheco, C. G. (2023, June). Towards an Earned Value Management Didactic Simulator to Engineering Management Teaching. In International Conference on Computational Science (pp. 780-792). Cham: Springer Nature Switzerland.
  • Castañón-Puga, M., Rosales-Cisneros, R. F., Acosta-Prado, J. C., Tirado-Ramos, A., Khatchikian, C., & Aburto-Camacllanqui, E. (2023). Earned Value Management Agent-Based Simulation Model. Systems, 11(2), 86.
  • Catola, M., & Leoni, S. (2023). Pollution Abatement and Lobbying in a Cournot Game: An Agent-Based Modelling Approach. Computational Economics, 1-28.
  • Cavallo, D., & Briceño, A. (2023). Rizomas, epistemología y aprendizaje: reformulación y reestructuración de los entornos de aprendizaje. Revista 180, 1.
  • Čech, P., Mattoš, M., Anderková, V., Babič, F., Alhasnawi, B. N., Bureš, V., ... & Triantafyllou, I. (2023). Architecture-Oriented Agent-Based Simulations and Machine Learning Solution: The Case of Tsunami Emergency Analysis for Local Decision Makers. Information, 14(3), 172.
  • Cegielski, W. H. (2024). Networks, Agent-Based Modeling, and Archaeology. The Oxford Handbook of Archaeological Network Research, 280.
  • Celik, B., & Zorba, Y. (2023). AN APPLICATION OF AGENT-BASED TRAFFIC FLOW MODEL FOR MARITIME SAFETY MANAGEMENT EVALUATION. International Journal of Maritime Engineering, 165(A1), 55-70.
  • ÇETİNER, B., & YAŞARCAN, H. The El Farol Bar Problem: A Comparative Analysis of Expectation Models Used in Decision Making. Endüstri Mühendisliği, 34, 90-108.
  • Chaabani, S., Einum, S., Jaspers, V. L., Asimakopoulos, A. G., Zhang, J., & Muller, E. (2023). Impact of the antidepressant Bupropion on the Dynamic Energy Budget of Daphnia magna. Science of The Total Environment, 895, 164984.
  • Chatterjee, A., Cao, Q., Sajadi, A., & Ravandi, B. (2023). Deterministic random walk model in NetLogo and the identification of asymmetric saturation time in random graph. Applied Network Science, 8(1), 1-11.
  • Chella, A., Gaglio, S., Mannone, M., Pilato, G., Seidita, V., Vella, F., & Zammuto, S. (2023). Quantum planning for swarm robotics. Robotics and Autonomous Systems, 104362.
  • Chen, J., & Wilensky, U. J. (2023). Measuring Young Learners’ Open-ended Agent-based Programming Practices with Learning Analytics. Proceedings of AERA Annual Meeting 2023.
  • Chen, J., Zhao, L., Horn, M. S., & Wilensky, U. J. (2023, June). The Pocketworld Playground: Engaging Online, Out-of-School Learners with Agent-based Programming. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (pp. 267-277).
  • Chen, J., Horn, M. S., & Wilensky, U. J. (2023, June). NetLogo AR: Bringing Room-Scale Real-World Environments Into Computational Modeling for Children. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (pp. 736-739).
  • Chen, X., Wang, Z., Yang, H., Ford, A. C., & Dawson, R. J. (2023). Enhanced urban growth modelling: Incorporating regional development heterogeneity and noise reduction in a cellular automata model-a case study of Zhengzhou, China. Sustainable Cities and Society, 99, 104959.
  • Chen, Y., Wang, C., Du, X., Shen, Y., & Hu, B. (2023). An agent-based simulation framework for developing the optimal rescue plan for older adults during the emergency evacuation. Simulation Modelling Practice and Theory, 102797.
  • Cheng, S., Giabbanelli, P. J., & Kuang, Z. (2023, May). Identifying the Building Blocks of Social Simulation Models: A Qualitative Analysis using Open-Source Codes in NetLogo. In 2023 Annual Modeling and Simulation Conference (ANNSIM) (pp. 306-317). IEEE
  • Cheng, Y. (2023). Significance of Digital Landscape Architecture. In Digital Landscape Architecture: Logic, Structure, Method and Application (pp. 7-44). Singapore: Springer Nature Singapore.
  • Chetouani, M., Dignum, V., Lukowicz, P., & Sierra, C. (2023). The Advanced Course on Human-Centered AI: Learning Objectives. In Human-Centered Artificial Intelligence: Advanced Lectures (pp. 3-7). Cham: Springer International Publishing.
  • Chica, M., Hermann, R. R., & Lin, N. (2023). Adopting different wind-assisted ship propulsion technologies as fleet retrofit: An agent-based modeling approach. Technological Forecasting and Social Change, 192, 122559.
  • Chiriță, N., Delcea, C., Nica, I., & IONESCU, Ş. A. (2023). Financial contagion and identifying speculative frenzies: Unraveling price bubbles in cryptocurrency markets. Theoretical & Applied Economics, 30(3).
  • Chour, K., Reddinger, J. P., Dotterweich, J., Childers, M., Humann, J., Rathinam, S., & Darbha, S. (2023). An agent-based modeling framework for the multi-UAV rendezvous recharging problem. Robotics and Autonomous Systems, 166, 104442.
  • Christen Marote, S. (2023). Análisis de sistemas e instalaciones en torno a la arquitectura regenerativa, eficiencia energética y optimización de recursos naturales (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Christensen, K., Ma, Z. G., & Jørgensen, B. N. (2023). Multi-Agent Based Simulation for Investigating Electric Vehicle Adoption and Its Impacts on Electricity Distribution Grids and CO2 Emissions. In Energy Informatics. Academy Conference 2023.
  • Chu, C. M., & Van Noi, N. (2023). Optimising truck arrival management and number of service gates at container terminals. Maritime Business Review, 8(1), 18-31.
  • Chu, J., Morikawa, H., & Chen, Y. (2023). Simulation of SARS-CoV-2 epidemic trends in Tokyo considering vaccinations, virus mutations, government policies and PCR tests. BioScience Trends, 17(1), 38-53.
  • Chueca Del Cerro, C. (2023). Polarisation and protest mobilisation around secessionist movements: an agent-based model of online and offline social networks (Doctoral dissertation, University of Glasgow).
  • Cline, D. H., & Munson, J. (2024). Epigraphic Networks in Cross-Cultural Perspective. The Oxford Handbook of Archaeological Network Research, 363.
  • Cinquemani, L. (2023). Nationsim, a story-driven approach to Agent-Based Modeling of Nations interacting (Master's thesis, NTNU).
  • Cockx, B. J. R., Foster, T., Clegg, R. J., Alden, K., Arya, S., Stekel, D. J., ... & Kreft, J. U. (2023). Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0. bioRxiv, 2023-06.
  • Coelho, H. (2023). Interdisciplinary Practice in Education. In Theory and Practice in the Interdisciplinary Production and Reproduction of Scientific Knowledge (pp. 147-155). Springer, Cham.
  • Cogoni, F., Bernard, D., Kazhen, R., Valitutti, S., Lobjois, V., & Cussat-Blanc, S. (2023). ISiCell: involving biologists in the design process of agent-based models in cell biology. bioRxiv, 2023-06.
  • Corlu, M. S., Kurutas, B. S., & Ozel, S. (2023). Effective Online Professional Development: A Facilitator's Perspective. In M. Ludwig, S. Barlovits, A. Caldeira, & A. Moura (Eds.), Research On STEM Education in the Digital Age. Proceedings of the ROSEDA Conference (pp. 9-23). WTM.
  • Cortés, C. E., & Stefoni, B. (2023). Trajectory Simulation of Emergency Vehicles and Interactions with Surrounding Traffic. Journal of Advanced Transportation, 2023.
  • Costas, J., Puche, J., Ponte, B., Gupta, M. (2023). An agent-based simulator for quantifying the cost of uncertainty in production systems. Simulation Modelling Practice and Theory, 123.
  • Covitt, B. A., Gunckel, K. L., Berkowitz, A., Woessner, W. W., & Moore, J. (2023). Employing a Groundwater Contamination Learning Experience to Build Proficiency in Computational Modeling for Socioscientific Literacy. Journal of Science Education and Technology, 1-23.
  • Crespi, C., Scollo, R. A., Fargetta, G., & Pavone, M. (2023). A sensitivity analysis of parameters in an agent-based model for crowd simulations. Applied Soft Computing, 146, 110684.
  • Crespi, C., Scollo, R. A., Fargetta, G., & Pavone, M. (2023, February). How a Different Ant Behavior Affects on the Performance of the Whole Colony. In Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings (pp. 187-199). Cham: Springer International Publishing.
  • Crevier, L. P. (2023). Bears, spirals, and stakeholders: agent-based models and the need for stakeholder involvement in their development and implementation (Doctoral dissertation, University of British Columbia).
  • Cuevas, E., Zaldívar, D., & Pérez-Cisneros, M. (2023). Exploring the Potential of Agent Systems for Metaheuristics. In New Metaheuristic Schemes: Mechanisms and Applications (pp. 11-74). Cham: Springer Nature Switzerland.
  • Cuevas, E., Zaldívar, D., & Pérez-Cisneros, M. (2023). New Metaheuristic Schemes: Mechanisms and Applications (Vol. 246). Springer Nature.
  • Cui, T., & Cao, S. (2024). Development of the Female Internet Celebrity Economy Based on MATLAB Analysis Mode. In INTERNET FINANCE AND DIGITAL ECONOMY: Advances in Digital Economy and Data Analysis Technology The 2nd International Conference on Internet Finance and Digital Economy, Kuala Lumpur, Malaysia, 19–21 August 2022 (pp. 275-288).
  • Cui, T., & Cao, S. (2023, August). 2024 World Scientific Publishing Company. In Internet Finance And Digital Economy: Advances In Digital Economy And Data Analysis Technology-Proceedings Of The 2nd International Conference (p. 275). World Scientific.
  • da Silva Gallo, E. R., Bertella, M. A., & da Fonseca, C. N. (2023). Aversão à perda em um mercado acionário virtual: uma abordagem agent-based. Revista de Economia, 43(81), 442-471.
  • Daems, D., & Boogers, S. (2023). The Power of Emergence: The Effects of Bottom-Up Decision-Making in Resource Exploitation Strategies on Community Sustainability in Iron Age to Hellenistic Anatolia. In Modelling Human-Environment Interactions in and beyond Prehistoric Europe (pp. 133-142). Cham: Springer International Publishing.
  • Dagienė, V., Gülbahar, Y., Grgurina, N., López-Pernas, S., Saqr, M., Apiola, M., & Stupurienė, G. (2023). Computing Education Research in Schools. In Past, Present and Future of Computing Education Research: A Global Perspective (pp. 481-520). Cham: Springer International Publishing.
  • Davey, T. (2023). Cohesion: A Measure of Organisation and Epistemic Uncertainty of Incoherent Ensembles. Entropy, 25(12), 1605.
  • David, J., & Wu, J. (Eds.). (2023). Mathematics of Public Health: Mathematical Modelling from the Next Generation (Vol. 88). Springer Nature.
  • Davis, P. K. (2023). Supporting Social Science and Management Areas. In Body of Knowledge for Modeling and Simulation: A Handbook by the Society for Modeling and Simulation International (pp. 373-382). Cham: Springer International Publishing.
  • De Cubber, L., Lefebvre, S., Lancelot, T., Jorge, D. S. F., & Gaudron, S. M. (2023). Unravelling mechanisms behind population dynamics, biological traits and latitudinal distribution in two benthic ecosystem engineers: A modelling approach. Progress in Oceanography, 103154.
  • de Gauna, D. E. R., Sánchez, L. E., Ruiz-Iniesta, A., Villalonga, C., & Serrano, M. A. (2023). Towards an integrated swarm intelligence framework for urban mobility: A systematic review and proposed theoretical model. Journal of King Saud University-Computer and Information Sciences, 101836.
  • de Jager, M., Buitendijk, N. H., Baveco, J. M., van Els, P., & Nolet, B. A. (2023). Limiting scaring activities reduces economic costs associated with foraging barnacle geese: Results from an individual‐based model. Journal of Applied Ecology.
  • De La Paz, S., Levin, D. M., & Butler, C. (2023). Addressing an Unfulfilled Expectation: Teaching Students With Disabilities to Write Scientific Arguments. Written Communication, 07410883221149093.
  • De Luca, G., & Simoni, M. (2023). The role of trust in the diffusion of privacy-invading digital technologies. Technology Analysis & Strategic Management, 1-14.
  • De Nicola, R., Di Stefano, L., Inverso, O., & Valiani, S. (2023). Modelling flocks of birds and colonies of ants from the bottom up. International Journal on Software Tools for Technology Transfer, 1-17.
  • de Souza, G. F., & Lopes, P. T. C. (2023). APLICAÇÃO DO PENSAMENTO COMPUTACIONAL NO ENSINO, UMA REVISÃO SISTEMÁTICA DE LITERATURA. Interfaces Científicas-Educação, 12(1), 144-165.
  • de Sousa Oliveira, K. K., da Silva Marcolino, A., de Deus, W. S., Falcão, T. P., & Barbosa, E. F. (2023). Pensamento Computacional na Programação Introdutória e Habilidades do Século XXI: Um Mapeamento Sistemático da Literatura. Revista Novas Tecnologias na Educação, 21(2), 519-531.
  • do Amaral Pinto, G., & Alberte, E. P. V. (2023). Modelagem baseada em agentes aplicada a estudos para incorporação imobiliária: um panorama acerca da literatura. SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E COMUNICAÇÃO NA CONSTRUÇÃO, 4, 1-11.
  • Debacher, N. M., Kuster, L. F., dos Santos, A. F., Vahldick, A., & Santos, F. (2023, September). Back to the Promotion-EvacSIM: a Serious Game to Practice Requirements Elicitation on an Agent-based Simulation. In Anais do XX Encontro Nacional de Inteligência Artificial e Computacional (pp. 169-183). SBC.
  • Debie, E., Kasmarik, K., & Garratt, M. (2023). Swarm robotics: A Survey from a Multi-tasking Perspective. ACM Computing Surveys.
  • DelaPaz-Ruíz, N., Augustijn, E. W., Farnaghi, M., & Zurita-Milla, R. (2023). Spatiotemporal domestic wastewater variability: Assessing implications of population mobility in pollutants dynamics. AGILE: GIScience Series, 4, 23.
  • Delcea, C., & Chirita, N. (2023). Exploring the Applications of Agent-Based Modeling in Transportation. Applied Sciences, 13(17), 9815.
  • Delcea, C., & Cotfas, L. A. (2023). Risk Assessment and Transport Cost Reduction Based on Grey Clustering. In Advancements of Grey Systems Theory in Economics and Social Sciences (pp. 139-178). Singapore: Springer Nature Singapore.
  • Delcea, C., Yang, Y., Liu, S., & Cotfas, L. A. (2023). Agent-Based Modelling in Grey Economic Systems. In Emerging Studies and Applications of Grey Systems (pp. 105-139). Singapore: Springer Nature Singapore.
  • Deshpande, S., & Hsieh, S. J. (2023). Cyber-Physical System for Smart Traffic Light Control. Sensors, 23(11), 5028.
  • Dimka, J. (2023). An Agent-Based Simulation Model of Epidemic Spread in a Residential School for Children with Disabilities. Scandinavian Journal of Disability Research, 25(1).
  • Ding, H., & Xie, L. (2023). Simulating rumor spreading and rebuttal strategy with rebuttal forgetting: An agent-based modeling approach. Physica A: Statistical Mechanics and its Applications, 128488.
  • Domenteanu, A., Delcea, C., Chiriță, N., & Ioanăș, C. (2023). From Data to Insights: A Bibliometric Assessment of Agent-Based Modeling Applications in Transportation. Applied Sciences, 13(23), 12693.
  • Dong, D. (2023). Agent-based cloud simulation model for resource management. Journal of Cloud Computing, 12(1), 1-24.
  • Dong, J., Tian, M., Li, X., & Crossan, M. Effects of human capital and learning rate: When organizations meet with information distortion and environmental dynamism. European Management Review.
  • Doran, J. W., Thompson, R. N., Yates, C. A., & Bowness, R. (2023). Mathematical methods for scaling from within-host to population-scale in infectious disease systems. Epidemics, 100724.
  • Doroudi, S. (2023). The forgotten African American innovators of educational technology: stories of education, technology, and civil rights. Learning, Media and Technology, 1-17.
  • dos Santos, N. T., dos Prazeres, J. B., Braga, R. M., & do Espírito Santo, A. O. A DIMENSÃO CRÍTICA DA MODELAGEM MATEMÁTICA E DA EDUCAÇÃO AMBIENTAL: REVISÃO DE LITERATURA E APLICAÇÃO PRÁTICA. MODELAGEM MATEMÁTICA: RE/CONSTRUÇÃO DE PERSPECTIVAS, 15.
  • Du, W., Zhu, S., Tong, L., Cai, K., & Liang, Z. (2023). Robust gate assignment to minimise aircraft conflicts. Transportmetrica B: Transport Dynamics, 11(1), 2185497.
  • Dutcher, K. E., Nussear, K. E., Heaton, J. S., Esque, T. C., & Vandergast, A. G. (2023). Move it or lose it: Predicted effects of culverts and population density on Mojave desert tortoise (Gopherus agassizii) connectivity. Plos one, 18(9), e0286820.
  • Ebrie, A. S., Paik, C., Chung, Y., & Kim, Y. J. (2023). Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning. Energies, 16(16), 5920.
  • Edali, M. (2023). Using linear regression metamodels for evaluating interventions in an individual-based influenza epidemic model. Simulation Modelling Practice and Theory, 126, 102772.
  • Egger, C., Mayer, A., Bertsch-Hörmann, B., Plutzar, C., Schindler, S., Tramberend, P., ... & Gaube, V. (2023). Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning. Agronomy for Sustainable Development, 43(3), 39.
  • Eglash, R. (2023). Ethno-biomathematics: A Decolonial Approach to Mathematics at the Intersection of Human and Nonhuman Design. In Ubiratan D’Ambrosio and Mathematics Education: Trajectory, Legacy and Future (pp. 289-303). Cham: Springer International Publishing.
  • El-Maghraby, I. M., Jahin, H., & El-Hagla, K. S. (2023, September). Computational-based Generative Design Exploration, Multi-Agent System as an Approach. In LET IT GROW, LET US PLAN, LET IT GROW. Nature-based Solutions for Sustainable Resilient Smart Green and Blue Cities. Proceedings of REAL CORP 2023, 28th International Conference on Urban Development, Regional Planning and Information Society (pp. 145-154). CORP–Competence Center of Urban and Regional Planning.
  • Elara, L., & McCarthy, K. S. (2023). Exploring Supports to Enhance Learning from Online Virtual Experiments in Science. American Journal of Distance Education, 1-19.
  • Elsayed, P., Mostafa, H., & Marzouk, M. (2023). BIM based framework for building evacuation using Bluetooth Low Energy and crowd simulation. Journal of Building Engineering, 106409.
  • Elsner, J., Sadler, T., Kirk, E., Rawson, R., Friedrichsen, P., & Ke, L. (2023). Using Multiple Models to Learn about COVID-19 Breadcrumb. The Science Teacher, 90(3).
  • EMELYANOV, I., KIRILCHUK, I., BARKOV, A., & PERSIDSKAYA, K. (2023). USE OF INTELLIGENT TRANSPORT SYSTEMS TO IMPROVE ENVIRONMENTAL SAFETY OF ROAD TRANSPORT IN THE KURSK REGION. Мир транспорта и технологических машин, 59.
  • Epstein, J. M. (2023). Inverse generative social science: Backward to the future. Journal of artificial societies and social simulation: JASSS, 26(2).
  • Erceg, M. (2023). Primjena modeliranja temeljenog na agentima u simulaciji tržišta (Doctoral dissertation, University of Split. Faculty of economics Split).
  • Escobar, H., Cuevas, E., Toski, M., Ceron, F., & Perez-Cisneros, M. (2023). An agent-based model for public security strategies by predicting crime patterns. IEEE Access.
  • Eslamizadeh, S., Ghorbani, A., & Weijnen, M. (2023). Establishing industrial community energy systems: Simulating the role of institutional designs and societal attributes. Journal of Cleaner Production, 138009.
  • Esmaelnezhad, D., Taghizadeh-Yazdi, M., Mahdiraji, H. A., & Vrontis, D. (2023). International strategic alliances for collaborative product Innovation: An agent-based scenario analysis in biopharmaceutical industry. Journal of Business Research, 158, 113663.
  • Essghaier, F., Chargui, T., Hsu, T., Bekrar, A., Allaoui, H., Trentesaux, D., & Goncalves, G. (2023). Fuzzy multi-objective truck scheduling in multi-modal rail-road Physical Internet hubs. Computers & Industrial Engineering, 109404.
  • Estrada-Jimenez, L. A., Kalateh, S., Hojjati, S. N., & Barata, J. (2023, June). A Bio-inspired and Altruistic-Based Framework to Support Collaborative Healing in a Smart Manufacturing Shop-Floor. In Doctoral Conference on Computing, Electrical and Industrial Systems (pp. 111-121). Cham: Springer Nature Switzerland.
  • Estrada-Jimenez, L. A., Kalateh, S., Hojjati, S. N., & Barata, J. (2023, June). A Bio-inspired and Altruistic-Based Framework to Support Collaborative Healing in a Smart Manufacturing Shop-Floor. In Technological Innovation for Connected Cyber Physical Spaces: 14th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2023, Caparica, Portugal, July 5–7, 2023, Proceedings (Vol. 678, p. 111). Springer Nature.
  • Estrada-Jimenez, L. A., Pulikottil, T. B., Nikghadam-Hojjati, S., & Barata, J. (2023). Self-organization in Smart Manufacturing-Background, Systematic Review, Challenges and Outlook. IEEE Access.
  • Evans, B. (2023). Strategic decision-making in multi-agent markets: The emergence of endogenous crises and volatility (Doctoral dissertation).
  • Farhat, D. (2023). The economics and evolution of heroic behavior. Theoretical & Applied Economics, 30(3).
  • Fayad, P., Hadjipetrou, S., Leventis, G., Kavroudakis, D., & Kyriakidis, P. (2023). Designing an Agent-Based Model for a City-Level Simulation of COVID-19 Spread in Cyprus. In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023) (pp. 218-224).
  • Feng, J., Liu, B., Tang, J., & Wang, Q. E. (2023). The Emergence of the Contractor’s Innovation Capability at Project Level: An Agent-Based Modeling Approach. Buildings, 13(12), 2941.
  • Feng, J. R., Zhao, M., Yu, G., Zhang, J., & Lu, S. (2023). Dynamic risk analysis of accidents chain and system protection strategy based on complex network and node structure importance. Reliability Engineering & System Safety, 109413.
  • Feng, S., Laili, Y., Zhang, L., & Zhang, L. (2023, January). Model Library System Based on Multi-Domain Simulation Model Integration Interface. In Intelligent Networked Things: 5th China Conference, CINT 2022, Urumqi, China, August 7-8, 2022, Revised Selected Papers (pp. 491-500). Singapore: Springer Nature Singapore.
  • Feng, Y., Zhou, C., Zou, Q., Liu, Y., Lyu, J., & Wu, X. (2023). A goal-based approach for modeling and simulation of different types of system-of-systems. Journal of Systems Engineering and Electronics, 34(3), 627-640.
  • Feuerwerker, S., Cockrell, R. C., & An, G. (2023). Characterizing the Crosstalk Between Programmed Cell Death Pathways in Cytokine Storm With an Agent-Based Model. Surgical Infections.
  • Ferrare, F. D. Otimização do UAM usando modelagem de VTOL baseada em sistemas multiagentes (Doctoral dissertation, Universidade de São Paulo).
  • Ferreyra Coroy, V. M. (2023). Introducción al concepto de integral mediante un contexto de aproximación de la longitud de una curva asociada a la columna vertebral con apoyo de tecnología digital (Master's thesis, Tesis (MC)--Centro de Investigación y de Estudios Avanzados del IPN Departamento de Matemática Educativa).
  • Fikirli, Ö., & Şahin, H. Türkiye’de e-ticaret difüzyon patikası: Ajan bazlı modelleme. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(2), 265-283.
  • Filandri, M., Pasqua, S., & Priori, E. (2023). Breaking through the glass ceiling. Simulating policies to close the gender gap in the Italian academia. Socio-Economic Planning Sciences, 88, 101655.
  • Fischer, H., Wijermans, N., & Schlüter, M. (2023). Testing the Social Function of Metacognition for Common‐Pool Resource Use. Cognitive Science, 47(3), e13212.
  • Francos, R. M., & Bruckstein, A. M. (2023). Guaranteed Evader Detection in Multi-Agent Search Tasks using Pincer Trajectories. arXiv preprint arXiv:2305.00533.
  • Flores López, A. C. (2023). Modelización basada en el individuo de sistemas de tratamiento anaerobio en biopilas de suelos contaminados por hidrocarburos de petróleo (Bachelor's thesis, Quito: UCE).
  • Foini, D., Rzyska, M., Baschmakov, K., & Murino, S. (2023). CrowdLogo: crowd simulation in NetLogo. arXiv preprint arXiv:2302.11036.
  • Francos, R. M., & Bruckstein, A. M. (2023, September). Spiral Sweeping Protocols for Detection of Smart Evaders. In Annual Conference Towards Autonomous Robotic Systems (pp. 89-100). Cham: Springer Nature Switzerland.
  • Freire, M., Marichal, R., Dufrechou, E., & Ezzatti, P. (2023, August). in Sparse Matrix Kernels. In Cloud Computing, Big Data & Emerging Topics: 11th Conference, JCC-BD&ET 2023, La Plata, Argentina, June 27–29, 2023, Proceedings (p. 17). Springer Nature.
  • Fuhrmann, T., Wagh, A., Rosenbaum, L. F., Eloy, A., Wilkerson, M., & Blikstein, P. (2023). How Can Computational Modeling Help Students Shift Their Ideas Towards Scientifically Accurate Explanations?. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 441-448. International Society of the Learning Sciences.
  • Gamalaldin, Y. (2023). The role of procedural utility in land market dynamics in Greater Cairo: an agent based model application. Environment and Planning B: Urban Analytics and City Science.
  • Gao, D., & Yang, Y. (2023). Identifying the impact of artifacts-based exploration and exploitation on routines’ formation dynamics: An agent-based model. Journal of Artificial Societies and Social Simulation, 26(3), 5.
  • Garcia Davalos, A. (2023). Mobile advertising spreading through personal social networks using a viral approach and branded apps (Doctoral dissertation, Enxeñaría telemática).
  • Garcia-Diaz, J. G. (2023). WNT LIGAND-SPECIFIC SIGNALING IN BONE (Doctoral dissertation, Johns Hopkins University).
  • Garcia, J. M., & Bittencourt, R. A. (2023, April). Um Mapeamento Sistemático da Literatura sobre Pensamento Computacional na Perspectiva dos Fundamentos Teóricos de Aprendizagem. In Anais do III Simpósio Brasileiro de Educação em Computação (pp. 01-12). SBC.
  • Garcia Davalos, A. (2023). Mobile advertising spreading through personal social networks using a viral approach and branded apps (Doctoral dissertation, Enxeñaría telemática).
  • Garcia-Davalos, A., & Garcia-Duque, J. (2023). A Simplified Mobile Advertising Model to Study Advertising Spreading through Personal Social Networks and Branded Apps. Journal of Promotion Management, 1-34.
  • Garnelo, I., & Islas, C. (2023). ¿ Hasta dónde alcanza realmente la potencia explicativa de los modelos basados en la autoorganización en el ámbito del aprendizaje? How far does the explanatory power of models based on self-organization really reach in the field of learning?. LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades, 4(2), 3307-3320.
  • Garzón, M., Álvarez-Pomar, L., & Rojas-Galeano, S. (2023, February). An Agent-Based Model of Follow-the-leader Search Using Multiple Leaders. In Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings (pp. 499-505). Cham: Springer International Publishing.
  • Gaur, S., & Singh, R. (2023). A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability, 15(2), 903.
  • Geldart, E. A., Love, O. P., Barnas, A. F., Harris, C. M., Gilchrist, H. G., & Semeniuk, C. A. (2023). A colonial-nesting seabird shows limited heart rate responses to natural variation in threats of polar bears. Royal Society Open Science, 10(10), 221108.
  • Genin, A., Dupont, G., Valencia, D., Zucconi, M., Avila-Thieme, M. I., Navarrete, S., & Wieters, E. (2023). Easy, fast and reproducible Stochastic Cellular Automata with'chouca'. bioRxiv, 2023-11.
  • Gerdes, L., Aigner, E., Meretz, S., Pahl, H., Schlemm, A., Scholz-Wäckerle, M., ... & Sutterlütti, S. (2023). COMMONSIM: Simulating the utopia of COMMONISM. Review of Evolutionary Political Economy, 1-37.
  • Ghaitaranpour, A., Koocheki, A., & Mohebbi, M. (2023). Simulation of bread baking with a conceptual agent-based model: An approach to study the effect of proofing time on baking behavior. Journal of Food Engineering, 111920.
  • Ghaleb, M., & Azzedin, F. (2023). Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach. Applied Sciences, 13(6), 3665.
  • Ghazimirsaeid, S. S., Jonban, M. S., Mudiyanselage, M. W., Marzband, M., Martinez, J. L. R., & Abusorrah, A. (2023). Multi-agent-based Energy Management of multiple Grid-connected green buildings. Journal of Building Engineering, 106866.
  • Giltri, M. (2023). From Real Affective States towards Affective Agents Modeling (Doctoral dissertation, University of Milano-Bicocca).
  • Gómez Herrera, J. S. Aplicación de un modelo de simulación para evaluar la difusión de taxis eléctricos en Colombia (Doctoral dissertation, Universidad Nacional de Colombia).
  • Gong, R., Hase, K., Ishikawa, N., Koshiba, R., & Minagawa, T. (2023). Pandemic Offline Informatics Education Using NetLogo-based Simulation for Course Scheduling in Japan. Bulletin of the Technical Committee on Learning Technology (ISSN: 2306-0212), 23(1), 13-19.
  • Gotschalk, P. A. (2023). Affluenza. In Dictionary of Ecological Economics (pp. 7-8). Edward Elgar Publishing.
  • Grajdura, S., Espeland, S., LanzDuret-Hernandez, J., & Rowangould, D. SARAH GRAJDURA, PH. D. Transportation Research Part D: Transport and Environment, 104, 103190.
  • Granger, J. N. (2023). Behavioral and Geophysical Factors Influencing Success in Long Distance Navigation (Doctoral dissertation, Duke University).
  • Grgurina, N., Tolboom, J., & de Vries, B. P. (2023, October). Evaluating the New Secondary Informatics Curriculum in The Netherlands: The Teachers’ Perspective. In International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 155-166). Cham: Springer Nature Switzerland.
  • Grønbakken, J. A. (2023). Mathematical modeling of multi-agent search & task allocation (Master's thesis).
  • Grotzer, T. A., & Solis, S. L. (2023). Thinking Like an Earthling: Children's Reasoning About Individual and Collective Action Related to Environmental Sustainability. Topics in Cognitive Science.
  • Gruzauskas, V., Burinskiene, A., & Krisciunas, A. (2023). Application of Information-Sharing for Resilient and Sustainable Food Delivery in Last-Mile Logistics. Mathematics, 11(2), 303.
  • Gu, H., Feng, L., & Zhen, X. (2022). Study on the stability of anaerobic digestion of food waste and the waste mushroom substrate based on SBR reactor and Netlogo simulation. Journal of Material Cycles and Waste Management, 1-17.
  • GÜLMEZ, B. (2023). Market zinciri ürün dağıtımı probleminin farklı genetik algoritma versiyonları ile çözümü ve karşılaştırması. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 180-196.
  • Gumahad, B., & Collins, A. (2023). Visualizing and Characterizing Emergent Behavior of Drone Swarm Systems with Agent-Based Modeling. In IIE Annual Conference. Proceedings (pp. 1-6). Institute of Industrial and Systems Engineers (IISE).
  • Gunaratne, C., Hatna, E., Epstein, J. M., & Garibay, I. (2023). Generating mixed patterns of residential segregation: An evolutionary approach. Journal of Artificial Societies and Social Simulation, 26(2).
  • Gürcan, Ö., Szczepanska, T., & Antosz, P. (2023, September). A Guide to Re-implementing Agent-Based Models: Experiences from the HUMAT Model. In Conference of the European Social Simulation Association (pp. 519-531). Cham: Springer Nature Switzerland.
  • Haase, K., Reinhardt, O., Lewin, W. C., Weltersbach, M. S., Strehlow, H. V., & Uhrmacher, A. M. (2023). Agent-Based Simulation Models in Fisheries Science. Reviews in Fisheries Science & Aquaculture, 1-24.
  • Haberle, I., Bavčević, L., & Klanjscek, T. Fish condition as an indicator of stock status: Insights from condition index in a food‐limiting environment. Fish and Fisheries.
  • Haddad, B. M., & Solomon, B. D. (Eds.). (2023). Dictionary of Ecological Economics: Terms for the New Millennium. Edward Elgar Publishing.
  • Haensel, M., Schmitt, T. M., & Bogenreuther, J. (2023). Teaching the Modeling of Human–Environment Systems: Acknowledging Complexity with an Agent-Based Model. Journal of Science Education and Technology, 1-11.
  • Hägglund, M. (2023). Agent Based Modelling for Simulating the Interregional Patient Mobility in Italy. CARING IS SHARING–EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION, 297.
  • Hamdi, M., & Goïta, K. (2023). Analysis of Groundwater Depletion in the Saskatchewan River Basin in Canada from Coupled SWAT-MODFLOW and Satellite Gravimetry. Hydrology, 10(9), 188.
  • Hamdi, M., & Goïta, K. (2023). Estimation of Aquifer Storativity Using 3D Geological Modeling and the Spatial Random Bagging Simulation Method: The Saskatchewan River Basin Case Study (Central Canada). Water, 15(6), 1156.
  • Hamed, N. A., & Hasson, S. T. (2023, April). A Developed Centralized Stable Clustering Approach for Vehicular Networks. In 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 867-872). IEEE.
  • Han, F., Zeng, J., Lin, J., Zhao, Y., & Gao, C. (2023). A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games. Applied Energy, 350, 121701.
  • Han, J., Tan, Q., Ji, Q., Li, Y., Liu, Y., & Wang, Y. (2023). Simulating the CCUS technology diffusion in thermal power plants: An agent-based evolutionary game model in complex networks. Journal of Cleaner Production, 421, 138515.
  • Han, N., & Liu, Z. (2023). Targeting alternative splicing in cancer immunotherapy. Frontiers in Cell and Developmental Biology, 11.
  • Han, Y. (2023). The Integration of New Media Communication and Social Network Based on Computer Technology. Media and Communication Research, 4(7), 13-21.
  • Han, Z., Mitani, Y., Kawano, K., Taniguchi, H., Honda, H., Meng, L., & Li, Z. (2023). Quantitative assessment of flooding risk based on predicted evacuation time: A case study in Joso city, Japan. International Journal of Disaster Risk Reduction, 104113.
  • Hanisch, S., Eirdosh, D., & Morgan, T. (2023). Evolving cooperation and sustainability for common pool resources. In Learning evolution through socioscientific issues (pp. 127-147). UA Editora.
  • Hanisch, S., & Eirdosh, D. (2023). Behavioral Science and Education for Sustainable Development: Towards Metacognitive Competency. Sustainability, 15(9), 7413.
  • Hartman, C. R. A. (2023). Hierarchically Embedded Social Dynamics in Vampire Bats (Doctoral dissertation, The Ohio State University).
  • Hassanpour, S., Gonzalez, V. A., Zou, Y., Liu, J., Wang, F., del Rey Castillo, E., & Cabrera-Guerrero, G. (2023). Incorporation of BIM-based probabilistic non-structural damage assessment into agent-based post-earthquake evacuation simulation. Advanced Engineering Informatics, 56, 101958.
  • Hatzis, J. J., Kim, J., & Klockow-McClain, K. E. (2024). An Agent-Based Modeling Approach to Protective Action Decision-Related Travel during Tornado Warnings. Natural Hazards Review, 25(1), 04023057.
  • Hayes, C. G. (2023). Expanding the Fisheries Management Tackle Box: A Multiple-Model Approach to Support Better Decisions (Doctoral dissertation, University of Maryland, College Park).
  • Hayes, R. (2023). Epistemic Agency in Lab: When, Why, and How Introductory College Biology Students Direct Their Own Science Investigations (Doctoral dissertation, Tufts University).
  • Hayes, R. J. (2023). Towards a Formal Theory of Humor in Organizations (Doctoral dissertation, Old Dominion University).
  • Hazlerigg, C. R., Mintram, K. S., Tyler, C. R., Weltje, L., & Thorbek, P. (2023). HARNESSING MODELLING FOR ASSESSING THE POPULATION RELEVANCE OF EXPOSURE TO ENDOCRINE ACTIVE CHEMICALS. Environmental Toxicology and Chemistry.
  • Hedger, R. D., Sundt‐Hansen, L. E., Juárez‐Gómez, A., Alfredsen, K., & Foldvik, A. (2023). Exploring sensitivities to hydropeaking in Atlantic salmon parr using individual‐based modelling. Ecohydrology, e2553.
  • Hénard, A., Rivière, J., Peillard, E., Kubicki, S., & Coppin, G. (2023). A unifying method-based classification of robot swarm spatial self-organisation behaviours. Adaptive Behavior, 10597123231163948.
  • Heppenstall, A., Polhill, J. G., Batty, M., Hare, M., Salt, D., & Milton, R. (2023). Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER)(Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
  • Heras, A., Sanchez-Enguix, V., Alberola, J. M., & Perez-Pascual, A. (2023). A BOARD GAME-BASED VIRTUAL ENVIRONMENT FOR INTELLIGENT BOTS PROGRAMMING. In INTED2023 Proceedings (pp. 3062-3068). IATED.
  • Herath, G. B., & Secchi, D. (2023). Organization-cognition fit: Supplementing or complementing team's capabilities?. In Organizational Cognition (pp. 120-144). Routledge.
  • Herberich, M. M., Gayler, S., & Tielbörger, K. (2023). Environmental heterogeneity promotes coexistence among plant life-history strategies through stabilizing mechanisms in space and time. Basic and Applied Ecology.
  • Hershkovitz, A., Bain, C., Kelter, J., Peel, A., Wu, S., Horn, M. S., & Wilensky, U. (2023). Contribution of Computational Thinking to STEM Education: High School Teachers' Perceptions after a Professional Development Program. Journal of Computers in Mathematics and Science Teaching, 42(1), 35-65.
  • Hicks, D. E. (2023). An Introduction to Complexity Pedagogy: Using Critical Theory, Critical Pedagogy and Complexity in Performance and Literature. Stylus Publishing, LLC.
  • Hipkiss, C. V. (2023). Stress in paradise: Reconstructing late holocene hydroclimate to investigate the role of drought in the timing of human migration and colonisation in the tropical South Pacific (Doctoral dissertation, University of Southampton).
  • Hu, H., & Wang, N. (2023). Dynamics of Business-IT Alignment: A Complex Adaptive System Model. PACIS 2023 Proceedings, 16.
  • Hu, W., Dong, Z., Huang, X., Gao, Y., Zhang, Z., & Hao, J. (2023, April). Photovoltaic inverter anomaly detection method based on LSTM serial depth autoencoder. In Journal of Physics: Conference Series (Vol. 2474, No. 1, p. 012026). IOP Publishing.
  • Hu, X., Yang, Z., Sun, J., & Zhang, Y. (2023). Optimal pricing strategy for electric vehicle battery swapping: Pay-per-swap or subscription?. Transportation Research Part E: Logistics and Transportation Review, 171, 103030.
  • Huang, R., Liu, G., Li, K., Liu, Z., Fu, X., & Wen, J. (2023). Evolution of residents' cooperative behavior in neighborhood renewal: An agent-based computational approach. Computers, Environment and Urban Systems, 105, 102022.
  • Huang, Y., Guo, Z., Chu, H., & Sengupta, R. (2023). Evacuation Simulation Implemented by ABM-BIM of Unity in Students’ Dormitory Based on Delay Time. ISPRS International Journal of Geo-Information, 12(4), 160.
  • Huber, M., & Karaali, G. (2023). Mathematics and Society. Journal of Humanistic Mathematics, 13(2), 1-3.
  • Huber, R., Späti, K., & Finger, R. (2023). A behavioural agent-based modelling approach for the ex-ante assessment of policies supporting precision agriculture. Ecological Economics, 212, 107936.
  • Huckins, E. (2023). Challenges and opportunities for consumers and producers in Central Iowa local food systems (Doctoral dissertation, Iowa State University).
  • Hughes, J. D., Langevin, C. D., Paulinski, S. R., Larsen, J. D., & Brakenhoff, D. (2023). FloPy Workflows for Creating and Constructing Structured and Unstructured MODFLOW 6 Models. Groundwater.
  • Hui, H., Gong, Z., An, J., & Qi, J. (2023). A dynamic Bayesian-based comprehensive trust evaluation model for dispersed computing environment. China Communications, 20(2), 278-288.
  • Hulkkonen, M., Kaaronen, R. O., Kokkola, H., Mielonen, T., Clusius, P., Xavier, C., ... & Malila, J. (2023). Modeling non-linear changes in an urban setting: From pro-environmental affordances to responses in behavior, emissions and air quality. Ambio, 52(5), 976-994.
  • Husarek, D. (2023). Analysis of sector-coupling effects between the mobility sector and the energy system under consideration of energy transport and charging infrastructure (Doctoral dissertation, Technische Universität Darmstadt).
  • Hussain, H., & de Vries, M. (2023). Extending the Meta Model for Enterprise Systems Dynamics from a Software Tooling Perspective. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) (Vol. 2, pp. 50-61). SCITEPRESS.
  • Iancu, L. D., Dragoi, P. A., & Delcea, C. (2023, June). Computer Modeling of Evacuation Patterns Comparison and Crowd Dynamics: A Use of NetLogo. In IBIMA Conference on Artificial intelligence and Machine Learning (pp. 356-367). Cham: Springer Nature Switzerland.
  • Ibrahim, S., & Maheshwari, P. (2023, March). Non-Pharmaceutical Intervention measures in the UAE–What Next?. In 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 226-231). IEEE.
  • Iii, B. Y. (2023). Combining Block-Based Programming With Robotics Kits to Support a Middle School Computing Curriculum (Doctoral dissertation, Vanderbilt University).
  • Ilagan, J. B., & Ilagan, J. R. (2023, July). Teaching Diffusion of Innovations Involving Technology Startups Using Agent-Based Simulation Modeling: Architecture and Design Considerations. In International Conference on Human-Computer Interaction (pp. 298-311). Cham: Springer Nature Switzerland.
  • Ilyas, N. A. J. I. (2023). Comparison of a posteriori error estimators. The 14th Edition of" Journées d'Analyse Numérique Optimisation, 75(256), 1659-1674.
  • Ionescu, Ș., Chiriță, N., Nica, I., & Delcea, C. (2023). An Analysis of Residual Financial Contagion in Romania’s Banking Market for Mortgage Loans. Sustainability, 15(15), 12037.
  • Isherwood, A., Koehler, M., & Slater, D. (2023, December). Using Evolutionary Model Discovery to Develop Robust Policies. In 2023 Winter Simulation Conference (WSC) (pp. 130-137). IEEE.
  • Ismael, W. K. F. A., & Ucan, O. (2023). Vanet performance evaluation in terms of nodes distribution, mobility models, and routing protocols. Technium: Romanian Journal of Applied Sciences and Technology, 8, 32-45.
  • Iswara, L., Gunawan, F. E., & Alamsjah, F. (2023, April). Assessing task difficulty, team-member ability, and motivation on the team productivity by an agent-based model. In AIP Conference Proceedings (Vol. 2594, No. 1). AIP Publishing.
  • Itahashi, K. (2023). Effeteness of Programing Education in Science Classroom—Through a Review of Studies on Programming Education. JSSE Research Report, 37(5), 29-34.
  • Ivanova, Y. A. (2023, September). Building Drawing Simulation Models for the Purposes of Industrial Design. In 2023 International Conference on Information Technologies (InfoTech) (pp. 1-4). IEEE.
  • Ivanova, Y. (2023). APPLICATIONS OF DIGITAL TRANSFORMATIONS AND SIMULATION MODELING IN AEROSPACE ENGINEERING AND SECURITY. International Journal on Information Technologies & Security, 15(3).
  • Jagutis, M., Russell, S., & Collier, R. (2023). Flexible simulation of traffic with microservices, agents & REST. International Journal of Parallel, Emergent and Distributed Systems, 1-17.
  • Jahn, L., Rendsvig, R. K., & Stærk-Østergaard, J. (2023). Detecting Coordinated Inauthentic Behavior in Likes on Social Media: Proof of Concept. arXiv preprint arXiv:2305.07350.
  • Jaimez-González, C. R., Erazo-Palacios, J., & García-Mendoza, B. (2023). BlockCode: A Web Application to Create Games that Support the Learning of Computer Programming Logic. International Journal of Emerging Technologies in Learning, 18(15).
  • Jayathilake, P., Victori, P., Pavillet, C., Voukantsis, D., Miar Cuervo, A., Arora, A., ... & Buffa, F. M. (2023). Metabolic Symbiosis between Oxygenated and Hypoxic Tumour Cells: An Agent-based Modelling Study. bioRxiv, 2023-07.
  • JE, O. (2023). SIX TIPS FOR BETTER CODING WITH CHATGPT. Nature, 618.
  • Jen, T., Brady, C. E., Vogelstein, L., & Ayalon, E. (2023). Designing for Feelings: Disruptive Beginnings in Youths’ Designs of Mixed Reality Activities for Sustainability. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 950-953. International Society of the Learning Sciences.
  • Jevtić, M., Mladenović, S., & Granić, A. (2023). Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps. Applied Sciences, 13(9), 5719.
  • Ji, J. (2023, October). Construction of public policy decision-making model based on big data analysis from the perspective of sustainable development. In Second International Conference on Sustainable Technology and Management (ICSTM 2023) (Vol. 12804, pp. 434-441). SPIE.
  • Jialin, L. I. U., Xianyu, Z. H. A. N. G., & Da, P. A. N. G. (2023). Research progress of adaptive therapy strategy based on Darwinian dynamics in tumor therapy. China Oncology, 33(4), 397-402.
  • Jiang, F., & Chen, Z. (2023, May). Behavior Simulation of Disturbed UAV Based on NetLogo. In 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP) (pp. 1143-1147). IEEE.
  • Jiang, J., & Sun, R. (2023, April). Research on the opinion evolution process under the effect of time delay. In Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) (Vol. 12610, pp. 1499-1506). SPIE.
  • Jiang, J., & Sun, R. (2023, June). Model of the evolution of opinion propagation under the effect of group pressure. In International Conference on Pure, Applied, and Computational Mathematics (PACM 2023) (Vol. 12725, pp. 8-16). SPIE.
  • Jiang, X., Jia, R., & Yang, L. (2023). Assessing the economic ripple effect of flood disasters in light of the recovery process: Insights from an agent‐based model. Risk analysis.
  • Jie, Y. A. N. G. Assessment of train passengers’ pre-and post-boarding behaviors and insights into platform and carriage design, preference, and satisfaction (Doctoral dissertation, RMIT University).
  • Jin, C. (2023). A dynamic management mechanism for object cooperation in the social Internet of Things (Doctoral dissertation, University of Manchester).
  • Jin, H., Zhang, S., Zhang, B., Dong, S., Liu, X., Zhang, H., & Tan, J. (2023). Evolutionary game decision-making method for network attack and defense based on regret minimization algorithm. Journal of King Saud University-Computer and Information Sciences, 35(3), 292-302.
  • Jing, K. (2023). Design of control algorithms for false information dissemination in smart city social media based on symmetric triangle interval. Soft Computing, 1-15.
  • Johnson, M., Lewis, T., Martin, K., Cesare, A., Schmitt, A., Lengerich, E., ... & Divyak, C. (2023). The Science of COVID-19. The Science Teacher, 90(3).
  • Jones, J. M (2023). A processual theory of strategic consensus (Doctoral dissertation, University of Georgia).
  • Jørgensen, B. N., da Silva, L. C. P., & Ma, Z. (Eds.). (2023). Energy Informatics: Third Energy Informatics Academy Conference, EI. A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part II (Vol. 14468). Springer Nature.
  • Jünger, J., & Gärtner, C. (2023). Simulationsverfahren. In Computational Methods für die Sozial-und Geisteswissenschaften (pp. 423-434). Wiesbaden: Springer Fachmedien Wiesbaden.
  • Júnior, A. O., Calvo-Rolle, J. L., & Leitão, P. (2023, April). Artificial Intelligence Data-Driven Petri nets Approach for Virtualizing Digital Twins. In 2023 IEEE International Conference on Industrial Technology (ICIT) (pp. 1-6). IEEE.
  • Käfer, T., Charpenay, V., & Harth, A. (2023). BOLD: A Benchmark for Linked Data User Agents and a Simulation Framework for Dynamic Linked Data Environments. arXiv preprint arXiv:2307.09114.
  • Kaimal, A. M., & Singhal, R. S. (2023). Bigels for controlled gastric release of ascorbic acid: Impact on rheology, texture, thermal stability and antioxidant activity. Food Hydrocolloids for Health, 100171.
  • Karalidis, K., Roumpos, C., Servou, A., Paraskevis, N., & Pavloudakis, F. (2023). A Scenario-Based Analysis for the Selection of Post-Mining Land Uses Applying a Cellular Automata Model. Materials Proceedings, 15(1), 4.
  • Karimian, H., Fan, Q., Li, Q., Chen, Y., & Shi, J. (2023). Spatiotemporal transmission of infectious nanochemical particles in water environment: A case study of Covid-19. Chemosphere, 139065.
  • Karutz, R., Klassert, C. J., & Kabisch, S. (2023). On Farmland and Floodplains—Modeling Urban Growth Impacts Based on Global Population Scenarios in Pune, India. Land, 12(5), 1051.
  • Katalevsky, D. (2023). New Governance Approaches to Prevent the Collapse of Complex Socioeconomic Systems. Foresight and STI Governance, 17(3), 56-67.
  • Kavitha, P., & Latha, S. D (2023). Genetic fuzzy logic algorithm as intelligent agents for swarm intelligence application. ICTACT Journal on Soft Computing, 14(3), 3243-48.
  • Ke, L., Kirk, E., Lesnefsky, R., & Sadler, T. D. (2023). Exploring system dynamics of complex societal issues through socio-scientific models. In Frontiers in Education (Vol. 8). Frontiers Media SA.
  • Kelter, J., Wilensky, U., & Potvin, J. (2023). Introducing Land Constraints to Macroeconomic Agent-Based Models. In Z. Yang & S. Núñez-Corrales (Eds.), Proceedings of the 2022 Conference of The Computational Social Science Society of the Americas (pp. 35–48). Springer International Publishing. https://doi.org/10.1007/978-3-031-37553-8_3
  • Khaddage, F., Lattemann, C., & Gebbing, P. (2023, February). Towards User2Machine Model for Higher Education-Enforced by Covid19 Pandemic. In Learning in the Age of Digital and Green Transition: Proceedings of the 25th International Conference on Interactive Collaborative Learning (ICL2022), Volume 2 (pp. 163-171). Cham: Springer International Publishing.
  • Khair, F., Wijaya, D. I., & Yulianto, H. D. (2023, August). Basic model simulation for disaster evacuation routes evaluation using agent based modeling (ABM). In AIP Conference Proceedings (Vol. 2485, No. 1). AIP Publishing.
  • Khajouei, M. S. H., Pilevari, N., Radfar, R., & Mohtashami, A. (2023). Iran’s Steel Chain Interactive Network Mapping. International Journal of ISSI.
  • Khan, J. R., Siddiqui, F. A., Rizwan, W., & Hamid, I. (2023). State of the Art Systematic Literature Review on Selection of Top Ten WSN Simulators–Part A. Journal of Independent Studies and Research Computing, 21(2), 42-49.
  • Khan, Z., Koubaa, A., Benjdira, B., & Boulila, W. (2023). A game theory approach for smart traffic management. Computers and Electrical Engineering, 110, 108825.
  • Khound, P., Will, P., Tordeux, A., & Gronwald, F. (2023). The Over-Damped String Stability Condition for a Platooning System. System Theory, Control and Computing Journal, 3(1), 12-19.
  • Kiel, L. (2023). The Importance of Replication In Uncertain Epistemic Landscapes (Master's thesis).
  • Kim, G., & Heo, G. (2023). Agent-based radiological emergency evacuation simulation modeling considering mitigation infrastructures. Reliability Engineering & System Safety, 109098.
  • Kim, Y. J., & Wang, J. (2023). Nondestructive Testing of Bridge Decks: Case Study and Suggestions. ACI Structural Journal, 120(2).
  • Kleiner, G., Rybachuk, M., & Ushakov, D. (2023, July). Behavioral Model of Interaction Between Economic Agents and the Institutional Environment. In Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies: Second International Conference, MSBC 2022, Vilnius, Lithuania, September 21–23, 2022, Proceedings (p. 48). Springer Nature.
  • KLINEC, M. (2023). Agentno modeliranje dinamike tropov volkov v okolju NetLogo (Doctoral dissertation, Univerza v Ljubljani, Fakulteta za računalništvo in informatiko).
  • Knepper, H. J., Evans, M. D., & Henley, T. J. (Eds.). (2023). Intersectionality and Crisis Management: A Path to Social Equity. Taylor & Francis.
  • Ko, C., Cho, W., Hwang, B., Chang, B., Kang, W., & Ko, D. W. (2023). Simulating Hunting Effects on the Wild Boar Population and African Swine Fever Expansion Using Agent-Based Modeling. Animals, 13(2), 298.
  • Koch, J., & De Schamphelaere, K. A. (2023). Investigating population level toxicity of the antidepressant citalopram in harpacticoid copepods using in vivo methods and bioenergetics‐based population modeling. Environmental Toxicology and Chemistry.
  • Koenig, L. J., Khurana, N., Islam, M. H., Gopalappa, C., & Farnham, P. G. (2023). Closing the gaps in the continuum of depression care for persons with HIV: modeling the impact on viral suppression in the United States. AIDS, 37(7), 1147-1156.
  • Koide, R., Yamamoto, H., Nansai, K., & Murakami, S. (2023). Agent-based model for assessment of multiple circular economy strategies: Quantifying product-service diffusion, circularity, and sustainability. Resources, Conservation and Recycling, 199, 107216.
  • Kolligs, T. (2023). The Slow Spread of Environmentally Friendly Action: An agent-based model simulation of social networks (Master's thesis, Stockholm University).
  • Kolodner, J. L. (2023). Learning engineering: What it is, why I’m involved, and why I think more of you should be. Journal of the Learning Sciences, 32(2), 305-323.
  • Kong, L., Wang, L., Cao, Z., & Wang, X. (2023). Resilience evaluation of UAV swarm considering resource supplementation. Reliability Engineering & System Safety, 109673.
  • Kortsch, S., Saravia, L., Cirtwill, A. R., Timberlake, T., Memmott, J., Kendall, L., ... & Strona, G. (2023). Landscape composition and pollinator traits interact to influence pollination success in an individual‐based model. Functional Ecology.
  • Köstler, V. (2023). Zwischen Präzision und Sensitivität: Generierung eines Studienkorpus am Beispiel einer Fragestellung zu Künstlicher Intelligenz (KI) in Bildungsprozessen. MedienPädagogik: Zeitschrift für Theorie und Praxis der Medienbildung, 54, 1-27.
  • Kousar, H., Fatima, S., Ahmed, S. I., Sajithra, S., Kushwaha, S., & Balaji, N. A. (2023, September). AI Based Security for Internet of Transportation Systems. In 2023 4th International Conference on Smart Electronics and Communication (ICOSEC) (pp. 701-708). IEEE.
  • Kout, A., Bouaita, B., Beghriche, A., Labed, S., Chikhi, S., & Bourennane, E. B. (2023). A Hybrid Optimization Solution for UAV Network Routing. Engineering, Technology & Applied Science Research, 13(2), 10270-10278.
  • Kumar, P., Raglin, A., & Richardson, J. (2023, July). General Agent Theory of Mind: Preliminary Investigations and Vision. In International Conference on Human-Computer Interaction (pp. 504-515). Cham: Springer Nature Switzerland.
  • Kumar, R., & Agrawal, N. (2023). Analysis of multi-dimensional Industrial IoT (IIoT) data in Edge-Fog-Cloud based architectural frameworks: A survey on current state and research challenges. Journal of Industrial Information Integration, 100504.
  • Kumari, S. (2023). Trust Management in Social Internet of Things: Challenges and Future Directions. International Journal of Computing and Digital Systems, 14(1), 1-xx.
  • Kunz, N., Chesney, T., Trautrims, A., & Gold, S. (2023). Adoption and transferability of joint interventions to fight modern slavery in food supply chains. International Journal of Production Economics, 258, 108809.
  • Kuroswiski, A. R., Medeiros, F. L. L., De Marchi, M. M., & Passaro, A. (2023). Beyond visual range air combat simulations: validation methods and analysis using agent-based models. The Journal of Defense Modeling and Simulation, 15485129231211915.
  • Kusumah, H., & Wasesa, M. (2023). Unraveling the Most Influential Determinants of Residential Segregation in Jakarta: A Spatial Agent-Based Modeling and Simulation Approach. Systems, 11(1), 20.
  • Lang, D., & Ertsen, M. W. (2023). Modelling farmland dynamics in response to farmer decisions using an advanced irrigation-related agent-based model. Ecological Modelling, 486, 110535.
  • Langbeheim, E., Ben-Hamo, S., Weintraub, G., & Shapira, S. (2023, April). Reasoning about crowd evacuations as emergent phenomena when using participatory computational models. In Frontiers in Education (Vol. 8, p. 1137828). Frontiers.
  • Larsson, A., & Große, C. (2023). Data use and data needs in critical infrastructure risk analysis. Journal of Risk Research, 26(5), 524-546.
  • Le, H. (2023). Automated Discovery of Candidate Simulation Models for Steering Behavior Simulation (Doctoral dissertation, Georgia State University).
  • Le, N. T. T. (2023). Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways. Journal of Information and Telecommunication, 1-15.
  • Le Pira, M., Marcucci, E., Gatta, V., Ignaccolo, M., & Inturri, G. (2023). 16. Participatory decision-support tools for stakeholder engagement in urban freight transport policy making. Handbook on City Logistics and Urban Freight: 0, 327.
  • Ledder, G. (2023). Modeling in Biology. In Mathematical Modeling for Epidemiology and Ecology (pp. 3-44). Cham: Springer International Publishing.
  • Lee, I. P. A., Eldakar, O. T., Gogarten, J. P., & Andam, C. P. (2023). Protocol for an agent-based model of recombination in bacteria playing a public goods game. STAR protocols, 4(4), 102733.
  • Lee, I. P. A., Eldakar, O. T., Gogarten, J. P., & Andam, C. P. (2023). Recombination as an enforcement mechanism of prosocial behavior in cooperating bacteria. iScience.
  • Lei, H., Wang, H., Wang, L., Dong, Y., Cheng, J., & Cai, K. (2023). An Analysis of the Evolution of Online Public Opinion on Public Health Emergencies by Combining CNN-BiLSTM+ Attention and LDA. Journal of Computer and Communications, 11(4), 190-199.
  • León, J., Ogueda, A., Gubler, A., Catalán, P., Correa, M., Castañeda, J., & Beninati, G. (2023). Increasing resilience to catastrophic near-field tsunamis: systems for capturing, modelling, and assessing vertical evacuation practices. Natural Hazards, 1-27.
  • Leonard-Duke, J., Bonham, C., & Peirce, S. M. (2023). Low Vascular Endothelial Growth Factor Levels and Increasing Environmental Stiffness Potentiate Vascular Hyper-remodeling in Computational Model of Idiopathic Pulmonary Fibrosis. In A65. THE ALVEOLAR NICHE IN THE SPOTLIGHT OF LUNG FIBROSIS (pp. A2190-A2190). American Thoracic Society.
  • Lessnau, T. A. (2023). An agent-based model of the canadian housing market (Doctoral dissertation, Université du Québec en Outaouais).
  • Lestari, D. P., Kosasih, R., Sari, I., & Amalia, A. (2023, August). Fire detection system on surveillance videos using faster region-based convolutional neural network for high buildings evacuation. In AIP Conference Proceedings (Vol. 2431, No. 1). AIP Publishing.
  • Lestari, D. P., Putra, H. D., & Sari, I. (2023, July). Prototype of evacuation management system on high rise building using stochastic dynamical optimization system. In AIP Conference Proceedings (Vol. 2689, No. 1). AIP Publishing.
  • Li, D., Yang, J., Li, J., Zhao, N., Ju, W., & Guo, M. (2023). Agent-Based Modeling and Simulation (ABMS) on the influence of adjusting medical service fees on patients' choice of medical treatment. BMC Health Services Research, 23(1), 928.
  • Li, H., & Zhang, H. (2023). Cost-effectiveness analysis of COVID-19 screening strategy under China's dynamic zero-case policy. Frontiers in Public Health, 11, 1099116.
  • Li, J., Wan, Q., & Yu, Z. (2023, November). Research on network security algorithms in distributed computing environment. In International Conference on Internet of Things and Machine Learning (IoTML 2023) (Vol. 12937, pp. 82-87). SPIE.
  • Li, L., Yuwen, Z., Zhu, J., Li, P., Duan, M., & Guo, X. Decision Support for Regeneration Mode of Old Community Under Multi-Agent Interaction: Evidence from China. Available at SSRN 4570595.
  • Li, P. (2023). 基于统计分析方法的网红直播带货效果影响因素研究 (Doctoral dissertation, Arizona State University).
  • Li, W., Lv, M., Hao, J., Chen, J., & Yu, K. Dynamic Simulation and Control Strategy Exploration of Unsafe Behavior of Coal Mine Staff. Maoyun and Hao, Jian and Chen, Jing and Yu, Kai, Dynamic Simulation and Control Strategy Exploration of Unsafe Behavior of Coal Mine Staff.
  • Li, W., Zhang, Q., Deng, S., Zhou, B., Wang, B., & Cao, J. (2023). Q-Learning Improved Lightweight Consensus Algorithm for Blockchain-structured Internet of Things. IEEE Internet of Things Journal.
  • Li, W., Zhou, L., Hao, J., Yu, K., Chen, J., Liu, P., & Feng, R. (2023). Dynamic simulation and control strategy exploration of the unsafe behavior of coal mine employees. Resources Policy, 86, 104067.
  • Li, X., Xu, D., Ding, C., Lu, W., Wang, M., Yan, W., ... & Li, Y. (2023). Comparative analysis of domestic and foreign coal mine safety supervision modes based on knowledge map. Environmental Science and Pollution Research, 1-13.
  • Li, Y., Yang, M., & Zhang, S. (2023). Study on the influence diffusion of SMEs in open-source communities from the perspective of complex networks. Mathematical Biosciences and Engineering, 20(7), 12731-12749.
  • Lian, C., Liu, J., & Wang, J. (2023, May). Resource Support for “Mobilization–Participation” in Public Health Emergencies Based on a Complex Network Evolutionary Game. In Healthcare (Vol. 11, No. 10, p. 1506). MDPI.
  • Lian, C., & Wang, J. (2023). Multi-actor cooperation for emergency supply support: a simulation of behavior diffusion based on social networks. Natural Hazards, 1-22.
  • Liang, Z., Várady, G., & Zagorácz, M. B. (2023). Sustainable Application of Automatically Generated Multi-Agent System Model in Urban Renewal. Sustainability, 15(9), 7308.
  • Liao, H., Holguín-Veras, J., & Calderón, O. (2023). Comparative analysis of the performance of humanitarian logistic structures using agent-based simulation. Socio-Economic Planning Sciences, 101751.
  • Liao, R., Liu, W., & Yuan, Y. (2023). Resilience Improvement and Risk Management of Multimodal Transport Logistics in the Post–COVID-19 Era: The Case of TIR-Based Sea–Road Multimodal Transport Logistics. Sustainability, 15(7), 6041.
  • Lienhard, D. Z. (2023). Reducing Abortion Rates Without Restricting Legal Access to Abortion: Evidence From Comparative Analysis of Relevant Policies and Demographic Indicators in 15 Post-Soviet Countries and Adaptive Agent-Based Modeling of Unintended Pregnancies (Doctoral dissertation, Arizona State University).
  • Lili, A., Tian, X., Wenbin, Y., & Xinbo, W. (2023). Modeling and Simulation of Spaceborne, Near-Spaceborne, and Airborne Integrated Collaborative Remote Sensing System Based on DoDAF. Journal of System Simulation, 35(5), 936.
  • Lima, H. P. D. Formigas de correição do gênero Eciton (Latreille, 1804): interação predador presa, forrageio e nidificação (Doctoral dissertation, Universidade de São Paulo).
  • Lin, C. Y. (2023). Co-evolution in Complex Adaptive Water Systems from Long-Term Planning to Short-Term Responses (Doctoral dissertation, Lehigh University).
  • Ling, B., Raynor, E. J., Joern, A., & Goodin, D. G. (2023). Dynamic Plant–Herbivore Interactions between Bison Space Use and Vegetation Heterogeneity in a Tallgrass Prairie. Remote Sensing, 15(22), 5269.
  • Liu, C., Liu, S., Zhang, J., Wang, L., Guo, X., Li, G., & Wang, W. (2023). An optimal design method of emergency evacuation space in the high-density community after earthquake based on evacuation simulation. Natural Hazards, 1-27.
  • Liu, H., & Gu, X. (2023). Leveraging AI, big data and educational technology to promote collaborative learning and improve cyberlearning courses: synopsis and linked presentations of the workshop at Orlando, Florida, 4-6 June 2019, and the online workshop, 13-14 August 2020. International Journal of Smart Technology and Learning, 3(2), 118-137.
  • Liu, M., & Liu, T. (2023). An Agent-Based Approach to Adaptive Design Based on Influences Mediated by Artifacts. In International Conference on-Design Computing and Cognition (pp. 605-625). Springer, Cham.
  • Liu, S., & Li, Y. (2023). Dynamic Simulation Study on Evolution Law and Intervention Strategy of Public Opinion Information of Major Public Health Events. Advances in Applied Sociology, 13(5), 422-440.
  • Liu, Y. (2023). Application: Simulation Model of Pedestrian Flows in the Re-design of Built Environment Around Metro Stations. In Built Environment and Walking & Cycling Around Metro Stations (pp. 97-121). Singapore: Springer Nature Singapore.
  • Liu, Y., Song, D., Wang, Z., Yu, X., & Wang, R. (2023, July). Walkability Assessment Using Agent-Based Model: Why It Becomes An Advantageous Way. In World Congress of Architects (pp. 367-374). Cham: Springer International Publishing.
  • Locatelli, M., Pellegrini, L., Accardo, D., Sulis, E., Tagliabue, L. C., & DI GIUDA, G. M. (2023, September). People flow management in a healthcare facility through crowd simulation and agent-based modeling methods. In JOURNAL OF PHYSICS. CONFERENCE SERIES (pp. 1-6).
  • Lomos, C., Luyten, J. W., & Tieck, S. (2023). Implementing ICT in classroom practice: what else matters besides the ICT infrastructure?. Large-scale Assessments in Education, 11(1), 1-28.
  • Long, Y., Yang, C., Li, X., Lu, W., Zhang, Q., & Gao, J. (2023). Forecasting law enforcement frequency of internet+ coal mine safety supervision. International Journal of Energy Sector Management.
  • Lorente, P. J., & Pereda, M. (2023, March). An Iterated Prisoner’s Dilemma Tool to Play and Learn Inside and Outside the Class. In IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización (pp. 59-63). Cham: Springer International Publishing.
  • Lorig, F., Vanhée, L., & Dignum, F. (2023). Agent-Based Social Simulation for Policy Making. In Human-Centered Artificial Intelligence: Advanced Lectures (pp. 391-414). Cham: Springer International Publishing.
  • Lou, J., Borjigin, S., Tang, C., Saadat, Y., Hu, M., & Niemeier, D. A. (2023). Facility design and worker justice: COVID‐19 transmission in meatpacking plants. American Journal of Industrial Medicine.
  • Lu, J., Wang, C., Li, J., Li, X., Zhao, J., & Wang, X. (2023, August). Trust evaluation model in new power system edge computing. In International Conference on Optoelectronic Information and Functional Materials (OIFM 2023) (Vol. 12781, pp. 8-16). SPIE.
  • Lu, P., Li, Y., Wen, F., & Chen, D. (2023). Agent-based modeling of mass shooting case with the counterforce of policemen. Complex & Intelligent Systems, 1-21.
  • Lu, P., Zhang, Z., & Li, M. (2023). Individual heights and phase transition under crowd emergencies: Agent-based modeling from 2 to 3D. Artificial Intelligence Review, 1-23.
  • Lu, P., Zhang, Z., Onyebuchi, C. H., & Zheng, L. (2024). Agent-based modeling of high-rise building fires reveals self-rescue behaviors and better fire protection designs. Engineering Applications of Artificial Intelligence, 127, 107401.
  • Lu, Q., & Hua, J. (2023). Micro-Household Human Capital Investment Decisions and a Simulation Study from the Intergenerational Conflict Perspective. International Journal of Environmental Research and Public Health, 20(3), 1696.
  • Lu, Y., Ou, D., Zhou, Z., Li, H., Deng, Y., Deng, Y., & Zhang, Z. (2023). Simulation analysis of passengers’ rescheduling strategies in metro station under COVID-19. Tunnelling and Underground Space Technology, 134, 105023.
  • Lucas, P., & Feliciani, T. (2023). Investigating social phenomena with agent-based models. In Research Handbook on Digital Sociology (pp. 146-160). Edward Elgar Publishing.
  • Luckner, K., & Fikfak, V. (2023). Not all nations at all times: How States Imitate Each Other’s Behavior Towards Non-Compliance with International Law Norms: an ABM proposal. In iCourts Working Paper 318; Proceedings of the AMPM-Workshop@JURIX2022.
  • Luo, F., Ijeluola, S. A., Westerlund, J., Walker, A., Denham, A., Walker, J., & Young, C. (2023). Supporting Elementary Teachers’ Technological, Pedagogical, and Content Knowledge in Computational Thinking Integration. Journal of Science Education and Technology, 1-14.
  • Lynch, A. R., Bradford, S., Zhou, A. S., Oxendine, K., Henderson, L., Horner, V. L., ... & Burkard, M. E. (2023). A survey of CIN measures across mechanistic models. bioRxiv, 2023-06.
  • Lyu, G., & Brennan, R. W. (2024). Evaluating a self-manageable architecture for industrial automation systems. Robotics and Computer-Integrated Manufacturing, 85, 102627.
  • Ma, J., & Xiao, C. (2023). Large-scale fire spread model for traditional Chinese building communities. Journal of Building Engineering, 105899.
  • Ma, J., Xu, S., Zhang, L., Li, Z., Qian, Z., & Cao, K. Y. (2023). Study on the Evolution Mechanism of Lane Change Decision in Urban Expressway Diversion Area. Tehnički vjesnik, 30(5), 1503-1516.
  • Ma, Y., Wei, X., Zhao, H., Zhao, D., Wang, S., Han, T., ... & Gao, K. (2023). UAV-based emergency treatment plan for flood disasters at the Hongyanhe nuclear power plant. Ecological Indicators, 154, 110676.
  • Ma, J., Zhao, J., Sun, G., & Peng, S. (2023, December). ANYLOGIC-Based Dynamics Model of the Bass New Product Diffusion Process. In Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023) (Vol. 19, p. 26). Springer Nature.
  • Mabey, C. S., Peiffer, E. E., MacCarty, N., & Mattson, C. A. (2023). Simulating the Adoption and Social Impact of Improved Cookstoves in Uganda Using Agent-Based Modeling and Neural Networks. Journal of Mechanical Design, 145(12).
  • Madhavi, M., Kusumaniswari, V., Srinivas, G. S. H., Nayak, T. P. K., & Shivaji, M. (2023, March). Land Development Interface. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 314-319). IEEE.
  • Maghaydah, S., Maheshwari, P., & Alomari, K. M. (2023, March). Agent-Based Modelling and Simulation of Crowd Evacuation: Case Study for Electric Train Cabin. In 2023 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1-7). IEEE.
  • Maheshwari, P. (2023). Agent-Based Simulation for University Students Admission: Medical Colleges in Jordan Universities. Computer Science & Engineering: An International Journal (CSEIJ), 13(1).
  • Maheshwari, P., & Devi, Y. (2023). Investigating the relationship between Lean Six Sigma performance strategy with digital twin modeling: Practices and factors. Journal of Cleaner Production, 140449.
  • MAKSIMYCHEV, O., MEZENTSEV, K., & VOLOSOVA, A. (2023). INFORMATION AND COMMUNICATION TECHNOLOGIES AND ELEMENTS OF ARTIFICIAL INTELLIGENCE IN INTELLIGENT TRANSPORT SYSTEMS. Мир транспорта и технологических машин, 3390, 62.
  • Malanson, G. P., Testolin, R., Pansing, E. R., & Jiménez‐Alfaro, B. (2023). Area, environmental heterogeneity, scale and the conservation of alpine diversity. Journal of biogeography, 50(4), 743-754.
  • Malik, J., Putra, H. C., Sun, K., & Hong, T. (2023, May). On the applicability of various levels of detail for occupant behavior representation and modeling in building performance simulation. In Building Simulation (pp. 1-18). Beijing: Tsinghua University Press.
  • Malik, S., Khan, M. A., El-Sayed, H., & Khan, M. J. (2023). Should Autonomous Vehicles Collaborate in a Complex Urban Environment or Not?. Smart Cities, 6(5), 2447-2483.
  • Manju, T., Rehman, S., & Sankar, M. G. A. (2023). Multi Domain Optical Network Routing Using OBGP and OSPF. International Journal for Recent Developments in Science & Technology, 7(2), 146-152.
  • Mannone, M., Seidita, V., & Chella, A. (2023). Modeling and designing a robotic swarm: A quantum computing approach. Swarm and Evolutionary Computation, 101297.
  • Manzi, D. (2023). THE RESILIENCE OF CRIMINAL NETWORKS: AN AGENT-BASED SIMULATION ASSESSING DRUG TRAFFICKING ORGANIZATIONS REACTIONS TO LAW ENFORCEMENT ATTEMPTS AT DISRUPTION (Doctoral dissertation, Università Cattolica del Sacro Cuore).
  • Marah, H., & Challenger, M. (2023). An Architecture for Intelligent Agent-Based Digital Twin for Cyber-Physical Systems. In Digital Twin Driven Intelligent Systems and Emerging Metaverse (pp. 65-99). Singapore: Springer Nature Singapore.
  • Marchand, G. C., & Hilpert, J. C. (2024). Contributions of complex systems approaches, perspectives, models, and methods in educational psychology. In Handbook of educational psychology (pp. 139-161). Routledge.
  • Marin Gutierrez, D., Vázquez Salceda, J., Álvarez Napagao, S., & Gnatyshak, D. (2023). Adding preferences and moral values in an agent-based simulation framework for high-performance computing. In International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS 2023: papers accepted for presentation.
  • Marron, A., Cohen, I. R., Frankel, G., Harel, D., & Szekely, S. (2023). Challenges in Modeling and Unmodeling Emergence, Rule Composition, and Networked Interactions in Complex Reactive Systems. In MODELSWARD (pp. 202-209).
  • Marsteller, R. B., & Bodzin, A. M. (2023). GIVING ONLINE LEARNING THE PERSONAL TOUCH. Teaching and Learning Online: Science for Secondary Grade Levels, 107.
  • Martens, Chris, Alexander Card, Henry Crain, and Asha Khatri. "Modeling Game Mechanics with Ceptre." IEEE Transactions on Games (2023).
  • Martí, P., Jordán, J., & Julian, V. (2023). A flexible approach for demand-responsive public transport in rural areas. Computer Science and Information Systems, (00), 74-74.
  • Marwal, A., & Silva, E. A. (2023). City affordability and residential location choice: A demonstration using agent based model. Habitat International, 136, 102816.
  • Masa, R. C., Barca, M. Á. M., & Baquedano, E. P. (2023). Desarrollo de un modelo híbrido para reproducir el crecimiento de la placa de ateroma. Jornada de Jóvenes Investigadores e Investigadoras del I3A, 11.
  • Mateos, A., Hölzchen, E., & Rodríguez, J. (2023). Sabretooths, giant hyenas, and hominins: Shifts in the niche of scavengers in Iberia at the Epivillafranchian-Galerian transition. Palaeogeography, Palaeoclimatology, Palaeoecology, 111926.
  • Mattia, S., Paolo, M., & Stefano, N. (2023). Virtual earth cloud: a multi-cloud framework for enabling geosciences digital ecosystems. International Journal of Digital Earth, 16(1), 43-65.
  • Maupin, C. K., Mohan, G., Choudhury, A., Deepak, P., & Jin, F. (2023). Network-based approaches to leadership: An organizing framework, review, and recommendations. The Leadership Quarterly, 101753.
  • Mayerhoffer, D. M. (2023). One Model, Multiple Stories? Using Agent-Based Models to Unveil Structural Similarities in a Complex World (Doctoral dissertation, Otto-Friedrich-Universität Bamberg, Fakultät Sozial-und Wirtschaftswissenschaften).
  • McClure, C. J., & Rolek, B. W. (2023). Pitfalls arising from site selection bias in population monitoring defy simple heuristics. Methods in Ecology and Evolution, 14(6), 1489-1499.
  • McCue-Weil, L., Knight, M., Driscoll, M., Jenkins, P., & Sorensen, J. (2023). A Case Study on the Practical Use of Low-Fidelity Modeling to Mitigate the Spread of COVID-19 Amongst the Underserved Farmworker Community. La Matematica, 1-19.
  • McCune, J. CBai, Y. (2023). Research on Civil Engineering Cost Prediction Based on Decision Tree Algorithm. Academic Journal of Architecture and Geotechnical Engineering, 5(1), 39-44.. (2023). Computational Concepts, Practices, and Perspectives in K-8 Computer Science Teaching Readiness (Doctoral dissertation, Grand Canyon University).
  • McElhaney, K. W., Basu, S., McBride, E., Hutchins, N., & Biswas, G. (2023). Design and Implementation of a Week-long, High School Curriculum Unit Integrating Physics and Computational Modeling. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 497-504. International Society of the Learning Sciences.
  • McEligot, K. (2023). Integrated Coastal Flood Mitigation Policies for Myrtle Beach, South Carolina Utilizing a Federated Hurricane Mitigation Modeling Framework (Doctoral dissertation, George Mason University).
  • McGough, A., Kavak, H., & Mahabir, R. (2023). Is more always better? Unveiling the impact of contributor dynamics on collaborative mapping. Computational and Mathematical Organization Theory, 1-14.
  • McNeill, G., Sondag, M., Powell, S., Asplin, P., Turkay, C., Moller, F., & Archambault, D. (2023, April). From Asymptomatics to Zombies: Visualization-Based Education of Disease Modeling for Children. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-17).
  • Mellmann, H., Taliaronak, V., & Hafner, V. V. (2023, May). Towards an Anticipatory Mechanism for Complex Decisions in a Bio-Hybrid Beehive. In Concurrency, Specification and Programming: Revised Selected Papers from the 29th International Workshop on Concurrency, Specification and Programming (CS&P'21), Berlin, Germany (pp. 145-173). Cham: Springer International Publishing.
  • Men, F., Wang, M., & Dong, F. (2023, August). Research on Simulation Technology of Intelligent Manufacturing Scene Operation Mechanism Based on Evolutionary Game Theory. In Journal of Physics: Conference Series (Vol. 2562, No. 1, p. 012064). IOP Publishing.
  • Menzemer, L. W., Ronchi, E., Karsten, M. M. V., Gwynne, S., & Frederiksen, J. (2023). A scoping review and bibliometric analysis of methods for fire evacuation training in buildings. Fire Safety Journal, 103742.
  • Meyer, D., & Yon, G. G. V. (2023). epiworldR: Fast Agent-Based Epi Models. Journal of Open Source Software, 8(90), 5781.
  • Meza, A., Ari, I., Al Sada, M., & Koç, M. (2023). Relevance and potential of the Arctic Sea Routes on the LNG trade. Energy Strategy Reviews, 50, 101174.
  • Mi, J., Yao, C., Zhao, X., & Li, F. (2023). Research on the Diffusion Mechanism of Green Technology Innovation Based on Enterprise Perception. Computational Economics, 1-30.
  • Mi, J., Yao, C., Zhao, X., & Li, F. (2023). The impact of innovation evolution and interaction control on interfirm network performance. Technology Analysis & Strategic Management, 1-17.
  • Miao, H., Zhang, G., Yu, P., Shi, C., & Zheng, J. (2023). Dynamic Dose-Based Emergency Evacuation Model for Enhancing Nuclear Power Plant Emergency Response Strategies. Energies, 16(17), 6338.
  • Minggang, Y., Yanjie, N., Xueda, L., Dongge, Z., Peng, Z., Ming, H., & Ling, L. (2023). Adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game. Journal of Systems Engineering and Electronics.
  • Miranda, L., Baggio, J., & Ozmen Garibay, O. (2023). Evolutionary model discovery of human behavioral factors driving decision-making in irrigation experiments. JASSS: Journal of Artificial Societies and Social Simulation, 26(2).
  • Miszczak, J.A., Rule switching mechanisms in the Game of Life with synchronous and asynchronous updating policy, Physica Scripta, 98,115210 (2023). arXiv:2310.05979 DOI:10.1088/1402-4896/acfc6c
  • Mitcham, J. (2023). Agent-Based Simulation of Police Funding Tradeoffs Through the Lens of Legitimacy and Hardship. Journal of Artificial Societies and Social Simulation, 26(3).
  • Mitcham, J. (2023). Simulation Modeling for Robust and Just Public Policy Decision-Making (Doctoral dissertation, University of Massachusetts Boston).
  • Mittal, S., Wittman, R. L., Gibson, J., Huffman, J., & Miller, H. (2023). Providing a User Extensible Service-Enabled Multi-Fidelity Hybrid Cloud-Deployable SoS Test and Evaluation (T&E) Infrastructure: Application of Modeling and Simulation (M&S) as a Service (MSaaS). Information, 14(10), 528.
  • Miyazaki, S. (2023). Heterodox modeling: practicing well-tuned provisioning or commoning with networked multi-agent environments. Review of Evolutionary Political Economy, 1-14.
  • Mobinizadeh, M., Mohammadshahi, M., Aboee, P., Fakoorfard, Z., Olyaeemanesh, A., & Mohamadi, E. (2023). The Application of System Simulation in the Health Sector: A Rapid Review. Decision Making in Healthcare Systems, 513, 11.
  • Modu, B., Polovina, N., & Konur, S. (2023). Agent-Based Modeling of Malaria Transmission. IEEE Access, 11, 19794-19808.
  • Moghaddam, R. M., & Aghazadeh, N. (2023). Lung Parenchyma Segmentation from CT Images with a Fully Automatic Method. Multimedia Tools and Applications, 1-23.
  • Mohammadi, N., Mesgari, M. S., & Klein-Paste, A. (2023). An Empirical Agent-Based Model for Residential Segregation, Case Study: Tehran. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 71-78.
  • Mohammadi, V., Rahmani, A. M., Darwesh, A., & Sahafi, A. (2023). Fault tolerance in fog-based Social Internet of Things. Knowledge-Based Systems, 265, 110376.
  • Mohammed, G. B., Shitharth, S., & Sucharitha, G. (2023). A Novel Trust Evaluation and Reputation Data Management Based Security System Model for Mobile Edge Computing Network. In Security and Risk Analysis for Intelligent Edge Computing (pp. 155-170). Cham: Springer International Publishing.
  • Mohammed, R., Kennedy-Clark, S., & Reimann, P. (2023). Using Immersive Technologies to build Primary Preservice Teacher Confidence in Science. Journal of Computers in Mathematics and Science Teaching, 42(3), 205-226.
  • Mohandes, N., Bayhan, S., Sanfilippo, A., & Rub, H. A. (2023). Peer-to-peer trade and the sharing economy at distribution level: A review of the literature. IEEE Access.
  • Mongkhonvanit, K., Hummer, T. M., & Chen, J. (2023, June). Velo: Exploring Animal Behavior Modeling through Hybrid Robotics-Simulation Learning Experience. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (pp. 701-704).
  • Monroe, J., Bolton, E. R., & Berglund, E. Z. (2023). Evaluating Peer-to-Peer Electricity Markets across the US Using an Agent-Based Modeling Approach. Advances in Environmental and Engineering Research, 4(1), 1-40.
  • Moosavi, S. F., Salehnia, N., Seifi, A., AsgharpourMasouleh, A., & Salehnia, N. Designing and Calibrating an Agent‐Based Platform to Evaluate the Effect of Climate Variables on Residential Water Demand. Water and Environment Journal.
  • Moreno, A., Jorba, J., Peralta, C., César, E., Sikora, A., & Hanzich, M. (2023). A methodology for selecting a performance-convenient ABMS development framework on HPC platforms. Simulation Modelling Practice and Theory, 102812.
  • Morell, J. (2023). A Complexity-Based Plan for Evaluating Transformation. Journal of MultiDisciplinary Evaluation, 19(45), 105-130.
  • Moritz, M., Cross, B., & Hunter, C. E. (2023). Artificial pastoral systems: a review of agent-based modelling studies of pastoral systems. Pastoralism, 13(1), 31.
  • Moritz, M., Hunter, C. E., Peart, D. C., Buffington, A., Yoak, A. J., Thomas, J. R., ... & Hamilton, I. M. (2023). Coupled Demographic Dynamics of Herds and Households Constrain Livestock Population Growth in Pastoral Systems. Human Ecology, 1-13.
  • Moritz, M., Hunter, C. E., Peart, D. C., & Hamilton, I. M. Agent-Based Modeling in Mixed Methods Research. In The Handbook of Teaching Qualitative and Mixed Research Methods (pp. 322-325). Routledge.
  • Morsi, N., Kamel, S., Sabry, H., & Assem, A. (2023). Computational design for architectural space planning of commercial exhibitions: A framework for visitors interaction using parametric design and agent-based modeling. Architecture and Planning Journal (APJ), 28(3), 11.
  • Mou, S., Zhong, K., & Ma, Y. (2023). Regulating the Big Data-Based Discriminatory Pricing in Platform Retailing: A Tripartite Evolutionary Game Theory Analysis. Mathematics, 11(11), 2579.
  • Mousavi Moghaddam, R., & Aghazadeh, N. (2023). Lung Parenchyma Segmentation from CT Images with a Fully Automatic Method. Multimedia Tools and Applications, 1-23.
  • Mponela, P., Le, Q. B., Snapp, S., Villamor, G. B., Tamene, L., & Borgemeister, C. (2023). MASSAI: Multi-agent system for simulating sustainable agricultural intensification of smallholder farms in Africa. MethodsX, 11, 102467.
  • Müller, B., Gosal, A., & Ziv, G. (2023, September). An Agent-Based Model of UK Farmers' Decision-Making on Adoption of Agri-environment Schemes Chunhui Li, Meike Will, Nastasija Grujić, Jiaqi Ge®. In Advances in Social Simulation: Proceedings of the 17th Social Simulation Conference, European Social Simulation Association (p. 463). Springer Nature.
  • Musaeus, L. H., Caspersen, M. E., & Musaeus, P. (2023, September). A Template for Teaching Computational Modelling in High School. In Proceedings of the 18th WiPSCE Conference on Primary and Secondary Computing Education Research (pp. 1-10).
  • Mussawar, O., Mayyas, A., & Azar, E. (2023). Built Form and Function as Determinants of Urban Energy Performance: An Integrated Agent-based Modeling Approach and Case Study. Sustainable Cities and Society, 104660.
  • Mussawar, O., Urs, R. R., Mayyas, A., & Azar, E. (2023). Performance and prospects of urban energy communities conditioned by the built form and function: A systematic investigation using agent-based modeling. Sustainable Cities and Society, 99, 104957.
  • Naredo, E., Sansores, C., Godinez, F., López, F., Urbano, P., Trujillo, L., & Ryan, C. (2023). Comprehensive Analysis of Learning Cases in an Autonomous Navigation Task for the Evolution of General Controllers. Mathematical and Computational Applications, 28(2), 35.
  • Navarrete López, C. F. (2023). Metodologías epidemiológicas de análisis de datos para la operación y gestión de redes de abastecimiento urbano de agua (Doctoral dissertation, Universitat Politècnica de València).
  • Navarro Osma, R. (2023). Implementación del juego jGomas en Spade 3 (Doctoral dissertation, Universitat Politècnica de València).
  • Neal, A., Ballard, T., & Palada, H. (2023). HOW TO PUBLISH AND REVIEW A COMPUTATIONAL MODEL. Computational Modeling for Industrial-Organizational Psychologists, 122.
  • Nespeca, V., Comes, T., & Brazier, F. (2023). A Methodology to Develop Agent-Based Models for Policy Support Via Qualitative Inquiry. Journal of Artificial Societies and Social Simulation, 26(1).
  • Neumann, M., Dirksen, V., & Dickel, S. On the construction of plausible futures in interpretive agent-based modelling. In An Interpretive Account to Agent-based Social Simulation (pp. 170-188). Routledge.
  • Newman, J. D. (2023). Determining the influence of organism level processes on the population dynamics of Bactrocera tryoni (Doctoral dissertation, Queensland University of Technology).
  • Ng, L. T. (2023). AGENT-BASED COMPUTATIONAL GEOMETRY (Doctoral dissertation, University of Washington).
  • Nguyen, D. T. (2023). Hybrid Simulation-based Lean Management Methodology to Improve the Sustainability of the Construction Phase (Doctoral dissertation, University of Kassel).
  • Nica, I., Georgescu, I., Delcea, C., & Chiriță, N. (2023). Toward Sustainable Development: Assessing the Effects of Financial Contagion on Human Well-Being in Romania. Risks, 11(11), 204.
  • Nichols, A. C. (2023). An Artificial Honeybee Colony Algorithm to Quantify Adaptability via Resilience for Space System Architectures (Doctoral dissertation, The University of Alabama).
  • Nitsch, F., Schimeczek, C., Frey, U., & Fuchs, B. (2023). FAME-Io: Configuration tools for complex agent-based simulations. Journal of Open Source Software, 8(84).
  • Noormohammadi, R., Khatami Firoozabadi, S. M., Alamtabriz, A., Ehtesham Rasi, R., & Daneshvar, A. (2023). Estimating the amount of fuel consumption and air pollution caused by the traffic of buses rapid transit using agent-based modeling. Journal of Environmental Science and Technology.
  • Noubar, H. B. K., Holagh, S. R., & Sadri, A. (2023). Identifying Factors Affecting Green Consumer Purchase Behavior on E-Commerce Websites. TalTech Journal of European Studies, 13(1), 40-62.
  • Nurdiansyah, H., Almubaroq, H. Z., Risdhianto, A., & Mualim, M. (2023). EVALUATION OF THE SPREAD OF RADICALISM, EXTREMISM, AND TERRORISM IN INDONESIA'S DEFENSE USING AGENT-BASED SIMULATIONS. International Journal of Humanity Studies (IJHS), 6(2), 228-239.
  • Obaid, A. N., & Namah, A. J. (2023). Effectiveness of Artificial Intelligence in Graphic Design. Remittances Review, 8(4).
  • Ocak, C., Yadav, A., Vogel, S., & Patel, A. (2023). Teacher Education Faculty’s Perceptions About Computational Thinking Integration for Pre-service Education. Journal of Technology and Teacher Education, 31(3), 299-349.
  • Ogami, T., & Nishinari, K. (2023). Features of ladders during evacuation from oil and LNG plants. Physica A: Statistical Mechanics and its Applications, 128745.
  • Ogunsakin, R., Mehandjiev, N., & Marin, C. A. (2023). Towards adaptive digital twins architecture. Computers in Industry, 149, 103920.
  • Oktavia Mulyono, Y., Sukhbaatar, U., & Cabrera, D. (2023). ‘Hard’and ‘Soft’Methods in Complex Adaptive Systems (CAS): Agent Based Modeling (ABM) and the Agent Based Approach (ABA). Journal of Systems Thinking, 1-33.
  • Olagoke, A., Jeltsch, F., Tietjen, B., Berger, U., Ritter, H., & Maaß, S. (2023). Small‐scale heterogeneity shapes grassland diversity in low‐to‐intermediate resource environments. Journal of Vegetation Science, 34(4), e13196.
  • Oliveira, H., Mendes, F., & Henriques, A. (2022). A investigação sobre o ensino e a aprendizagem de temas matemáticos publicada em 30 anos da revista Quadrante. Quadrante, 31(2), 32-62.
  • O’Brien, W., Calì, D., De Simone, M., Tabadkani, A., Azar, E., Rajus, V. S., ... & Rysanek, A. (2023). Introduction to Occupant Modeling. In W. O'Brien & F. Tahmasebi (Eds.), Occupant-Centric Simulation-Aided Building Design: Theory, Application, and Case Studies (pp. 104-144). Routledge.
  • O’Neill, M. (2023). Coordination games and regional economic transitions. Territory, Politics, Governance, 1-19.
  • O'Shea, T., Bates, P., & Neal, J. Testing the impact of direct the indirect flood warnations on population environment using an agent-based model. Natural Hazards and Earth System Sciences, 20, 2281-2305.
  • Osoianu, F. (2023). Utilizarea datelor etnografice în crearea de medii și agenți pentru simularea comportamentului criminal real (Doctoral dissertation, Universitatea Tehnică a Moldovei).
  • Ouda, E., Sleptchenko, A., & Simsekler, M. C. E. (2023). Comprehensive review and future research agenda on discrete-event simulation and agent-based simulation of emergency departments. Simulation Modelling Practice and Theory, 102823.
  • Ozulumba, T., Montalbine, A. N., Ortiz-Cárdenas, J. E., & Pompano, R. R. (2023). New tools for immunologists: models of lymph node function from cells to tissues. Frontiers in Immunology, 14, 1183286.
  • Paape, N., Van Eekelen, J. A. W. M., & Reniers, M. A. (2023). Review of simulation software for cyber-physical production systems with intelligent distributed production control. International Journal of Computer Integrated Manufacturing, 1-23.
  • Pacheco, V. C., dos Santos Bonfim, G., Junior, M. C. B., Alberte, E. P. V., & Costa, D. B. (2023). Modelagem baseada em agentes para a gestão da segurança no canteiro de obras: uma análise da produção científica. SIMPÓSIO BRASILEIRO DE GESTÃO E ECONOMIA DA CONSTRUÇÃO, 13, 1-9.
  • Palanca, J., Rincon, J. A., Carrascosa, C., Julian, V. J., & Terrasa, A. (2023). Flexible Agent Architecture: Mixing Reactive and Deliberative Behaviors in SPADE. Electronics, 12(3), 659.
  • Palma-Morales, O. J., Noguera-Hidalgo, Á. L., Hernández-Sandoval, J. S., & Ávila-Robayo, D. S. (2023). Redes de narcotráfico marítimo: un análisis desde la complejidad y la simulación de sistemas sociales. Revista Científica General José María Córdova, 21(43), 743-764.
  • Panbumrungkij, T., & Vannametee, E. (2023). การ ศึกษา พฤติกรรม การ เลือก สถาน ที่ ท่องเที่ยว และ การ กระจาย ตัว ของ นักท่องเที่ยว ด้วย แบบ จำลอง ตัวแทน: กรณี ศึกษา จังหวัด ชลบุรี: A Case Study of Chon Buri Province. Journal of Letters, 52(2), 1-30.
  • Pantelić, S., Milovanović, B., Đogatović, M., Živanović, P., Bajčetić, S., Tica, S., & Nađ, A. (2023). Consequence Assessment Model for Gasoline Transport: Belgrade Case Study Based on Multi-Agent Simulation. Sustainability, 15(3), 2598.
  • Panzer, M., & Gronau, N. (2023). Designing an adaptive and deep learning based control framework for modular production systems. Journal of Intelligent Manufacturing, 1-24.
  • Patel, V. (2023). The impacts of climate change on structurally interconnected social-ecological systems: using integrated spatial modelling to assess beehive migration patterns in Western Australia (Doctoral dissertation, The University of Western Australia).
  • Paudel, R., & Ligmann-Zielinska, A. (2023). A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-Based Model Development. Algorithms, 16(7), 338.
  • Paul, P. N. T., Diarra, M., Dione, D., & Dathe, H. (2023, October). An Agent-Based Model for Studying Effects of Mosquito Bites Protection and the Intra-host Treatment on the Malaria Transmission. In Proceedings of the Future Technologies Conference (pp. 298-308). Cham: Springer Nature Switzerland.
  • Pavone, M. (2023, February). How a Different Ant Behavior Affects on the Performance of the Whole Colony. In Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings (Vol. 13838, p. 187). Springer Nature.
  • Pecoraro, F., Accordino, F., Cecconi, F., & Paolucci, M. (2023). Agent Based Modelling for Simulating the Interregional Patient Mobility in Italy. Studies in Health Technology and Informatics, 302, 297-301.
  • Pedreschi, D., Dignum, F., Morini, V., Pansanella, V., & Cornacchia, G. (2023). Towards a Social Artificial Intelligence. In Human-Centered Artificial Intelligence: Advanced Lectures (pp. 415-428). Cham: Springer International Publishing.
  • Peel, A., Dabholkar, S., Anton, G., Horn, M., & Wilensky, U. (2023). Characterizing changes in teacher practice and values through co-design and implementation of computational thinking integrated biology units. Computer Science Education, 1-26.
  • Peleg, R., Lahav, O., Hagab, N., Talis, V., & Levy, S. T. (2023). Listening to or looking at models: Learning about dynamic complex systems in science among learners who are blind and learners who are sighted. Journal of Computer Assisted Learning.
  • Pellet, T. L. (2023). Essays in Macroeconomics, Production Networks and Monetary History (Doctoral dissertation, Northwestern University).
  • Peng, Y., Lopez, J. M. R., Santos, A. P., Mobeen, M., & Scheffran, J. (2022). Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil. Cities, 104161.
  • Peng, L., & Luo, S. (2023). Exploration of tolerance of unfairness under COVID-19 mortality salience and its effect on epidemic development. Journal of Pacific Rim Psychology, 17, 18344909231165188.
  • Peñuela Escobar, L. F. (2023). Agent-based Modelling for Assessing Potential Water-Related Conflicts (Doctoral dissertation, Escuela Colombiana de Ingeniería).
  • Perdana, M. J. G., Mulyatno, I. P., Santosa, A. W. B., & dibantu Komputer, L. D. K. K. (2023). Analisa Evakuasi Pada Kapal KM. Kirana IX 9168 GT Menggunakan Metode Agent Based Modelling Simulation Dalam Kondisi Kebakaran. Jurnal Teknik Perkapalan, 11(2), 93.
  • Perez Aguilar, D. A. (2023). Diseño de un clasificador para el mantenimiento predictivo de una red de distribución eléctrica utilizando Deep Learning (Doctoral dissertation, University of Piura, Peru).
  • Peters, M. (2023). Smallholder Market Systems: Understanding System Behavior and the Role of Relationships (Doctoral dissertation, The George Washington University).
  • Pietzsch, B. W., Schmidt, A., Groeneveld, J., Bahlburg, D., Meyer, B., & Berger, U. (2023). The impact of salps (Salpa thompsoni) on the Antarctic krill population (Euphausia superba): an individual-based modelling study. Ecological Processes, 12(1), 1-16.
  • Platas-López, A., Guerra-Hernández, A., Quiroz-Castellanos, M., & Cruz-Ramírez, N. (2023). Agent-Based Models Assisted by Supervised Learning: A Proposal for Model Specification. Electronics, 12(3), 495.
  • Potturi, A. (2023). MASS JAVA Benchmarking (Doctoral dissertation, University of Washington).
  • Potvin, J. (2023). Data with direction: design research leading to a system specification for ‘an internet of rules’ (Doctoral dissertation, Université du Québec en Outaouais).
  • Prakayaphun, T., Hayashi, Y., Vichiensan, V., & Takeshita, H. (2023). Identifying Impacts of School-Escorted Trips on Traffic Congestion and the Countermeasures in Bangkok: An Agent-Based Simulation Approach. Sustainability, 15(23), 16244.
  • Prédhumeau, M., & Manley, E. (2023). A synthetic population for agent-based modelling in Canada. Scientific Data, 10(1), 148.
  • Prinz, A., Engebretsen, M., Gjøsæter, T., Møller-Pedersen, B., & Xanthopoulou, T. D. (2023). Models, systems, and descriptions. Frontiers in Computer Science, 5, 1031807.
  • Prnjak, A., Zaharija, G., Mladenović, M., & Nejašmić, D. (2023). USING SIMULATION IN TEACHING ARTIFICIAL INTELLIGENCE. In INTED2023 Proceedings (pp. 6954-6960). IATED.
  • Purbani, D., Marzuki, M. I., Ontowirjo, B., Zein, F. M., & Wisha, U. J. Tsunami Evacuation Model Assessment in the Panimbang Subdistrict, Banten Province, Indonesia: GIS-and Agent-Based Modeling Approaches. Banten Province, Indonesia: GIS-and Agent-Based Modeling Approaches.
  • Qabaja, H., Ashqer, M. I., Bikdash, M., & Ashqar, H. I. (2023). A Meso-Scale Petri Net Model to Simulate a Massive Evacuation along the Highway System. Future Transportation, 3(1), 311-328.
  • Qi, J. (2023). Advancing Green Stormwater Infrastructure Through Understanding the Influences of Social Factors (Doctoral dissertation, The University of North Carolina at Charlotte).
  • Qi, X., Xie, J., Huang, H., Li, J., & Yuan, W. (2023). Reconciling grain production and environmental costs during rural livelihood transitions: a simulation-based approach in southern China. Food Security, 1-19.
  • Quagliarini, E., Bernardini, G., & D'Orazio, M. (2023). How Increasing Temperature Scenarios Can Alter Terrorist Act Risk in Different Historical Squares? A Simulation-Based Approach in Typological Italian Squares. Heritage, 6(7), 5151-5188.
  • Rabb, N., & Cowen, L. (2023). Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization. In International Conference on Complex Networks and Their Applications (pp. 3-15). Springer, Cham.
  • Rabb, N., Cowen, L., & de Ruiter, J. P. (2023). Investigating the effect of selective exposure, audience fragmentation, and echo-chambers on polarization in dynamic media ecosystems. Applied Network Science, 8(1), 78.
  • Rahman, S., & Li, S. (2023, March). Multi-Agent-Based Modeling of Deshopping Behavior Considering Two or More Shops or Web Sites. In 2023 International Conference on Information Management (ICIM) (pp. 104-109). IEEE.
  • Raimbault, J., & Pumain, D. (2023, July). Innovation dynamics in multi-scalar systems of cities. In ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press.
  • Rajanikanth, K. N., Sait, M. R., & Kashi, S. R. (2023, February). Enhancing Immersive User Experience Quality of StudoBot Telepresence Robots with Reinforcement Learning. In 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS) (pp. 1-6). IEEE.
  • Rakotoarisoa, M. M., Reulier, R., & Delahaye, D. (2023). Agent-Based Modelling of the Evolution of Hydro-Sedimentary Connectivity: The Case of Flash Floods on Arable Plateaus. Applied Sciences, 13(5), 2967.
  • Ran, P., Jie, M. C., Keyuan, M. D., Yehao, B. L., & Xueliang, B. L. (2023). Research on Traditional Performing Places in Wuling Mountain Area of China. Convergence of Contemporary Thought in Architecture, Urbanism, and Heritage Studies, 237.
  • Rangoni, R., & Jager, W. Social Dynamics von Littering and Adaptable Clean Solutions Explored Usage Agent-Based Modelling Download PDF. Journal out Artificial Societies and Socially Simulation, 20(2), 1.
  • Rankin, P., Meunier, L., & Kontopoulou, M. (2023, May). Characterization and identification of microplastics in freshwater systems. In AIP Conference Proceedings (Vol. 2607, No. 1). AIP Publishing.
  • Rappel, O., Ben-Asher, J. Z., & Bruckstein, A. M. (2023). Exploration of unknown indoor regions by a swarm of energy-constrained drones. arXiv preprint arXiv:2305.08957.
  • Rathee, G., Garg, S., Kaddoum, G., Choi, B. J., Benslimane, A., & Hassan, M. M. (2023). A secure and trusted context prediction for next generation autonomous vehicles. Alexandria Engineering Journal, 78, 131-140.
  • Rattner, B. A., Bean, T. G., Beasley, V. R., Berny, P., Eisenreich, K. M., Elliott, J. E., ... & Salice, C. J. (2023). Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects. Integrated Environmental Assessment and Management.
  • Regis, S., Manicom, O., & Doncescu, A. (2023). An agent-based model of COVID-19 pandemic and its variants using fuzzy subsets and real data applied in an island environment. The Knowledge Engineering Review, 38, e2.
  • Reimuth, A., Hagenlocher, M., Yang, L. E., Katzschner, A., Harb, M., & Garschagen, M. (2023). Urban growth modelling for the assessment of future climate and disaster risks: approaches, gaps and needs. Environmental Research Letters.
  • Reinhard, A., & Zaia, S. (2023). Photogrammetry and GIS in Human-Occupied Digital Landscapes. Advances in Archaeological Practice, 1-13.
  • Ren, J., Li, H., Zhang, M., Wu, C., & Yu, X. (2023). A Self-powered Sensor Network Data Acquisition, Modeling and Analysis Method for Cold Chain Logistics Quality Perception. IEEE Sensors Journal.
  • Rennels, L., & Chasins, S. E. (2023). How Domain Experts Use an Embedded DSL. Proceedings of the ACM on Programming Languages, 7(OOPSLA2), 1499-1530.
  • Richards, S., Gámez, S., & Harris, N. C. (2023). Modeling effects of habitat structure on intraguild predation frequency and spatial coexistence between jaguars and ocelots. Behavioral Ecology, arad080.
  • Richmond, P., Chisholm, R., Heywood, P., Chimeh, M. K., & Leach, M. (2023). FLAME GPU 2: A framework for flexible and performant agent based simulation on GPUs. Software: Practice and Experience.
  • Rivière, J., Hénard, A., Peillard, E., Kubicki, S., & Coppin, G. (2023, May). How to Grasp the Complexity of Self-Organised Robot Swarms?. In French Regional Conference on Complex Systems (FRCCS 2023).
  • Robak, A., Bush, S., & Bjornlund, H. (2023). Advancing the impact identification step of benefit-cost analysis of potable water infrastructure investments: A systems method for identifying important impacts pre-monetisation. Water Research, 239, 120058.
  • Roberts, S., Lebbin, P., Gwynne, S., Thomas, R., Dabkowski, R., Law, A., ... & Grewal, A. (2023). Communicable Disease Transmission in Air Travel: Human Behavior–Phase 1 Report (No. DOT/FAA/AM-23/28). United States. Department of Transportation. Federal Aviation Administration. Office of Aviation. Civil Aerospace Medical Institute.
  • Robinson, J. A., Kanduč, T., Sarigiannis, D., & Kocman, D. (2023). Simulating the impact of particulate matter exposure on health-related behaviour: A comparative study of stochastic modelling and personal monitoring data. Health & Place, 83, 103111.
  • Rodríguez, J., Hölzchen, E., Caso-Alonso, A. I., Berndt, J. O., Hertler, C., Timm, I. J., & Mateos, A. (2023). Computer simulation of scavenging by hominins and giant hyenas in the late Early Pleistocene. Scientific Reports, 13(1), 14283.
  • Rodriguez, M., Boixader, F., Epelde, F., Bruballa, E., De Giusti, A., Wong, A., ... & Luque, E. (2023, June). Resilience Analysis of an Emergency Department in Stressful Situations. In Conference on Cloud Computing, Big Data & Emerging Topics (pp. 45-54). Cham: Springer Nature Switzerland.
  • Rodríguez, S. S., & Alatriste, F. R. (2023). La coopetencia como un fenómeno emergente. Un modelo basado en agentes. Revista Universidad y Empresa, 25(45), 1-31.
  • Rodríguez-Arias, A., Alonso-Betanzos, A., Guijarro-Berdiñas, B., & Sánchez-Marroño, N. (2023). Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures. arXiv preprint arXiv:2307.15723.
  • Roma, G. Agent-Based Music Live Coding: Sonic adventures in 2D. Organised Sound, 1-10.
  • Roosta, A., Kaths, H., Barthauer, M., Erdmann, J., Flötteröd, Y. P., & Behrisch, M. (2023, June). The state of bicycle modeling in sumo. In SUMO Conference Proceedings (Vol. 4, pp. 55-64).
  • Rouleau, M. D. (2023). Agent-based modeling (ABM). In Dictionary of Ecological Economics (pp. 8-8). Edward Elgar Publishing.
  • Ruiz-Martin, C. (2023). Theory and foundations of modeling and simulation. Simulation, 99(5), 431-432.
  • Rumsey, K., Francom, D., & Shen, A. (2023). Generalized Bayesian MARS: Tools for Emulating Stochastic Computer Models. arXiv preprint arXiv:2306.01911.
  • Russo, R., & Blikstein, P. (2023). Beyond Disinformation: An Agent-Based Modeling and Curriculum for the Post-Truth World. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 2125-2126. International Society of the Learning Sciences.
  • Rusinko, J. (2023). 2012-Rusinko-Swan-Agent-Based Fabric Modeling Using Differential Equations.
  • Saba, J., Hel-Or, H., & Levy, S. T. (2023). Promoting learning transfer in science through a complexity approach and computational modeling. Instructional Science, 1-33.
  • Saba, J., Kapur, M., & Roll, I. (2023, June). The Development of Multivariable Causality Strategy: Instruction or Simulation First?. In International Conference on Artificial Intelligence in Education (pp. 41-53). Cham: Springer Nature Switzerland.
  • Sabal, J., Kapur, M., & Roll, I. 1 Hebrew University of Jerusalem, Jerusalem, Israel 2. In Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (p. 41). Springer Nature.
  • Sáez, P., Herrera, C., Booth, C., Belmokhtar-Berraf, S., & Parada, V. (2023). A product-driven system with an evolutionary algorithm to increase flexibility in planning a job shop. PloS one, 18(2), e0281807.
  • Sáez, P., Herrera, C., & Parada, V. (2023). Reducing Nervousness in Master Production Planning: A Systematic Approach Incorporating Product-Driven Strategies. Algorithms, 16(8), 386.
  • Safdar, M. F., Nowak, R. M., & Pałka, P. (2023). Exploring artificial intelligence algorithms for electrocardiogram (ECG) signal analysis: A comprehensive review. Computers in Biology and Medicine, 107908.
  • Sagar, S. (2023). Trust Computational Heuristics for Social Internet of Things (Doctoral dissertation, Macquarie University).
  • Saint-Pierre, P., & Savy, N. (2023). Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue. The International Journal of Biostatistics.
  • Saisridhar, P., Thürer, M., & Avittathur, B. (2023). Assessing supply chain responsiveness, resilience and robustness (Triple-R) by computer simulation: a systematic review of the literature. International Journal of Production Research, 1-31.
  • Salazar-Serna, K., Cadavid, L., Franco, C. J., & Carley, K. M. (2023, September). Simulating Transport Mode Choices in Developing Countries. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 209-218). Cham: Springer Nature Switzerland.
  • Salman, M. Y., & Hasar, H. (2023). Review on Environmental Aspects in Smart City Concept: Water, Waste, Air Pollution and Transportation Smart Applications using IoT Techniques. Sustainable Cities and Society, 104567.
  • Sánchez, S., & Alatriste, F. R. (2023). Coopetition as an Emerging Phenomenon: A Model Based on Agents. Universidad & Empresa, 25(45), 1.
  • Sanfilippo, D. The Aesthetics of Musical Complex Systems. Organised Sound, 1-11.
  • Sang, B., Aghamohammadi, N., & Md Noor, R. (2023). The Effects of Dynamic Strategy and Updating Network Structure Towards Customer Participation Innovation Performance. Journal of the Knowledge Economy, 1-31.
  • Santana-Robles, F., Granillo-Macias, R., Armas-Alvarez, B., & Rodríguez, Z. B. (2023). Modelos matemáticos para la evacuación de personas en la cadena de suministro humanitaria: una revisión. Ingenio y Conciencia Boletín Científico de la Escuela Superior Ciudad Sahagún, 10(19), 48-60.
  • Santos, J. I., Pereda, M., Ahedo, V., & Galán, J. M. (2023, July). NetLogo Teaching Tool to Illustrate the Cooling Process in Simulated Annealing Using the Metropolis Model. In Industry 4.0: The Power of Data: Selected Papers from the 15th International Conference on Industrial Engineering and Industrial Management (pp. 19-27). Cham: Springer International Publishing.
  • Santos, M., Garcês, C., Ferreira, A., Carvalho, D., Travassos, P., Bastos, R., ... & Cabral, J. A. (2023). Side effects of European eco schemes and agri-environment-climate measures on endangered species conservation: Clues from a case study in mountain vineyard landscapes. Ecological Indicators, 148, 110155.
  • Santucci, J. F., Capocchi, L., Ören, T., Szabo, C., & Graciano Neto, V. V. (2023). Synergies of Soft Computing and M&S. In Body of Knowledge for Modeling and Simulation: A Handbook by the Society for Modeling and Simulation International (pp. 287-309). Cham: Springer International Publishing.
  • Sapienza, A., & Falcone, R. (2023). An Autonomy-Based Collaborative Trust Model for User-IoT Systems Interaction. In Intelligent Distributed Computing XV (pp. 178-187). Cham: Springer International Publishing.
  • Sapienza, A., & Falcone, R. (2023). Exploiting autonomy in a User-IoT system collaborative trust model. International Journal of Parallel, Emergent and Distributed Systems, 1-13.
  • Sapino, F., Haer, T., Saiz-Santiago, P., & Pérez-Blanco, C. D. (2023). A multi-agent cellular automata model to explore water trading potential under information transaction costs. Journal of Hydrology, 618, 129195.
  • Sarnatskyi, V. V., & Baklan, I. V. (2023). CTRACEENV: A PLATFORM FOR DEVELOPMENT AND ANALYSIS OF AGENT-BASED EPIDEMIOLOGICAL MODELS. ВЧЕНІ ЗАПИСКИ, 234-240.
  • Saval, A., Minh, D. P., Chapuis, K., Tranouez, P., Caron, C., Daudé, É., & Taillandier, P. (2023). Dealing with mixed and non-normative traffic. An agent-based simulation with the GAMA platform. Plos one, 18(3), e0281658.
  • Sazzad, A., Nawer, N., Mahbub Rimi, M., Habibul Kabir, K., & Foysal Haque, K. (2023). Designing of an Underwater-Internet of Things (U-IoT) for Marine Life Monitoring. In The Fourth Industrial Revolution and Beyond: Select Proceedings of IC4IR+ (pp. 291-303). Singapore: Springer Nature Singapore.
  • Schmickl, T., & Karsai, I. (2023). Self-complexification through integral feedback in eusocial paper wasps of various levels of sociality. Heliyon, 9(9).
  • Schmolke, A., Galic, N., & Hinarejos, S. (2023). SolBeePop: A model of solitary bee populations in agricultural landscapes. Journal of Applied Ecology.
  • Schoeman, I., & Chakwizira, J. (2023). Advancing a Performance Management Tool for Service Delivery in Local Government. Administrative Sciences, 13(2), 31.
  • Scholz, G., Wijermans, N., Paolillo, R., Neumann, M., Masson, T., Chappin, É., ... & Kocheril, G. (2023). Social Agents? A Systematic Review of Social Identity Formalizations. Journal of Artificial Societies and Social Simulation, 26(2), 6.
  • Schubert, D. (2023, September). Using Survey Data to Develop Agent-Based Models of Spatial Segregation. In Conference of the European Social Simulation Association (pp. 609-619). Cham: Springer Nature Switzerland.
  • Secchi, D. (2023). The relevance of social dynamics and dispositions on non-traditional aids to the strategic process. In Cognitive aids in strategy (Vol. 6, pp. 135-157). Emerald Publishing Limited.
  • Seger, H. (2023). Agent-based and contact network modeling applications for Escherichia coli transmission in commercial feedlot settings (Doctoral dissertation, Kansas State University).
  • Seizovic, A., Thorpe, D., Goh, S., & Skoufer, L. (2023). Cybernetics and battle management system (BMS) in network soldier system application. Australian Journal of Multi-Disciplinary Engineering, 1-23.
  • Sellán Vera, K. B. (2023). Modelado basado en agentes para comprobar la validez de un sistema industrial circular en Santa Elena, Ecuador (Bachelor's thesis, La Libertad: Universidad Estatal Península de Santa Elena, 2023).
  • Sengupta, R., Vankeerberghen, G., Wen, R., Rao, J., & Chen, Y. (2023). GIS‐enabled historiography to determine travel routes during the Western Han period via agent‐based models and least‐cost path analysis. Transactions in GIS, 27(4), 1090-1103.
  • Sengupta, S., Kovalevsky, D. V., Bouwer, L. M., & Scheffran, J. (2023). Urban Planning of Coastal Adaptation under Sea-Level Rise: An Agent-Based Model in the VIABLE Framework. Urban Science, 7(3), 79.
  • Serena, L., Marzolla, M., D’Angelo, G., & Ferretti, S. (2023). A review of multilevel modeling and simulation for human mobility and behavior. Simulation Modelling Practice and Theory, 102780.
  • Seuru, Samuel, Liliana Perez, and Ariane Burke. "Why Were Rabbits Hunted in the Past? Insights from an Agent-Based Model of Human Diet Breadth in Iberia During the Last Glacial Maximum." Modelling Human-Environment Interactions in and beyond Prehistoric Europe. Cham: Springer International Publishing, 2023. 107-123.
  • Shaaban, M. (2023). Viability of the social–ecological agroecosystem (ViSA). SoftwareX, 101360.
  • Shahangian, S. A., Tabesh, M., Yazdanpanah, M., Akbarzadeh, A., Raoof, M. A., Zobeidi, T., ... & Sitzenfrei, R. Introducing a Novel Hybrid Agent-Based Framework for Simulating the Adoption of Residential Water Conservation Behaviors. In World Environmental and Water Resources Congress 2023 (pp. 775-788).
  • Shaharuddin, R. A., & Misro, M. Y. (2023). Controlling Traffic Congestion in Urbanised City: A Framework Using Agent-Based Modelling and Simulation Approach. ISPRS International Journal of Geo-Information, 12(6), 226.
  • Shakya, J., Ghribi, C., & Merghem-Boulahia, L. (2023). Agent-based modeling and simulation for 5G and beyond networks: A comprehensive survey. Simulation Modelling Practice and Theory, 102855.
  • Shangguan, Y., Tian, X., Jin, S., Gao, K., Hu, X., Yi, W., ... & Wang, S. (2023). On the Fundamental Diagram for Freeway Traffic: Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models. Mathematics, 11(16), 3460.
  • Shen, X., & Zhou, Y. (2023, April). Analysis and Research on Express Packaging Recycling Logistics System Based on Netlogo. In 2023 IEEE 3rd International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB) (pp. 124-129). IEEE.
  • Shinde, S. B., & Kurhekar, M. P. (2023, June). Agent-based modeling of complex adaptive systems: An interdisciplinary approach. In AIP Conference Proceedings (Vol. 2705, No. 1). AIP Publishing.
  • Shobayo, P. (2023). Enhancing the competitiveness of inland waterway transport: a multi-methodological approach applied to port barge congestion and urban areas (Doctoral dissertation, University of Antwerp).
  • Siatras, V., Bakopoulos, E., Mavrothalassitis, P., Nikolakis, N., & Alexopoulos, K. (2023). On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System. Applied Sciences, 13(17), 9540.
  • Sikk, K. (2023). Exploring Environmental Determinism with Agent-Based Simulation of Settlement Choice. In Modelling Human-Environment Interactions in and beyond Prehistoric Europe (pp. 143-154). Cham: Springer International Publishing.
  • Siljak, H., Kennedy, J., Byrne, S., & Einicke, K. (2023, February). Noise mitigation of UAV operations through a complex networks approach. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings (Vol. 265, No. 5, pp. 2208-2214). Institute of Noise Control Engineering.
  • Simms, I. J. (2023). Are Food Banks Impacting Food Retail? Examining the Relationship Between Hunger Relief Distributions and Retail Transactions in a Local Food Environment (Doctoral dissertation, Case Western Reserve University).
  • Singh, S. K., Sharma, C., & Maiti, A. (2023). Modeling and experimental validation of forward osmosis process: Parameters selection, permeate flux prediction, and process optimization. Journal of Membrane Science, 121439.
  • Slingerland, G., Nikolic, I., & Brazier, F. (2023). It's in the social network: The Social Neighbourhood model to unravel local social structures for liveable and safe neighbourhoods. Cities, 135, 104215.
  • Smaldino, P. (2023). Modeling social behavior: Mathematical and agent-based models of social dynamics and cultural evolution. Princeton University Press.
  • Smith, M. M., & Pauli, J. N. Connectivity maintains genetic diversity and population persistence within an archipelagic refugia even under declining lake ice. Mechanisms of species recovery for a forest carnivore in a changing landscape, 173.
  • Snyder, S., Zhu, K., Vega, R., Nowzari, C., & Parsa, M. (2023). Zespol: A Lightweight Environment for Training Swarming Agents. arXiv preprint arXiv:2306.17744.
  • Sobkowicz, P. (2023). Social Depolarization and Diversity of Opinions—Unified ABM Framework. Entropy, 25(4), 568.
  • Sondakh, D. E., Pungus, S. R., & Tombeng, M. T. (2023). Pengenalan Computational Thinking Bagi Siswa Vokasi SMKN 1 Sorong. Servitium Smart Journal, 2(2), 90-98.
  • Sotnik, G., Choporov, S., & Shannon, T. (2023). The Role of Social Identity in a Population's Adoption of Prosocial Common-Pool Behavior. Journal of Artificial Societies and Social Simulation, 26(3).
  • Soto, M. D. C. S., Ramírez, M. R., Rojas, E. M., & Barajas, S. M. (2023). La simulación computacional del ecosistema social Entorno Laboral-Escuela. Revista Ibérica de Sistemas e Tecnologias de Informação, (E61), 296-306.
  • Souidi, M. E. H., Maarouk, T. M., Ledmi, M., Ledmi, A., & Rahab, H. (2023). Multi-Pursuer Multi-Evader Games Based on Dynamic Elimination Priorities of the Dominated Strategies. Journal of Computer and Systems Sciences International, 1-14.
  • Soumya, K. V., Sujitha, S., Kanaujia, S., Agarwalla, S., Sameer, S., & Manzoor, T. (2023, February). Silent Surveillance Autonomous Drone For Disaster Management And Military Security Using Artificial Intelligence. In 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 1-4). IEEE.
  • Soy, O., & Tavacioglu, L. (2023). Satisfaction of Istanbul Citizens with Urban Public Transportation. Transactions on Maritime Science, 12(1).
  • Speelman, E. N., Escano, E., Marcos, D., & Becu, N. (2023). Serious games and citizen science; from parallel pathways to greater synergies. Current Opinion in Environmental Sustainability, 64, 101320.
  • Squazzoni, F., & Bianchi, F. (2023). Exploring Interventions on Social Outcomes with In Silico, Agent-Based Experiments. In Causality in Policy Studies: a Pluralist Toolbox (pp. 217-234). Cham: Springer International Publishing.
  • Stavropoulou, E., Mitropoulos, L., Tzouras, P. G., Karolemeas, C., & Kepaptsoglou, K. (2023, March). An Evaluation of Agent-Based Models for Simulating E-Scooter Sharing Services in Urban Areas. In Smart Energy for Smart Transport: Proceedings of the 6th Conference on Sustainable Urban Mobility, CSUM2022, August 31-September 2, 2022, Skiathos Island, Greece (pp. 959-976). Cham: Springer Nature Switzerland.
  • Stellbrink, L., Kojan, L., & Calero Valdez, A. (2023, July). Making Assumptions Transparent: Iterative Exploratory Modeling as a Stepping Stone for Agent-Based Model Development. In International Conference on Human-Computer Interaction (pp. 389-402). Cham: Springer Nature Switzerland.
  • Stern, J. L., Siddiqi, A., & Grogan, P. T. (2023). Effects of individual strategies for resource access on collaboratively maintained irrigation infrastructure. Systems Engineering, 1-17.
  • Sterrett, S. G. (2023). How mathematics figures differently in exact solutions, simulations, and physical models. In Working Toward Solutions in Fluid Dynamics and Astrophysics: What the Equations Don’t Say (pp. 5-29). Cham: Springer International Publishing.
  • Stolpe, K., & Hallström, J. (2023). Visual Programming as a Tool for Developing Knowledge in STEM Subjects: A Literature Review. Programming and Computational Thinking in Technology Education, 130-169.
  • Su, Y., & Jiang, X. Simulation research on knowledge flow in a collaborative innovation network. Expert Systems, e13280.
  • Su, M. (2023). Fostering Deep Understandings of Emergent Science Concepts (Doctoral dissertation, Arizona State University).
  • Su, M., Ha, J., & Xin, Y. (2023). Investigating the Efficacy of an Ontological Framework for Teaching Natural Selection Using Agent-Based Simulations. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 106-113. International Society of the Learning Sciences.
  • Sulis, E., Mariani, S., & Montagna, S. (2023). A survey on agents applications in healthcare: Opportunities, challenges and trends. Computer Methods and Programs in Biomedicine, 107525.
  • Summad, E., Al Kindi, M., Ouhmidou, I., & Al Kindi, A (2023). An Agent-based Modeling Approach for Effective Innovation Ecosystem Orchestration. In Proceedings of the International Conference on Industrial Engineering and Operations Management.
  • Sun, X., Hu, C., Liu, T., Yue, S., Peng, J., & Fu, Q. (2023). Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics. Biomimetics, 8(8), 580.
  • Sun, Z., Bai, R., & Bai, Z. (2023). The Application of Simulation Methods During the COVID-19 Pandemic: A Scoping Review. Journal of Biomedical Informatics, 104543.
  • Swanson, H., Lawrence, L., Arnell, J., Dawkins, A., Jones, B., Sherin, B., & Wilensky, U. (2023, June). How Co-Designing Computational Modeling Activities Helped Teachers Implement Responsive Teaching Strategies. In Proceedings of the 2023 Symposium on Learning, Design and Technology (pp. 79-86).
  • Sznajd-Weron, K., Je˛ drzejewski, A., & Kamińska, B. (2023). Toward Understanding of the Social Hysteresis: Insights From Agent-Based Modeling. Perspectives on Psychological Science, 17456916231195361.
  • Tack, E., Énée, G., Gaillard, T., Fotsing, J. M., & Flouvat, F. (2023, February). Towards User-Centred Validation and Calibration of Agent-Based Models. In 15th International Conference on Agents and Artificial Intelligence (Vol. 1, pp. 322-329). SCITEPRESS-Science and Technology Publications.
  • Taha, M. A., Sah, M., & Direkoglu, C. (2023). Identification of Locations in Mecca using Image Pre-Processing, Neural Networks and Deep Learning. Arabian Journal for Science and Engineering, 1-21.
  • Tan, Q., Han, J., & Liu, Y. (2023). Examining the synergistic diffusion process of carbon capture and renewable energy generation technologies under market environment: A multi-agent simulation analysis. Energy, 282, 128815.
  • Tang, X., & Lira, M. (2023). Drawing Upon Computational Experiences to Navigate Ontologies. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 926-929. International Society of the Learning Sciences.
  • Tang, Y., Cui, A., & Hu, Y. (2023, September). Research on the Ideological and Political System of Logistics System Simulation Course in the Context of Artificial Intelligence. In 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023) (pp. 519-525). Atlantis Press.
  • Tang, Y., & Javeed, S. A. (2023). The dynamics of entrepreneurial agglomeration formation: Social selection and simulation. Plos one, 18(9), e0291615.
  • Tantiwong, C., Dunster, J. L., Cavill, R., Tomlinson, M. G., Wierling, C., Heemskerk, J. W., & Gibbins, J. M. (2023). An agent-based approach for modelling and simulation of glycoprotein VI receptor diffusion, localisation and dimerisation in platelet lipid rafts. Scientific Reports, 13(1), 3906.
  • Tarantino, R., Panunzi, G., & Romano, V. (2023). Modeling of Hardy-Weinberg Equilibrium Using Dynamic Random Networks in an ABM Framework. In International Conference on Complex Networks and Their Applications (pp. 241-250). Springer, Cham.
  • Tarazona, M., Mula, J., & Poler, R. (2023, March). Optimisation of Production Scheduling and Sequencing Problems in Industry 4.0. In IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización (pp. 107-112). Cham: Springer International Publishing.
  • Tashtoush, B., Alyahya, W. E., Al Ghadi, M., Al-Omari, J., & Morosuk, T. (2023). Renewable energy integration in water desalination: State-of-the-art review and comparative analysis. Applied Energy, 352, 121950.
  • Tauböck, S., Schöfecker, A., Ledermüller, K., Krakovsky, M., Sharma, S., Reismann, M., ... & Wurzer, G. (2023). PASSt–Predictive Analytics Services für Studienerfolgsmanagement. Zeitschrift für Hochschulentwicklung, 18(Sonderheft Hochschullehre), 251-277.
  • Tedeschi, L. O. (2023). The prevailing mathematical modelling classifications and paradigms to support the advancement of sustainable animal production. animal, 100813.
  • Tian, Y., Zhao, Y., Zhang, X., Li, S., & Wu, H. Incorporating Carbon Sequestration into Lake Management: A New Perspective on Climate Change. Science of The Total Environment, 895, 164939.
  • Tian, Z. (2023, October). Multi-Stage Vertex-Centric Programming for Agent-Based Simulations. In Proceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE'23), October 22--23, 2023, Cascais, Portugal.
  • Tian, Z., Lindner, P., Nissl, M., Koch, C., & Tannen, V. (2023). Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations. Proceedings of the ACM on Management of Data, 1(2), 1-28.
  • Tittonell, P. (2023). Trade-Offs Around Production and Livelihood Decisions. In A Systems Approach to Agroecology (pp. 317-353). Cham: Springer Nature Switzerland.
  • Tomás, V. R., García, L. A., & Alonso, A. L. (2023). An agent-based platform to evaluate V2X routing road traffic scenarios. Simulation Modelling Practice and Theory, 102750.
  • Trček, D. (2023). Trust Management Methodology and Agents Simulations Framework for Conflict Research. Advanced Theory and Simulations, 2200705.
  • Trinh, T. T., & Munro, A. (2023). Integrating a choice experiment into an agent-based model to simulate climate-change induced migration: The case of the Mekong River Delta, Vietnam. Journal of Choice Modelling, 48, 100428.
  • Trivedi, A., Pandey, M., Ramesh, G., & Chhabra, R. (2023). An agent based modeling approach to evaluate crowd movement strategies and density at bathing areas during Kumbh Mela-2019. Multimedia Tools and Applications, 1-39.
  • Tshakwanda, P. M., Arzo, S. T., & Devetsikiotis, M. (2023, March). Multi-agent-based simulation of intelligent network system. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0813-0819). IEEE.
  • Tsurushima, A. (2023, January). Efficient Visual Sign Assignment for Crowd Evacuation Guidance Considering Risks and Multiple Objectives. In Agents and Artificial Intelligence: 14th International Conference, ICAART 2022, Virtual Event, February 3–5, 2022, Revised Selected Papers (pp. 3-26). Cham: Springer International Publishing.
  • Tyagi, H., Kumar, R., & Pandey, S. K. (2023). A detailed study on trust management techniques for security and privacy in IoT: Challenges, trends, and research directions. High-Confidence Computing, 100127.
  • Udayakumar, R., Kalam, M. A., Sugumar, R., & Elankavi, R. (2023). Assessing Learning Behaviors Using Gaussian Hybrid Fuzzy Clustering (GHFC) in Special Education Classrooms. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 14(1), 118-125.
  • Uhrmacher, A. M., Frazier, P., Hähnle, R., Klügl, F., Lorig, F., Ludäscher, B., ... & Wilsdorf, P. (2023). 4.3 Context, composition, automation and communication: towards sustainable simulation studies. Computer Science Methods for Effective and Sustainable Simulation Studies, 30(4), 53.
  • Umlauft, M., Gojkovic, M., Harshina, K., & Schranz, M. (2023). Bottom-Up Bio-Inspired Algorithms for Optimizing Industrial Plants. In ICAART (1) (pp. 59-70).
  • Urbane, L. M., Chen, C., Lindell, M., & Wang, H. (2023). Which Mode Should I Choose to Evacuate: Analyze and Synthesize Case Studies of Rapid-Onset Disasters. In International Conference on Transportation and Development 2023 (pp. 72-83).
  • Uslu, B. Ç. (2023). IoT uygulamalarında çok etmenli sistemlerin (MAS) birlikte çalışabilirliğinin rolü: Üretim sistemlerindeki son gelişmeler üzerine bir araştırma. Journal of the Faculty of Engineering & Architecture of Gazi University/Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi,, 38(2).
  • Uthpala, N., Hansika, N., Dissanayaka, S., Tennakoon, K., Dharmarathne, S., Vidanarachchi, R., ... & Herath, D. (2023). Analyzing transportation mode interactions using agent-based models. SN Applied Sciences, 5(12), 357.
  • Vargas, L. F. C., & Marmolejo-Saucedo, J. A. (2023, September). A Strategy to Analyze the Metal Packaging Market in the Food Cans Industry Using Agent-Based Simulation. In Computer Science and Engineering in Health Services: 6th EAI International Conference Proceedings, COMPSE 2022, Mexico City, July 28, 2022 (p. 109). Springer Nature.
  • Vargas-Pérez, V. A., Chica, M., Leon, C. J., & Cordon, O. Understanding the Impact of Climate Change in European Island Tourism by Agent-Based Simulations. Available at SSRN 4600317.
  • Vasquez, S., Barquero, B., & Bosch, M. (2023, July). Modelling COVID-19 data with simulations: A recursive process. In Thirteenth Congress of the European Society for Research in Mathematics Education (CERME13) (No. 42). Alfréd Rényi Institute of Mathematics; ERME.
  • Veloso, P., & Krishnamurti, R. (2023). Spatial synthesis for architectural design as an interactive simulation with multiple agents. Automation in Construction, 154, 104997.
  • Verma, P., Gupta, A., Kumar, M., & Gill, S. S. (2023). FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease. Internet of Things, 100828.
  • Viaene, K. P., De Schamphelaere, K. A., & Van Sprang, P. (2023). Extrapolation of Metal Toxicity Data for the Rotifer Brachionus calyciflorus Using an Individual‐Based Population Model. Environmental Toxicology and Chemistry.
  • Villa-Enciso, E., Ruiz-Castañeda, W., & Robledo Velásquez, J. (2023). Agent-Based Model to Analyze the Role of the University in Reducing Social Exclusion. Sustainability, 15(16), 12666.
  • Villa Enciso, E. M. El rol de la universidad en la innovación inclusiva: análisis desde el modelado y simulación computacional (Doctoral dissertation, Universidad Nacional de Colombia).
  • Villavicencio-Valero, K., Ramírez-Juidias, E., Madueño-Luna, A., Madueño-Luna, J. M., & López-Gordillo, M. C. (2023). Influence of the Surface Temperature Evolution over Organic and Inorganic Compounds on Iapetus. Universe, 9(9), 403.
  • Villamor, G. B. (2023). Gender and Water-Energy-Food Nexus in the Rural Highlands of Ethiopia: Where Are the Trade-Offs?. Land, 12(3), 585.
  • Vizanko, B., Kadinski, L., Ostfeld, A., & Berglund, E. Z. (2023). Social Distancing, Water Demand Changes, and Quality of Drinking Water During the Covid-19 Pandemic. Water Demand Changes, and Quality of Drinking Water During the Covid-19 Pandemic.
  • von Essen, M., & Lambin, E. F. (2023). Agent-Based Simulation of Land Use Governance (ABSOLUG) in Tropical Commodity Frontiers. Journal of Artificial Societies and Social Simulation, 26(1).
  • Vosoughkhosravi, S., Norouziasl, S., & Jafari, A. (2023, July). Lighting energy load prediction framework using agent-based simulation and artificial neural network models. In EC3 Conference 2023 (Vol. 4, pp. 0-0). European Council on Computing in Construction.
  • Vu, T. M., Buckley, C., Duro, J. A., Brennan, A., Epstein, J. M., & Purshouse, R. C. (2023). Can social norms explain long-term trends in alcohol use? Insights from inverse generative social science. Journal of artificial societies and social simulation: JASSS, 26(2).
  • Waight, N., Liu, X., & Whitford, M. (2023). “Like They Are Everyday Substances, You Like See Them, Hold Them, Use Them Every Day”: Students’ Understanding of Big Ideas and Macro and Submicro Chemistry Phenomena in the Context of Computer-Based Models. Research in Science Education, 1-26.
  • WALID, M., & ZAKARIA, A. B. (2023). Simulation Du Modèle De Ségrégation De Schelling (Doctoral dissertation, UNIVERSITY BBA).
  • Walzberg, J., Sethuraman, S., Ghosh, T., Uekert, T., & Carpenter, A. (2023). Think before you throw! An analysis of behavioral interventions targeting PET bottle recycling in the United States. Energy Research & Social Science, 100, 103116.
  • Wang, B., & Liao, X. (2023). A trusted routing mechanism for multi-attribute chain energy optimization for Industrial Internet of Things. Neural Computing and Applications, 1-11.
  • Wang, D., Wu, Z., Ma, G., Gao, Z., & Yang, Z. (2023). Coupled Control of Traffic Signal and Connected Autonomous Vehicles at Signalized Intersections. Journal of Advanced Transportation, 2023.
  • Wang, H. H., Bishop, A. E., Koralewski, T. E., & Grant, W. E. (2023). In Search of Proximate Triggers of Anthrax Outbreaks in Wildlife: A Hypothetical Individual-Based Model of Plasmid Transfer within Bacillus Communities. Diversity, 15(3), 347.
  • Wang, H., Zhang, Y., & Zhao, J. (2023). Enhancing the SVD Compression Losslessly. Journal of Computational Science, 102182.
  • Wang, J., Wu, J., Li, J., Kong, R., Li, X., & Wang, X. (2023). Simulation of various biofilm fractal morphologies by agent-based model. Colloids and Surfaces B: Biointerfaces, 113352.
  • Wang, L., Wu, J., Yang, M., Zhang, J., & Meng, Z. (2023, June). Guidance Method of Connected Autonomous Vehicles Under Automatic Control Intersections. In Proceedings of KES-STS International Symposium (pp. 35-43). Singapore: Springer Nature Singapore.
  • Wang, T., Liu, Y., Li, Q., Du, P., Zheng, X., & Gao, Q. (2023). State-of-the-Art Review of the Resilience of Urban Bridge Networks. Sustainability, 15(2), 989.
  • Wang, T., Zhang, X., Ma, Y., & Wang, Y. (2023). Risk contagion and decision-making evolution of carbon market enterprises: Comparisons with China, the United States, and the European Union. Environmental Impact Assessment Review, 99, 107036.
  • Wang, W., Wu, F., Yu, H., & Wang, X. (2023). Assessing the effectiveness of intervention policies for reclaimed water reuse in China considering multi-scenario simulations. Journal of Environmental Management, 335, 117519.
  • Wang, Y., Ge, J., & Comber, A. (2023). A pedestrian ABM in complex evacuation environments based on Bayesian Nash Equilibrium. AGILE: GIScience Series, 4, 50.
  • Wang, Y., Ge, J., & Comber, A. (2023). Navigation in Complex Space: An Bayesian Nash Equilibrium-Informed Agent-Based Model (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
  • Warr, O., Song, M., & Sherwood Lollar, B. (2023). The application of Monte Carlo modelling to quantify in situ hydrogen and associated element production in the deep subsurface. Frontiers in Earth Science, 11, 1150740.
  • Warrier, R., Boone, R., & Salerno, J. Migration land systems model: a theoretical agent-based model (Doctoral dissertation).
  • Watts, K. M., & Richardson, W. (2023). Inclusive pedagogy strategies to introduce high schoolers to systems biology. bioRxiv, 2023-03.
  • Watz, J., Schill, J., Addo, L., Piccolo, J. J., & Hajiesmaeili, M. (2023). Increased Temperature and Discharge Influence Overwinter Growth and Survival of Juvenile Salmonids in a Hydropeaking River: Simulating Effects of Climate Change Using Individual-Based Modelling. Fishes, 8(6), 323.
  • Weatherley, G., Araujo, R. P., Dando, S. J., & Jenner, A. L. (2023). Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis?. Bulletin of Mathematical Biology, 85(8), 75.
  • Weber, A. L., Ruesink, B., & Gronau, S. (2023). Dynamics of refugee settlements and energy provision: the case of forest stocks in Zambia. Journal of Economics and Development.
  • Wever, M., O'Leary, N., Shah, M., Wognum, N., & Onofrei, G. (2023). Towards a transdisciplinary framework for systemic risk detection. International Journal of Agile Systems and Management, 16(4), 458-483.
  • Will, M., Groeneveld, J., Lenel, F., Frank, K., & Müller, B. (2023). Determinants of Household Vulnerability in Networks with Formal Insurance and Informal Risk-Sharing. Ecological Economics, 212, 107921.
  • Williams, E. M., & Carley, K. M. (2023, September). Agent-Based Moral Interaction Simulations in Imbalanced Polarized Settings. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 139-148). Cham: Springer Nature Switzerland.
  • Wilson, K. M., Cole, K. E., & Codding, B. F. (2023). Identifying key socioecological factors influencing the expression of egalitarianism and inequality among foragers. Philosophical Transactions of the Royal Society B, 378(1883), 20220311.
  • Wrona, Z., Buchwald, W., Ganzha, M., Paprzycki, M., Leon, F., Noor, N., & Pal, C. V. (2023). Overview of Software Agent Platforms Available in 2023. Information, 14(6), 348.
  • Wu, J., Ohya, T., & Sekiguchi, T. (2023). Applications of agent-based modeling and simulation in organization management: a quarter-century review through bibliometric mapping (1998–2022). Computational and Mathematical Organization Theory, 1-31.
  • Xiang, W., Chen, L., Yan, X., Wang, B., & Liu, X. (2023). The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model. Cities, 135, 104238.
  • Xiangmei, W., Xiaoxiao, G., & Wang, Y. (2023). Research on the network topology characteristics of unsafe behavior propagation in coal mine group from the perspective of human factors. Resources Policy, 85, 104020.
  • Xu, B., & Lu, M. (2023). Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing. Applied Sciences, 13(6), 3703.
  • Xu, B., Lu, M., & Zhang, H. (2023). Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs. Sensors, 23(8), 3846.
  • Xu, G., Liu, X., Zhong, L., Ren, K., Lu, C., & Deng, L. (2023). Seat allocation optimization for railways considering social distancing during the post-pandemic period. Journal of Transport & Health, 33, 101691.
  • Xu, J., Wang, Y., Gomez, H., & Feng, X. Q. (2023). Biomechanical modelling of tumor growth with chemotherapeutic treatment: A review. Smart Materials and Structures.
  • Xu, X., Xu, L., & Wang, X. (2023). Study on coopetition relationship simulation among M-commerce information service subjects based on Lotka-Volterra model. Journal of Management Analytics, 1-24.
  • Xu, Y., & Wali, A. (2023). Handwritten Pattern Recognition using Birds-Flocking Inspired Data Augmentation Technique. IEEE Access.
  • Xue, F., Geng, P., Hao, H., He, Y., & Liang, H. (2023, November). A Multiple Fire Zones Detection Method for UAVs Based on Improved Ant Colony Algorithm. In International Artificial Intelligence Conference (pp. 194-208). Singapore: Springer Nature Singapore.
  • Yang, C., Yang, Z., & Li, Y. (2023). Negotiation mechanism of carbon emission quota trading process. Sustainable Production and Consumption, 39, 336-344.
  • Yang, D., Snelson, C., & Feng, S. (2023). Identifying computational thinking in students through project-based problem-solving activities. Information Discovery and Delivery, (ahead-of-print).
  • Yang, G., Cai, W., Hu, M., Li, C., & Pan, D. (2023, April). S tudy on the Influence of Exit Width Change on Heterogeneous Passengers Evacuation Based on the Social Force Model. In Bio-Inspired Computing: Theories and Applications: 17th International Conference, BIC-TA 2022, Wuhan, China, December 16–18, 2022, Revised Selected Papers (pp. 531-539). Singapore: Springer Nature Singapore.
  • Yang, J., Shiwakoti, N., & Tay, R. (2023). Exploring Melbourne Metro Train Passengers’ Pre-Boarding Behaviors and Perceptions. Sustainability, 15(15), 11564.
  • Yang, L., Han, J., Long, W., & Zhang, Y. (2023, June). Trust evaluation model for electric power mobile Internet environment based on graph and semantic time window. In Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023) (Vol. 12721, pp. 22-27). SPIE.
  • Yang, Y., & Fukuda, M. (2023). Agents Visualization and Web GUI Development in MASS Java. MS Capstone White Paper, University of Washington Bothell, Bothell, WA, 98011.
  • Yang, Y., Mao, X., Yang, S., & Wu, M. (2023). NorMASS: A normative MAS-based modeling approach for simulating incentive mechanisms of Q&A communities. Plos one, 18(2), e0281431.
  • Yao, Z., & Sun, C. (2023, March). Characteristic Analysis and Strategy Research on Synergetic Development of Construction Enterprises of Different Scales——Take Northwest China as an example. In Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, December 9-11, 2022, Chongqing, China.
  • Yasik, Y. L. (2023). Dunia Maya (Virtual World) Berbasis Agent Based Modeling-ABM untuk Pemodelan Perilaku Konsumen. Prosiding FRIMA (Festival Riset Ilmiah Manajemen dan Akuntansi), (6), 558-571.
  • Yeğenoğlu, A., Romero, C. J., Martín, A. P., van der Vlag, M., Klijn, W., Hater, T., ... & Diaz-Pier, S. Gradient free optimization of neuroscience models at different scales with L2L. meta, 1, 2.
  • Yeni, S., Grgurina, N., Saeli, M., Hermans, F., Tolboom, J., & Barendsen, E. (2023). Interdisciplinary Integration of Computational Thinking in K-12 Education: A Systematic Review. Informatics in Education.
  • YESHITLA, H. D. (2023). Development of large scale Agent Based Modeling Simulator with Microservice architecture (Doctoral dissertation, 부경대학교).
  • Yimin, F. E. N. G., Chenchu, Z. H. O. U., Qiang, Z. O. U., Yusheng, L. I. U., Jiyuan, L. Y. U., & Xinfeng, W. U. (2023). A goal-based approach for modeling and simulation of different types of system-of-systems. Journal of Systems Engineering and Electronics, 34(3), 627-640.
  • Yin, J., Wang, D., Li, H., Li, Y., & Shang, Y. Spatial Optimization of Rural Settlements in Ecologically Fragile Regions Based on a Multi-Agent Model: Evidence from Different Types of Towns. Available at SSRN 4650929.
  • Yin, C., Ji, W., Ye, T., Mao, T., & Zhang, K. (2023, December). Study on Self-Organizing Aggregation Method for Massive Flexible Resources and Its Simulation. In 2023 4th International Conference on Advanced Electrical and Energy Systems (AEES) (pp. 452-459). IEEE.
  • Ying, Y., Antfolk, J., & Santtila, P. (2023). An Agent-Based Model of Sex and Sexual Orientation Differences in Short-Term Mating Behaviors as a Result of Mating Preferences. The Journal of Sex Research, 1-9.
  • Younas, M. I., Iqbal, M. J., Aziz, A., & Sodhro, A. H. (2023). Toward QoS Monitoring in IoT Edge Devices Driven Healthcare—A Systematic Literature Review. Sensors, 23(21), 8885.
  • Yu, C., & Wellman, H. M. (2023). Where do differences in theory of mind development come from? An agent-based model of social interaction and theory of mind. Frontiers in Developmental Psychology, 1.
  • Yu, H., Li, Y., & Wang, W. (2023). Optimal innovation strategies of automakers with market competition under the dual-credit policy. Energy, 128403.
  • Yu, S. (2023). Evaluating architectural layouts for occupancy patterns and interactions using agent-based modelling as a methodology for workplace design. Automation in Construction, 155, 105025.
  • Yu, S., & Hou, Z. (2023). Melodie: Agent-based Modeling in Python. Journal of Open Source Software, 8(83), 5100.
  • Yuan, C., Yan, S., Li, C., & Zhang, H. (2023, November). Multi-agent-based emergency supplies dispatch. In Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023) (Vol. 12923, pp. 163-170). SPIE.
  • Yuguo, J., Yu, H., Ampaw, E. M., Wang, C., & Jiang, P. (2023). Innovating for a greener world: Simulating low-carbon innovation in manufacturing companies from the lens of community succession. Journal of Cleaner Production, 140053.
  • Zebua, R. S. Y. (2023). Teknik Menjaring Literatur di Berbagai Literature Databases (Vol. 1). XREI Institute.
  • Zhang, A., Zhen, Q., Zheng, C., Li, J., Zheng, Y., Du, Y., ... & Zhang, Q. (2023). Assessing the impact of architectural and behavioral interventions for controlling indoor COVID-19 infection risk: An agent-based approach. Journal of Building Engineering, 106807.
  • Zhang, C., Wu, X., Zhao, S., Madani, H., Chen, J., & Chen, Y. (2023). A dynamical system model to analyze the low carbon transition in energy-economic system. Journal of Economy and Technology, 1, 1-15.
  • Zhang, E., Jiang, H., & Zhang, X. (2023, March). A slotted and OFDM federated protocol for safety message broadcasting in VANETs. In Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022) (Vol. 12586, pp. 64-68). SPIE.
  • Zhang, H., Zhu, P., & Yao, Z. (2023). An Agent-Based Model to Simulate the Diffusion of New Energy Vehicles. Complexity, 2023.
  • Zhang, J. (2023). How do trust and decentralization impact adoption?: an agent-based model for diffusion of blockchain-based COVID-19 contact tracing apps (Doctoral dissertation, University of British Columbia).
  • Zhang, J. (2023). Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique. Journal of Advanced Transportation, 2023.
  • Zhang, J., & Zhang, J. (2023). Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective. Discrete Dynamics in Nature and Society, 2023.
  • Zhang, J., Rong, L., & Gong, Y. Check for updates Construction and Simulation of Major Infectious Disease Transmission Model Based on Individual-Place Interaction. In Knowledge and Systems Sciences: 22nd International Symposium, KSS 2023, Guangzhou, China, December 2–3, 2023, Proceedings (p. 178). Springer Nature.
  • Zhang, J., Rong, L., & Gong, Y. (2023, November). Construction and Simulation of Major Infectious Disease Transmission Model Based on Individual-Place Interaction. In International Symposium on Knowledge and Systems Sciences (pp. 178-195). Singapore: Springer Nature Singapore.
  • Zhang, Q., Qiu, T., & Fu, Y. (2023, February). 3D Model-Based Design Method for Complex Cable Network Three-Dimensional Marking of Spacecraft. In Signal and Information Processing, Networking and Computers: Proceedings of the 10th International Conference on Signal and Information Processing, Networking and Computers (ICSINC) (pp. 866-872). Singapore: Springer Nature Singapore.
  • Zhang, W. H., Yuan, Q., & Cai, H. (2023). Unravelling urban governance challenges: Objective assessment and expert insights on livability in Longgang District, Shenzhen. Ecological Indicators, 155, 110989.
  • Zhang, Y., Li, S. H., & Liu, H. Y. (2023). Simulation Research on Conflicts of Matching Persons and Posts in High-risk Construction Based on Prospect Theory. Operations Research and Management Science, 31(12), 179.
  • Zhang, Z., & Wu, Z. (2023, April). A Sunlight Duration Time Driven Multi-objective Optimization Method for the Layout of High-Rise Residential Quarters Based on NSGA2 Algorithm. In Hybrid Intelligence: Proceedings of the 4th International Conference on Computational Design and Robotic Fabrication (CDRF 2022) (pp. 138-149). Singapore: Springer Nature Singapore.
  • Zhao, J., He, J., Liu, X., & Zhang, Y. (2023, August). Evaluation and optimization of data systems. In Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023) (Vol. 12784, pp. 839-847). SPIE.
  • Zheng, G., Zhang, Y., Peng, T., Guo, C., Guo, C., & Liu, X. (2023, April). Research on Distributed Internet of Things Trusted Operation Based on Dynamic Trusted Metrics. In 2023 8th International Conference on Computer and Communication Systems (ICCCS) (pp. 689-694). IEEE.
  • Zheng, W., Cao, Y., & Tan, H. (2023). Secure sharing of industrial IoT data based on distributed trust management and trusted execution environments: a federated learning approach. Neural Computing and Applications, 1-11.
  • Zheng, X. (2023). Complex behavior of individuals and collectives in a social system: An introduction to exploratory computational experimental methodology based on multi-agent modeling. Annals of Operations Research, 1-25.
  • Zhijian, W. (2023). Nash equilibrium selection by eigenvalue control. arXiv preprint arXiv:2302.09131.
  • Zhong, X., Yang, Y., Deng, F., & Liu, G. (2023). Rumor Propagation Control With Anti-Rumor Mechanism and Intermittent Control Strategies. IEEE Transactions on Computational Social Systems, 1-13.
  • Zhu, M., Yang, G., Jiang, Y., & Wang, X. (2023). Agent-Based Modeling for Water–Energy–Food Nexus and Its Application in Ningdong Energy and Chemical Base. Sustainability, 15(14), 11428.
  • Zhu, X., Huang, J., & Qi, C. (2023). Modeling and Analysis of Malware Propagation for IoT Heterogeneous Devices. IEEE Systems Journal.
  • Zixin, L. (2023). APPLICATION OF MULTI-AGENT SYSTEM IN URBAN RENEWAL DESIGN (Doctoral dissertation, University of Pécs).
  • Zixin, L., Várady, G., & Zagorácz, M. B. (2023). Multi-agent simulation of pedestrian activity in historic district. Pollack Periodica, 18(2), 137-143.
  • Zohar, A. R., & Levy, S. T. Teaching Molecules How to React: Middle School Students’ Learning through Computational Modeling of Chemical Reactions Using MMM Platform. Proceedings of the 18th Chais Conference for the Study of Innovation and Learning Technologies: Learning in the Digital Era, 18E-24E.
  • Zhou, Y., Cao, J., & Zhang, Y. (2023). Research on Network Security Situation Awareness based on Netlogo.
  • Zou, F., Jiang, H., Che, E., Wang, J., & Wu, X. (2023). Quantitative evaluation of emergency shelters in mountainous areas among multiple scenarios: Evidence from Biancheng, China. International Journal of Disaster Risk Reduction, 103665.
  • Zouhri, S., & El Baroudi, M. (2023). Mathematical formalism for agent-based model of proteins interaction inside cancer cell. Commun. Math. Biol. Neurosci., 2023, Article-ID.
  • 우선희. (2023). 노인요양시설 화재위험평가를 통한 인명 안전성 확보 방안 연구 (Doctoral dissertation, 부경대학교).
  • 주럴러, & 이향숙. (2023). 경쟁우위 관점에서 본 전자상거래 플랫폼에서의 SCF 적용에 관한 연구. Journal of the Korean Society of Supply Chain Management ISSN, 23(1), 39-54.
  • 김민수, 이지환, & 김영진. (2023). 대규모 인구동태 시뮬레이션을 통한 에이전트 기반 고성능 시뮬레이터의 구현에 관한 연구. 한국전자거래학회지, 28(1), 109-121.
  • 조성진, 최희정, 임종서, 이혜영, & 김선미. (2023). 해양공간계획 이행을 위한 행위자기반 시뮬레이션 활용방안 연구-정책 현안 및 기술 수요 분석을 중심으로. 해양정책연구, 38(1), 153-179.
  • 浅野俊幸. (2023). シミュレーション教育のための NetLogo の利用. 湘南工科大学紀要, 57(1), 27-31.
  • 名倉卓弥, & 秋山英三. (2023). SNS におけるトピックス数の増加が意見の分極化とエコーチェンバーに与える影響. 人工知能学会論文誌, 38(4), B-N11_1.
  • 森田裕之, 西口真央, 白井康之, & 後藤裕介. (2023). タクシープローブデータを用いたスカイタクシー実用化における空路設定問題に関する研究. 経済研究 The Journal of Economic Studies/大阪府立大学経済学研究科 編, 68(1-4), 1-36.
  • 刘家霖, 张显玉, & 庞达. (2023). 基于达尔文演化动力学的适应性治疗策略在肿瘤治疗中的研究进展. 中国癌症杂志, 33(4), 397-402.
  • 齋藤美紀, 阿部健太, & 林久志. (2023). P2P 型企業間人材共有 PF におけるリソース・トークンの偏りの対処法: 銀行方式. 人工知能学会第二種研究会資料, 2023(SAI-048), 02.
  • 石云, 朱晓雯, 李建华, 马小燕, 赵娜, & 佘洁. (2023). 基于多智能体的黄土高原沟壑区农村居民点优化布局. 经济地理, 43(7), 170-178.
  • 刘舫, 吕天, 刘心阁, 叶盛, 郭锐, 张烈, ... & 刘永进. (2023). Research on the Construction and Interactive Feedback of Online Exhibition Hall with the Introduction of Intelligent Virtual Agent. Journal of Software, 1-18.
  • 安井一真, 宇都宮陽一, & 奥田隆史. (2023). 足の速さによる有利不利を小さくする鬼ごっこルールの検証. 電気学会論文誌 C (電子・情報・システム部門誌), 143(12), 1145-1153.
  • 刘捷, 王曈, 孙恒飞, & 沐波. (2023). 考虑驾驶员个性的博弈协商机制与出行路径选择研究. 交通运输研究, 9(1), 86.
  • 杨鹤林. (2023). 促进开放科学: 国外高校图书馆 2022 年 “爱数据周” 活动分析与启示. Journal of Academic Libraries, 41(3).
  • 櫻井祐子. (2023). 私のブックマーク: 人工知能とゲーム理論. 人工知能, 38(5), 750-756.
  • 薛领, 彭志斌, & 赵威. (2023). 空间集聚和知识溢出的微观机理与动态模拟. 复杂系统与复杂性科学, 20(1), 18-26.
  • 沈颂, & 沈国峰. (2023). 一种应对 COVID-19 疫情的社会接触自动检测方法. Application Research of Computers/Jisuanji Yingyong Yanjiu, 40(4).
  • 项凤涛, 苏炯铭, 谷学强, & 张万鹏 (2023). 基于智能体建模的新冠肺炎疫情传播问题研究. 智能科学与技术学报, 5(1), 51-57.
  • 黄秋怡, 郑小平, & 王瑞梅 (2023). 农村交通基础设施改善能够缓解农业要素错配吗?. 中国农业大学学报, 28(3), 279-292.
  • נורית ברגר-גיל .(2023) למידת מושגים בכימיה באמצעות בניית מודלים חישוביים ומעוגני גוף בגישת מערכות מורכבות (Doctoral dissertation, University of Haifa, Israel).
  • Безбородова, О. Е. (2023). ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ ВЗАИМОДЕЙСТВИЯ ЧЕЛОВЕКА И ОБЪЕКТА ТЕХНОСФЕРЫ В ИНФОРМАЦИОННО-ИЗМЕРИТЕЛЬНЫХ И УПРАВЛЯЮЩИХ СИСТЕМАХ ОБЕСПЕЧЕНИЯ ЭКОЛОГИЧЕСКОГО БЛАГОПОЛУЧИЯ ЧЕЛОВЕКА. Модели, системы, сети в экономике, технике, природе и обществе, (1 (45)), 164-177.
  • Макареня, Т. А., Маннаа, А. С., Калиниченко, А. И., & Петренко, С. В. (2023). Когнитивное моделирование социально-экономических систем: ретроспективный анализ инструментов и информационных систем. Вестник ВГУ. Серия: Системный анализ и информационные технологии, (3), 84-94.
  • Алексеева, Е. А. (2023). Проекты компьютерной эпистемологии. Философия науки и техники Philosophy of Science and Technology, 28(2), 88-101.
  • Μπενίση, Α., Γκιόλμας, Α., Στούμπα, Α., Χαλκίδης, Ά., Μπόικος, Η., Ψωμά, Β., ... & Παπαναγιώτου, Α. Τ. (2023). Εξελιγμένη μορφή του μοντέλου της NetLοgo για τη φωτιά στο δάσος: Μία διδακτική προσέγγιση σε μαθητές Δημοτικού. 13o Πανελλήνιο Συνέδριο της Διδακτικής των Φυσικών Επιστημών και Νέων Τεχνολογιών στην Εκπαίδευση, 13.
  • Каталевский, Д. (2023). Новые управленческие подходы для предотвращения краха сложных социальноэкономических систем. Форсайт, 17(3), 56-67.
  • Белолуцкая, А. К., Вачкова, С. Н., & Патаракин, Е. Д. Связь цифрового компонента обучения и развития детей дошкольного и школьного возраста: обзор исследований и международных образовательных практик, 18(2), 37-55.
  • Гнатчук, Є. Г., Засорнова, І. О., & Рей, К. С. (2023). СИСТЕМА ПІдТРИМКИ ПРИйНяТТя РІшЕНь ПРО МОЖлИВІСТь ВАКцИНАцІЇ ВІд COVID-19. ВЧЕНІ ЗАПИСКИ, 1202376.
  • Тимофеев, Г. А. (2023). Поиск подходящей архитектуры для разработки цифрового двойника гибридных энергетических систем в изолированных от сетевых энергосистем средах с использованием ТРИЗ-эволюционного подхода. Вестник НГУ. Серия: Информационные технологии, 20(4), 76-99.
  • Антонова, Е. М (2023). Медицинская информатика: влияние времени, проблемы и возможные пути решения. ЦИТИСЭ, 3, 18-27.
  • АНДРИАНОВА, Л., ПАВЛОВА, З., ХАКИМЬЯНОВ, М., & ХАЗИЕВА, Р. ИНФОРМАТИВНОЕ ПРЕДСТАВЛЕНИЕ ДИСЦИПЛИНЫ «МУЛЬТИАГЕНТНЫЕ СИСТЕМЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА» В ВОПРОСАХ И ОТВЕТАХ. Международный центр научного партнерства" Новая Наука" КОНФЕРЕНЦИЯ: ПРЕПОДАВАТЕЛЬ ГОДА 2023 Петрозаводск, 13 декабря 2023 года Организаторы: Международный центр научного партнерства" Новая Наука".
  • Дудко, В. В., & Патаракин, Е. Д. (2023). Исследование научных школ университета средствами библиометрического картирования. Территория новых возможностей. Вестник Владивостокского государственного университета экономики и сервиса, 15(1 (65)), 150-167.
  • Шепель, А. С. ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ. ТЕОРИЯ И ПРАКТИКА (ИММОД-2023). Издательство АН РТ КОНФЕРЕНЦИЯ: ОДИННАДЦАТАЯ ВСЕРОССИЙСКАЯ НАУЧНО-ПРАКТИЧЕСКАЯ КОНФЕРЕНЦИЯ ПО ИМИТАЦИОННОМУ МОДЕЛИРОВАНИЮ И ЕГО ПРИМЕНЕНИЮ В НАУКЕ И ПРОМЫШЛЕННОСТИ «ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ. ТЕОРИЯ И ПРАКТИКА» ИММОД-2023 Казань, 18–20 октября 2023 года Организаторы: Казанский государственный энергетический университет.
  • ودادی کلانتر, سیف الدین, & امیرعلی. (2023). سیاست پژوهی بحران خاموشی: مدل‌سازی تصادفیِ عامل پایه مصرف برق در شهر تهران. نشریه انرژی ایران, 25(4), 55-80.
  • رمش ناصر القحطاني, & أحمد زيد آل مسعد. (2023). واقع تدريس مهارات التفكير الحوسبي من وجهة نظر معلمات الحاسب بمدينة الرياض. مجلة المناهج وطرق التدريس, 2(2), 82-106.

2022

  • Abhishek, B., & Hirve, S. (2022). Overview of Social Network Analysis and Different Graph File Formats. Social Network Analysis: Theory and Applications, 1-18.
  • Abrahamson, D. (2022). Enactive perception as mathematics learning. In M.-C. Shanahan, B. Kim, M. A. Takeuchi, K. Koh, A. P. Preciado-Babb, & P. Sengupta (Eds.), The Learning Sciences in conversation: Theories, methodologies, and boundary spaces (pp. 153–170). Routledge.
  • Abrahamson, D., Dutton, E., & Bakker, A. (2022).Towards an enactivist mathematics pedagogy. In S. A. Stolz (Ed.), The body, embodiment, and education: An interdisciplinary approach (pp. 156–182). Routledge.
  • Abrahamson, D., & Mechsner, F. (2022). Toward synergizing educational research and movement sciences: A dialogue on learning as developing perception for action. Educational Psychology Review. https://doi.org/0.1007/s10648-022-09668-3
  • Accolla, C., Schmolke, A., Jacobson, A., Roy, C., Forbes, V. E., Brain, R., & Galic, N. TRACE Document. Ecology and Evolution, 25, 479-486.
  • Adam, C., & Arduin, H. (2022, May). Finding and explaining optimal screening strategies with limited tests during the COVID-19 epidemics. In 19th International Conference on Information Systems for Crisis Response and Management ISCRAM.
  • Adebayo, S. A., Sathasiva, S., & Ali, M. K. M. (2022). HornSAT Solver Using Agent-Based Modelling in Hopfield Network. In Intelligent Systems Modeling and Simulation II (pp. 251-263). Springer, Cham.
  • Adeolu, A. (2022). Learning Computational Thinking Practices Through Agent-Based Modeling in an Informal Setting. Journal of Research in Science Mathematics and Technology Education, 17-39.
  • Agyemang, F. S., Silva, E., & Fox, S. (2022). Modelling and simulating ‘informal urbanization’: An integrated agent-based and cellular automata model of urban residential growth in Ghana. Environment and Planning B: Urban Analytics and City Science, 23998083211068843.
  • Aitken, S. (2022). An exploration of local rules to map spawning processes to regular hardware architectures (Doctoral dissertation, University of York).
  • Akhatova, A., Kranzl, L., Schipfer, F., & Heendeniya, C. B. (2022). Agent-Based Modelling of Urban District Energy System Decarbonisation—A Systematic Literature Review. Energies 2022, 15, 554.
  • Akhtar, S. M., Nazir, M., Saleem, K., Ahmad, R. Z., & Javed, A. R. (2022). S. Band S and Mosavi A (2022) A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems. Front. Public Health, 10, 849185.
  • Aktas, M., & Wolf, S. M. (2022). Diagnostische Fragen zur Zwei-und Mehrsprachigkeit bei Kindern mit kognitiven Beeinträchtigungen. Zwei-und Mehrsprachigkeit bei Kindern mit kognitiven Beeinträchtigungen, 27.
  • Alam, A., & Khurshid, F. (2022). Teachers’ Knowledge of ICT and e-learning in Pakistan: The wave of e-learning during COVID-19. International Research Journal of Education and Innovation, 3(1), 34-46.
  • Alhady, S. S. N. (2022). Simulating Solitary Foraging Behaviour of Chimpanzee in Hunting Red Colobus Monkeys Using Agent-Based Modelling Approach. Intelligent Manufacturing and Mechatronics: Proceedings of SympoSIMM 2021, 387.
  • Alkhatib, A. A., Abu Maria, K., Alzu'bi, S., & Abu Maria, E. (2022). Novel system for road traffic optimisation in large cities. IET Smart Cities.
  • Alkhatib, A. A., Maria, K. A., AlZu'bi, S., & Maria, E. A. (2022). Smart Traffic Scheduling for Crowded Cities Road Networks. Egyptian Informatics Journal.
  • Alajlan, A. (2022). Predicting Human Movement in Crowds (Doctoral dissertation, University of Idaho).
  • Alami, K. COVID-19 IMPACT ON TUCSON FIRE DEPARTMENT RESOURCES (Doctoral dissertation, UNIVERSITY OF ARIZONA).
  • Al Ghamdi, M. A. (2022). A Novel Approach to Printed Arabic Optical Character Recognition. Arabian Journal for Science and Engineering, 47(2), 2219-2237.
  • Alencar de Paula, R. (2022). Contributions to Continuous Improvement Management Using its Variables to Measure Productivity, Employee Engagement and Participatory Management (Doctoral dissertation, Polytechnique Montréal).
  • Alexander, S., & Block, P. (2022). Integration of seasonal precipitation forecast information into local-level agricultural decision-making using an agent-based model to support community adaptation. Climate Risk Management, 100417.
  • Ali Kumar, D. S. N. K. P., Shah Newaz, S. H., Rahman, F. H., Lee, G. M., Karmakar, G., & Au, T. W. Green Demand Aware Fog Computing: A Prediction-based Dynamic Resource Provisioning Approach. Electronics.
  • Ali, A., & Farooqui, M. F. (2022, May). Interaction among Multiple Intelligent Agent Systems in web mining. In 2022 3rd International Conference for Emerging Technology (INCET) (pp. 1-8). IEEE.
  • Alkhatib, A. A., Abu Maria, K., Alzu'bi, S., & Abu Maria, E. (2022). Novel system for road traffic optimisation in large cities. IET Smart Cities.
  • Alkhatib, A. A., Maria, K. A., AlZu'bi, S., & Maria, E. A. (2022). Smart Traffic Scheduling for Crowded Cities Road Networks. Egyptian Informatics Journal.
  • Allahmoradi, E., Mirzamohammadi, S., Bonyadi Naeini, A., Maleki, A., Mobayen, S., & Skruch, P. (2022). Policy Instruments for the Improvement of Customers’ Willingness to Purchase Electric Vehicles: A Case Study in Iran. Energies, 15(12), 4269.
  • Almaguer-Sustegui, D. S., Islas-Moreno, C., Padilla-Longoria, P., Prado-Zäyago, M. A., & Vizuet-Morales, D. F. (2022). Manejo biológico de una plaga usando un modelo multiagentes. Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI, 10(Especial), 140-146.
  • Almeida, F. P. S. (2022). Predictive long-term asset maintenance strategy: development of a fuzzy logic condition-based control system (Doctoral dissertation).
  • AlMuhaideb, S., Touir, A., Alshraihi, R., Altwaijry, N., & Qasem, S. (2022). Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem. Applied Sciences, 12(7), 3630.
  • Alobeidyeen, A., & Du, L. Information Dissemination Dynamics Through Vehicle-to-Vehicle Communication Built Upon Traffic Flow Dynamics Over Roadway Networks. Available at SSRN 4077909.
  • Alsammak, I. L. H., Mahmoud, M. A., Aris, H., AlKilabi, M., & Mahdi, M. N. (2022). The Use of Swarms of Unmanned Aerial Vehicles in Mitigating Area Coverage Challenges of Forest-Fire-Extinguishing Activities: A Systematic Literature Review. Forests, 13(5), 811.
  • Altamimi, M. (2022). Big Data in E-government: Classification and Prediction using Machine Learning Algorithms. Iraqi Journal of Intelligent Computing and Informatics (IJICI), 1(2), 41-55.
  • Alvarado, V., Hsu, S. C., Wu, Z., Zhuang, H., Lee, P. H., & Guest, J. S. (2022). Roadmap from Microbial Communities to Individuality Modeling for Anaerobic Digestion of Sewage Sludge. Environmental Science & Technology.
  • Álvarez Arce, J. R. (2022). Modelización basada en el individuo de sistemas de tratamiento biológico en biopilas de suelos contaminados por hidrocarburos de petróleo (Bachelor's thesis, Quito: UCE).
  • Alves, F., Rocha, A. M. A., Pereira, A. I., & Leitão, P. Conceptual Multi-Agent System Design for Distributed Scheduling Systems. In Smart and Sustainable Manufacturing Systems for Industry 4.0 (pp. 129-148). CRC Press.
  • Alzaeemi, S. A., Sathasivam, S., Velavan, M., & Mamat, M. Agent-based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network. International Journal of Engineering and Advanced Technology (IJEAT), 9(4), 1872-1879.
  • Al-Janabi, S., Alkaim, A., & Rahem, A. (2022). An alternative technique to reduce time, cost and human effort during natural or manufactured disasters. IAES International Journal of Robotics and Automation, 11(1), 10.
  • Al-Shaery, A. M., Hejase, B., Tridane, A., Farooqi, N. S., & Al Jassmi, H. (2022). Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities. Scientific Reports, 12(1), 1-9.
  • Amadae, S. M., & Watts, C. J. (2022). Red Queen and Red King Effects in cultural agent-based modeling: Hawk Dove Binary and Systemic Discrimination. The Journal of Mathematical Sociology, 1-28.
  • Ambrosius, F. H., Kramer, M. R., Spiegel, A., Bokkers, E. A., Bock, B. B., & Hofstede, G. J. (2022). Diffusion of organic farming among Dutch pig farmers: An agent-based model. Agricultural Systems, 197, 103336.
  • Ampatzidis, G., & Armeni, A. (2022). Designing a learning environment to teach about COVID-19/Σχεδιασμός ενός μαθησιακού περιβάλλοντος διδασκαλίας για τη νόσο COVID-19. Διεθνές Συνέδριο για την Ανοικτή & εξ Αποστάσεως Εκπαίδευση, 11(8Β), 169-175.
  • Amparore, E. G. (2022). Stochastic modelling and evaluation using GreatSPN. ACM SIGMETRICS Performance Evaluation Review, 49(4), 87-91.
  • Anceaume, E., Djari, A., & Tucci-Piergiovanni, S. (2022). An agent-based simulation study of Sycomore++, a scalable and self-adapting graph-based permissionless distributed ledger.
  • Andringa, S. P., & Yorke-Smith, N. (2022). Flexible Enterprise Optimization with Constraint Programming. In Enterprise Engineering Working Conference (pp. 58-73). Springer, Cham
  • Angione, C., Silverman, E., & Yaneske, E. (2022). Using machine learning as a surrogate model for agent-based simulations. PLOS ONE, 17(2), e0263150.
  • Angourakis, A., Bates, J., Baudouin, J. P., Giesche, A., Walker, J. R., Ustunkaya, M. C., ... & Petrie, C. A. (2022). Weather, land and crops in the Indus Village model: A simulation framework for crop dynamics under environmental variability and climate change in the Indus Civilisation. Quaternary, 5(2), 25.
  • Antelmi, A., Cordasco, G., D’Ambrosio, G., De Vinco, D., & Spagnuolo, C. (2023). Experimenting with Agent-Based Model Simulation Tools. Applied Sciences, 13(1), 13.
  • Anubhuti, Kaur, H. (2023). Role of Multi-agent Systems in Health Care: A Review. Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-19-4052-1_37
  • Arbelaez-Velasquez, C. A., Giraldo, D., & Quintero, S. (2022). Analysis of a Teleworking Technology Adoption Case: An Agent-Based Model. Sustainability, 14(16), 9930.
  • Arima, Y. (2022). Psychology of Group and Collective Intelligence. Springer Nature.
  • Asgarpour, S., Hartmann, A., Augustijn, E. W. P., & Dorée, A. (2022). The Other Side of the Interdependency Coin: Identifying Coordination and Investment Opportunities for Infrastructure Systems. Journal of Infrastructure Systems, 28(2), 04022011.
  • Asif, M. J. (2022). Zakat Charity and Wealth Distribution: An Agent Based Computational Model. International Journal of Zakat, 7(1), 63-74.
  • Assaraf, O. B. Z., & Knippels, M. C. P. (2022). Lessons Learned: Synthesizing Approaches That Foster Understanding of Complex Biological Phenomena. Fostering Understanding of Complex Systems in Biology Education: Pedagogies, Guidelines and Insights from Classroom-based Research, 249.
  • Avil, M. G., Taghipourian, M. J., Farrokhseresht, B., & Aghajani, H. (2022). Analysis of Rumor Management in the Context of Social Networks with a Meta-Combined Method from the Perspective of Islamic Concepts. Health, 5(4), 173-191.
  • Avila-Garzon, C., Balaguera, M., & Tabares-Morales, V. (2022). An Agent-Based Social Simulation for Citizenship Competences and Conflict Resolution Styles. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-23.
  • Axelrod, D. E. (2022). Chronotherapy of Early Colon Cancer: Advantage of Morning Dose Schedules. Cancer Informatics, 21, 11769351211067697.
  • AYBUĞA, K., & IŞILDAR, A. G. Y. (2022). Agent-Based Approach on Water Resources Management: A Modified Systematic Review. Turkish Journal of Water Science and Management, 6(2), 202-236.
  • Azadi, F. (2022). Comprehensive Arterial Traffic Control for Fully Automated and Connected Vehicles (Doctoral dissertation, University of Pittsburgh).
  • Azadi, F., Mitrovic, N., & Stevanovic, A. Z. (2022). Combined flexible lane assignment and reservation-based intersection control in field-like traffic conditions. Transportmetrica A: Transport Science, 1-36.
  • Azarov, I., Helmlinger, G., Kosinsky, Y., & Peskov, K. (2022). Elaborating on anti CTLA-4 mechanisms of action using an agent-based modeling approach. Frontiers in Applied Mathematics and Statistics, 8, 993581.
  • Azizi, K. (2022). Application of Local Knowledge for Better Characterization and Modeling of Urban Pluvial Flooding (Doctoral dissertation, The University of Memphis).
  • Backhaus, A. E., Lister, A., Tomkins, M., Adamski, N. M., Simmonds, J., Macaulay, I., ... & Uauy, C. (2022). High expression of the MADS-box gene VRT2 increases the number of rudimentary basal spikelets in wheat. Plant Physiology.
  • Baden-Böhm, F., Thiele, J., & Dauber, J. (2022). Response of honeybee colony size to flower strips in agricultural landscapes depends on areal proportion, spatial distribution and plant composition. Basic and Applied Ecology.
  • Bahrami, N., Sadr, S. M. K., Afshar, A., & Afshar, M. H. (2022). Application of Agent Based Models as a Powerful Tool in the Field of Water Resources Management. In Computational Intelligence for Water and Environmental Sciences (pp. 491-506). Springer, Singapore.
  • Baktash, A., Huang, A., de la Mora Velasco, E., Jahromi, M. F., & Bahja, F. (2022). Agent-based modelling for tourism research. Current Issues in Tourism, 1-13.
  • Bamaqa, A., Sedky, M., Bosakowski, T., Bastaki, B. B., & Alshammari, N. O. (2022). SIMCD: SIMulated Crowd Data for Anomaly Detection and Prediction. Expert Systems with Applications, 117475.
  • Barbierato, L., Rando Mazzarino, P., Montarolo, M., Macii, A., Patti, E., & Bottaccioli, L. (2022). A comparison study of co-simulation frameworks for multi-energy systems: the scalability problem. Energy Informatics, 5(4), 1-26.
  • Barelli, E., & Levrini, O. (2022). Computational simulations at the interface of physics and society: A teaching-learning module for high-school students. Il nuovo cimento C, 45(6), 1-4.
  • Barnett, T., Valdez-Tullett, J., & Bjerketvedt, L. M. (2022). Close encounters: visibility and accessibility of Atlantic rock art in Scotland. Abstractions Based on Circles: Papers on prehistoric rock art presented to Stan Beckensall on his 90th birthday, 63.
  • Barnett-Neefs C, Sullivan G, Zoellner C, Wiedmann M, Ivanek R (2022). Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses. PLOS ONE 17(3): e0265251. https://doi.org/10.1371/journal.pone.0265251
  • Barnett-Neefs, C., Wiedmann, M., & Ivanek, R. (2022). Examining Patterns of Persistent Listeria Contamination in Packinghouses Using Agent-Based Models. Journal of Food Protection, 85(12), 1824–1841. https://doi.org/10.4315/JFP-22-119
  • Barnes, B., Dunn, S., & Wilkinson, S. (2022). Replicating capacity and congestion in microscale agent-based simulations. Travel Behaviour and Society, 29, 308-318.
  • Barnes, E. M. (2022). Realistic evacuation simulation through micro and macro scale agent-based modelling including demographics, agent patience and evacuation route capacities (Doctoral dissertation, Newcastle University).
  • Barrón-Estrada, M. L., Zatarain-Cabada, R., Romero-Polo, J. A., & Monroy, J. N. (2022). Patrony: A mobile application for pattern recognition learning. Education and Information Technologies, 27(1), 1237-1260.
  • Basha, S. M., Neto, A. V. L., Alshathri, S., Abd Elaziz, M., Mohisin, S. H., & De Albuquerque, V. H. C. (2022). Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia. Computational Intelligence and Neuroscience, 2022.
  • Bayley, T. (2022). A Quantitative Exploration of the Mechanisms Relating Obesity, Depression and Socioeconomic Position (Doctoral dissertation, University of Sheffield).
  • Bearss, E. M., Cantrell, W. A., Hall, C. W., Pinckard, J. E., & Petty, M. D. (2022, April). Wolf sheep predation: reimplementing a predator-prey ecosystem model as an instructional exercise in agent-based modeling. In Proceedings of the 2022 ACM Southeast Conference (pp. 38-43).
  • Beernink, S., Bloemendal, M., Kleinlugtenbelt, R., & Hartog, N. (2022). Maximizing the use of aquifer thermal energy storage systems in urban areas: effects on individual system primary energy use and overall GHG emissions. Applied Energy, 311, 118587.
  • Bekker, R. A., Kim, S., Pilon-Thomas, S., & Enderling, H. (2022). Mathematical modeling of radiotherapy and its impact on tumor interactions with the immune system. Neoplasia, 28, 100796.
  • Belda, A., Giancola, E., Williams, K., Dabirian, S., Jradi, M., Volpe, R., ... & Eicker, U. (2022). Reviewing challenges and limitations of energy modelling software in the assessment of PEDs using case studies. In Sustainability in Energy and Buildings 2021 (pp. 465-477). Springer, Singapore.
  • Bellaj, B., Ouaddah, A., Bertin, E., Crespi, N., Mezrioui, A., & Bellaj, K. (2022). BTrust: A New Blockchain-Based Trust Management Protocol for Resource Sharing. Journal of Network and Systems Management, 30(4), 1-31.
  • Ben Zvi Assaraf, O., & Knippels, M. C. P. (2022). Lessons Learned: Synthesizing Approaches That Foster Understanding of Complex Biological Phenomena. In Fostering Understanding of Complex Systems in Biology Education (pp. 249-278). Springer, Cham.
  • Benally, J., Palatnik, A., Ryokai, K., & Abrahamson, D. (2022). Learning through negotiating conceptually generative perspectival complementarities: The case of geometry. For the Learning of Mathematics, 42(3), 34–41.
  • Benitez Castillo, J. P. (2022). Simulación basada en el individuo utilizando NetLogo de un sistema en serie de reactores CSTR de lodos activados (Bachelor's thesis, Quito: UCE).
  • Benmir, M., Chabbar, S., Aboulaich, R., & Ismaili, N. (2022, October). A Hybrid Model of Tumor Growth Under Chemotherapy Medicine. In Colloque Africain sur la Recherche en Informatique et en Mathématiques Appliquées (CARI).
  • Bennai, M. T. (2022). Development of a self-adaptive multi-agent system for medical image processing (Doctoral dissertation, Université M'hamad Bougara: Faculté des Sciences).
  • Bennett, M. R. (2022). Climate Change Is Normal. In Our Dynamic Earth: A Primer (pp. 51-82). Springer, Cham.
  • Berceanu, C., & Patrascu, M. (2022). Initial Conditions Sensitivity Analysis of a Two-Species Butterfly-Effect Agent-Based Model. In European Conference on Multi-Agent Systems (pp. 60-78). Springer, Cham.
  • Berea, A., Liu, R., & Santiago, M. F. (2022). Universal constraints to life derived from artificial agents and games. In New Frontiers in Astrobiology (pp. 305-317). Elsevier.
  • Bernard, J. (2022). Building the Bio-CS Bridge: Expanding High School Curriculum that Integrates Biology and Computer Science (Doctoral dissertation, Worcester Polytechnic Institute).
  • Bhat, V., AL, S., Muraly, A., Rao, P., & Chatterjee, J. (2022). An agent-based model to examine neuroinvasion by SARS-CoV-2.
  • Björklöf, C. (2022). Investigating the collective behaviour of the stock market using Agent-Based Modelling.
  • Boavida-Portugal, I. (2022). Future land use/cover change and tourism development: integrating land use policy and tourist decision behaviour. In Mapping and Forecasting Land Use (pp. 243-264). Elsevier.
  • Bogdanowski, A., Frank, K., Banitz, T., Muhsal, L. K., & Kost, C. (2022). McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems. PLOS Computational Biology, 18(1), e1009777.
  • Borgonovo, E., Pangallo, M., Rivkin, J., Rizzo, L., & Siggelkow, N. (2022). Sensitivity analysis of agent-based models: a new protocol. Computational and Mathematical Organization Theory, 1-43.
  • Bórquez-Paredes, D., Beghelli, A., Leiva, A., Jara, N., Lozada, A., Morales, P., ... & Olivares, R. (2022). Agent-based distributed protocol for resource discovery and allocation of virtual networks over elastic optical networks. Journal of Optical Communications and Networking, 14(8), 667-679.
  • Bouabdallah, I., & Mellah, H. (2022). Handling Trust in A Cloud Based Multi Agent System. arXiv preprint arXiv:2201.01807.
  • Bourceret, A., Amblard, L., & Mathias, J. D. (2022). Adapting the governance of social–ecological systems to behavioural dynamics: An agent-based model for water quality management using the theory of planned behaviour. Ecological Economics, 194, 107338.
  • Brady, C., Jen, T., Vogelstein, L., & Dim, E. (2022, June). Designing with Feeling: How Students Constructed Embodied Participatory Simulations for Groups of Younger Learners to Understand and Care About Sustainability in Ecosystems. In Interaction Design and Children (pp. 315-326).
  • Breitwieser, L., Hesam, A., De Montigny, J., Vavourakis, V., Iosif, A., Jennings, J., ... & Bauer, R. (2022). BioDynaMo: A modular platform for high-performance agent-based simulation. Bioinformatics, 38(2), 453-460.
  • Brinkmann, T., Steinfeldt, M., Arndt, C., Carstens, A., & Spuziak-Salzenberg, D. (2022). High-quality recycling through self-learning and resilient recycling networks using a combination of agent-based modelling and life cycle assessment. In E3S Web of Conferences (Vol. 349, p. 12004). EDP Sciences.
  • Brito, A. M. (2022). Una visión de complejidad a los sistemas urba-nos. La modelación basada en agentes (ABM) para la recreación de escenarios urbanos y sus posibilidades. Diseño y complejidad, 215.
  • Brocardo, J., Vale, I., & Menezes, L. (2022). A investigação em resolução de problemas, raciocínio, comunicação e modelação: Uma análise de 30 anos de publicações na revista Quadrante. Quadrante, 31(2), 63-93.
  • Bron, M. (2022). Understanding the influence of local government strategies targeting creative industries in Bandung, Indonesia, using Agent-Based Modelling (Master's thesis).
  • Bulumulla, C., Singh, D., Padgham, L., & Chan, J. (2022). Multi-level simulation of the physical, cognitive and social. Computers, Environment and Urban Systems, 93, 101756.
  • BULUMULLA, C. B. (2022). Integrating social network diffusion into BDI-based simulations: application focus on large-scale evacuations (Doctoral dissertation, RMIT University).
  • Bunin, S., Celestin, W., Hornback, A., & Rugaber, S. (2022, June). Incorporating Habitats in Conceptual Models and Agent-Based Simulations: Expanding the Virtual Ecological Research Assistant (VERA). In Proceedings of the Ninth ACM Conference on Learning@ Scale (pp. 472-474).
  • Butner, J. D., Dogra, P., Chung, C., Pasqualini, R., Arap, W., Lowengrub, J., ... & Wang, Z. (2022). Mathematical modeling of cancer immunotherapy for personalized clinical translation. Nature Computational Science, 2(12), 785-796.
  • Byer, N. W., & Reid, B. N. (2022). The emergence of imperfect philopatry and fidelity in spatially and temporally heterogeneous environments. Ecological Modelling, 468, 109968.
  • Calabrò, G., Le Pira, M., Giuffrida, N., Fazio, M., Inturri, G., & Ignaccolo, M. (2022). Modelling the dynamics of fragmented vs. consolidated last-mile e-commerce deliveries via an agent-based model. Transportation Research Procedia, 62, 155-162.
  • Calabrò, G., Le Pira, M., Giuffrida, N., Inturri, G., Ignaccolo, M., & Correia, G. H. D. A. (2022). Fixed-Route vs. Demand-Responsive Transport Feeder Services: An Exploratory Study Using an Agent-Based Model. Journal of Advanced Transportation, 2022.
  • Canals, C., Maroulis, S., Canessa, E., Chaigneau, S., & Mizala, A. (2022). Mechanisms Underlying Choice-Set Formation: The Case of School Choice in Chile. Social Science Computer Review, 08944393221088659.
  • Canessa, E., Chaigneau, S. E., & Moreno, S. (2022). Using agreement probability to study differences in types of concepts and conceptualizers. Behavior Research Methods, 1-20.
  • Cantin, G., Silva, C. J., & Banos, A. (2022). Mathematical analysis of a hybrid model: Impacts of individual behaviors on the spreading of an epidemic. Networks & Heterogeneous Media.
  • Carley, L. R. (2022, September). OSIRIS: Organization Simulation in Response to Intrusion Strategies. In Social, Cultural, and Behavioral Modeling: 15th International Conference, SBP-BRiMS 2022, Pittsburgh, PA, USA, September 20–23, 2022, Proceedings (Vol. 13558, p. 134). Springer Nature.
  • Carolina, N. (2022). Algoritma Path Planning Terkoordinasi Untuk Multi Robot Smart Warehouse (Doctoral dissertation, Universitas Pertamina).
  • Carson, D. B., & Carson, D. A. (2022). Understanding the demographic future of small Arctic villages using agent-based modelling. More than'Nature': Research on Infrastructure and Settlements in the North, 3, 263.
  • Carvajal León, B. F. (2022). Modelización basada en el individuo, de un reactor CSTR con recirculación de lodos activados (Bachelor's thesis, Quito: UCE).
  • Carvalho, I., Bernardi, F. A., Neiva, M. B., Lima, V. C., de Oliveira, L. L., Miyoshi, N. S. B., ... & Alves, D. (2022). COVID-19 BR: A web portal for COVID-19 information in Brazil. Procedia computer science, 196, 525-532.
  • Casadei, R., Fortino, G., Pianini, D., Placuzzi, A., Savaglio, C., & Viroli, M. (2022). A Methodology and Simulation-based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge. IEEE Internet of Things Journal.
  • Castelli, R. P (2022). ANALISI COMPARATIVA DI SIMULAZIONI PHET DI SISTEMI MECCANICI CLASSICI E SIMULAZIONI NETLOGO DI SISTEMI COMPLESSI (Doctoral dissertation)
  • Castro-Ríos, G. A., & Noguera-Hidalgo, Á. L. (2022). ¿ Los seguidores eligen a sus líderes? Explicación desde la simulación basada en agentes. Revista Venezolana de Gerencia (RVG), 27(100), 1594-1612.
  • Cayaban, C. J., Tacardon, E., Sario, M. C., & Intal, G. L. System Analysis and Design of Company XYZ’s RFQ/RFP Processes.
  • Cegielski, W. H. Agent-Based Modeling for Archaeology: Simulating the Complexity of Societies. IZA ROMANOWSKA, COLIN D. WREN, and STEFANI A. CRABTREE. 2021. Santa Fe Institute Press, Santa Fe, New Mexico. xiii+ 429 pp. 0.00 (PDF), ISBN 978-1-947864-25-2. American Antiquity, 1-2.
  • Čeh, I., Štorga, M., & Delač, G. (2022). Agent-Based Modelling: Parallel and Distributed Simulation of Product Development Team. Tehnički vjesnik, 29(4), 1424-1432.
  • Chappin, E. J., Schleich, J., Guetlein, M. C., Faure, C., & Bouwmans, I. (2022). Linking of a multi-country discrete choice experiment and an agent-based model to simulate the diffusion of smart thermostats. Technological Forecasting and Social Change, 180, 121682.
  • Chatalova, L. (2022). Resource Sufficiency in a Sustainable Bioeconomy: A Predator–Prey Perspective. In Bioeconomy and Sustainability (pp. 209-224). Springer, Cham.
  • Chatha, K. A., & Jalil, M. N. (2022). Complexity in Three-Echelon Supply Chain Network and Manufacturing Firm’s Operational Performance. Computers & Industrial Engineering, 108196.
  • Chemweno, P., Sullivan, B. P., Bermperidis, G., & Thiede, S. (2022). Exploring the Added-Value of Integrating Real-Time Location Systems for Tracking Critical Maintenance Tools. Procedia CIRP, 107, 902-907.
  • Chen, B., & Poquet, O. (2022). Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect. Journal of Learning Analytics, 9(1), 1-12.
  • Chen, C., Mostafizi, A., Wang, H., Cox, D., & Chand, C. (2022). An integrative agent‐based vertical evacuation risk assessment model for near‐field tsunami hazards. Risk Analysis.
  • Chen, D. (2022, July). Exploring Online Physician–Patient Interactions Through Information Sharing with Agent-Based Modeling. In International Conference on Logistics, Informatics and Service Sciences (pp. 59-73). Singapore: Springer Nature Singapore.
  • Chen, H., Chen, C., Li, H., Zhang, J., & Yang, Z. (2022). A Simulation Study on the Processes of Intra-Group Informal Interaction Affecting Workers’ Safety Behaviors. International journal of environmental research and public health, 19(16), 10048.
  • Chen, J., Zhang, X., Peng, X., Xu, D., & Peng, J. (2022). Efficient routing for multi-AGV based on optimized Ant-agent. Computers & Industrial Engineering, 108042.
  • Chen, J., Zhao, L., Xiao, F., Horn, M. & Wilensky, U. (2022). Self-Governed Collaborative Inquiry in Action: A Case Study of a Large-Scale Online Youth Community. Paper accepted to CSCL 2022. Hiroshima, Japan: International Society of the Learning Sciences.
  • Chen, L., Chen, Z., Lin, L., Ye, Q., Guo, S., & Lin, J. (2022). Augmenting deep land use prediction with randomized simulation. Computer Animation and Virtual Worlds, 33(3-4), e2071.
  • Chen, W., Ding, Y., Zhang, Y., Tian, Z., & Wei, S. (2022). Risk Assessment and Prevention Strategy of Virus Infection in the Context of University Resumption. Buildings, 12(6), 806.
  • Chen, Y., Xu, L., Zhang, X., Wang, Z., Li, H., Yang, Y., ... & Li, D. (2023). Socio-econ-ecosystem multipurpose simulator (SEEMS): An easy-to-apply agent-based model for simulating small-scale coupled human and nature systems in biological conservation hotspots. Ecological Modelling, 476, 110232.
  • Chersoni, G., DellaValle, N., & Fontana, M. (2022). Modelling thermal insulation investment choice in the EU via a behaviourally informed agent-based model. Energy Policy, 163, 112823.
  • Chetcuti, J., Kunin, W. E., & Bullock, J. M. (2022). Species' movement influence responses to habitat fragmentation. Diversity and Distributions.
  • Chettry, V., & Manisha, K. (2022). Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India. In Modeling, Simulation and Optimization (pp. 387-397). Springer, Singapore.
  • Chichorro, F., Correia, L., & Cardoso, P. (2022). Biological traits interact with human threats to drive extinctions: A modelling study. Ecological Informatics, 101604.
  • Childers, G., Linsky, C. L., Payne, B., Byers, J., & Baker, D. (2022). K-12 Educators’ Self-Confidence in Designing and Implementing Cybersecurity Lessons. Computers and Education Open, 100119.
  • Christensen, C., & Salmon, J. (2022). An agent-based modeling approach for simulating the impact of small unmanned aircraft systems on future battlefields. The Journal of Defense Modeling and Simulation, 19(3), 481-500.
  • Chopra, A. (2022). Decision Making for Populations (Doctoral dissertation, Massachusetts Institute of Technology).
  • Civico, M. (2022). Complexity in language matters Concept and uses of agent-based modelling. Advances in Interdisciplinary Language Policy, 9, 381.
  • Clark, R., & Kimbrough, S. O. (2022). On Modeling Evolution in Continuous Spaces. In Conference of the Computational Social Science Society of the Americas (pp. 23-42). Springer, Cham.
  • Collaborative, H. R. (2022). Smart Industry–Better Management. Advanced manufacturing, 1(6), 8.
  • Collard, P. (2022). The “flat peer learning” agent-based model. Journal of Computational Social Science, 5(1), 161-187.
  • Correa-Martinez, Y. C., & Seck, M. (2022). A generic representation of supply network resilience using simulation based experimentation. Journal of Simulation, 1-34.
  • Corsini, R. R., Costa, A., Fichera, S., & Pluchino, A. (2022). A configurable computer simulation model for reducing patient waiting time in oncology departments. Health Systems, 1-15.
  • Costanzo, A., van Haeringen, E., & Hemelrijk, C. K. (2022). Effect of time-delayed interactions on milling: a minimal model. Europhysics Letters.
  • Costas, J., Puche, J., Ponte, B., & Gupta, M. C. (2022). An agent-based simulator for quantifying the cost of uncertainty in production systems. Simulation Modelling Practice and Theory, 102660.
  • Cotfas, L. A., Delcea, C., Iancu, L. D., Ioanăş, C., & Ponsiglione, C. (2022). Large Event Halls Evacuation using an Agent-Based Modeling Approach. IEEE Access.
  • Covitt, B. A., & Anderson, C. W. (2022). Untangling Trustworthiness and Uncertainty in Science. Science & Education, 1-26.
  • Crouse, K. N., Desai, N. P., Cassidy, K. A., Stahler, E. E., Lehman, C. L., & Wilson, M. L. (2022). Larger territories reduce mortality risk for chimpanzees, wolves, and agents: Multiple lines of evidence in a model validation framework. Ecological Modelling, 471, 110063.
  • Cui, Y., Zhao, G., & Zhang, D. (2022). Improving students' inquiry learning in web‐based environments by providing structure: Does the teacher matter or platform matter?. British Journal of Educational Technology.
  • da Silva, A. C. G., de Lima, C. L., da Silva, C. C., Moreno, G. M. M., Silva, E. L., Marques, G. S., ... & dos Santos, W. P. (2022). Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis, 333-357.
  • Daly, A. J., De Visscher, L., Baetens, J. M., & De Baets, B. (2022). Quo vadis, agent-based modelling tools?. Environmental Modelling & Software, 105514.
  • Dangelo, V., Rodríguez, G. L., Sklate, M. F., & Pairetti, C. (2022). Robótica en FabLab: Introducción a la programación para estudiantes de Ingeniería Mecánica. Memorias de las JAIIO, 8(8), 6-23.
  • D'Angelo, G., & Ferretti, S. (2022). Adaptive Parallel and Distributed Simulation of Complex Networks. Journal of Parallel and Distributed Computing.
  • Dar, A. R., Shah, M. A., & Ahmed, M. A Meta Sensor-Based Autonomous Vehicle Safety System for Collision Avoidance Using Li-Fi Technology. Intelligent Cyber-Physical Systems for Autonomous Transportation, 237.
  • Datseris, G., & Parlitz, U. (2022). Dynamics on Networks, Power Grids, and Epidemics. In Nonlinear Dynamics (pp. 157-173). Springer, Cham.
  • Datseris, G., Vahdati, A. R., & DuBois, T. C. (2022). Agents. jl: a performant and feature-full agent-based modeling software of minimal code complexity. Simulation, 00375497211068820.
  • Datta, S., Rokade, S., & Rajput, S. P. (2022). Unsignalized Intersection Capacity Estimation Through Traffic Rule Re-adjustments Using Agent-Based Cellular Automata Simulations. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-27.
  • Davey, B., Peel, A., Horn, M., & Wilensky, U. (2022). Learning Natural Selection through Computational Models in a High School AP Biology Classroom. In Proceedings of the 16th International Conference of the Learning Sciences-ICLS 2022, pp. 1916-1917. International Society of the Learning Sciences.
  • de Castro, M. G. A., & García-Peñalvo, F. J. (2022). Metodologías educativas de éxito: proyectos Erasmus+ relacionados con e-learning o TIC. Campus Virtuales, 11(1), 95-114.
  • Delcea, C., Cotfus, L. A., Mieriwiak, R., & Ioanfăş, C. (2022). Grey Clustering of the Variations in Reverse Pyramid Boarding Method Considering Pandemic Restrictions. Journal of Grey System, 34(1).
  • Delcea, C., Milne, R. J., & Cotfas, L. A. (2022). Evaluating Classical Airplane Boarding Methods for Passenger Health during Normal Times. Applied Sciences, 12(7), 3235.
  • de Campos Silva, A. D., & de Carvalho, L. L. (2022). Recifes de coral: A importância da tecnologia e dos jogos didáticos no processo de Educação Ambiental no Ensino Básico. Ambiente & Educação, 27(2), 1-35.
  • De La Paz, S., Butler, C., Levin, D. M., & Felton, M. K. (2022). Effects of a Cognitive Apprenticeship on Transfer of Argumentative Writing in Middle School Science. Learning Disability Quarterly, 07319487221119365.
  • de Lima, H. P., Teseo, S., de Lima, R. L. C., Ferreira-Châline, R. S., & Châline, N. (2022). Temporary prey storage along swarm columns of army ants: an adaptive strategy for successful raiding?. Biology Letters, 18(2), 20210440.
  • de Oliveira Simoyama, F., Sarti, F. M., & Battisti, M. C. G. (2022). Effects of disclosing inspection scores of health facilities. Socio-Economic Planning Sciences, 81, 101183.
  • De Vizia, C., Macii, A., Patti, E., & Bottaccioli, L. (2022). A hierarchical and modular agent-oriented framework for power systems co-simulations. Energy Informatics, 5(4), 1-21.
  • de Vries, O. I. (2022). Simulating Social Interaction in Times of COVID Restrictions (Doctoral dissertation).
  • De Winne, K. (2022). Simulation-Based Port Competition: Feasibility and Implementation of an Agent-Based Framework. Competition and Regulation in Network Industries, 17835917211064920.
  • Deng, S. (2022, January). Public opinion propagation model and simulation based on Impact model and improved SIR. In ICETIS 2022; 7th International Conference on Electronic Technology and Information Science (pp. 1-5). VDE.
  • Des Jarlais, D., Bobashev, G., Feelemyer, J., & McKnight, C. (2022). Modeling HIV transmission among persons who inject drugs (PWID) at the “End of the HIV Epidemic” and during the COVID-19 pandemic. Drug and alcohol dependence, 238, 109573.
  • Detotto, C., Idda, C., Innocenti, E., Yuheng, L., Parker, D. C., & Prunetti, D. (2022, July). A resuable, extensible Netlogo building block of land and housing markets in a touristic region. In Biennial iEMSs (international Environmental Modelling and Software society) conference.
  • Dhanare, R., Nagwanshi, K. K., & Varma, S. (2022). A Study to Enhance the Route Optimization Algorithm for the Internet of Vehicle. Wireless Communications and Mobile Computing, 2022.
  • Dhou, K., & Cruzen, C. (2022). A creative chain coding technique for bi-level image compression inspired by the NetLogo HIV agent-based modeling simulation. Journal of Computational Science, 101613.
  • Dhou, K., & Cruzen, C. (2022). An innovative chain coding mechanism for information processing and compression using a virtual bat-bug agent-based modeling simulation. Engineering Applications of Artificial Intelligence, 113, 104888.
  • DIACONESCU, A., HOUZE, E., DESSALLES, J. L., VANGHELUWE, H., & FRANCESCHINI, R. (2022). Multi-Scale Model-based Explanations for Cyber-Physical Systems: the Urban Traffic Case.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. A comparison of occupational accidents in the manufacturing and construction sector through data mining techniques. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. Collaborative Planning as a determining factor at the group level in Collaborative Intelligence. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. Decentralized, Fast and Scalable Almost-Global Convergence in Single-Optimum Coordination Problems. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. Industry 4.0 and Airspace Operations Research with EU financial contribution. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díez-Echavarría, L., Gutiérrez-Gutiérrez, G., & Ríos-Echeverri, D. C. (2022). Competitive Dynamics between Physical and Virtual Markets in Multiplex Networks. Periodica Polytechnica Social and Management Sciences.
  • Dimitrov, N. (2022). Understanding the Effects of Fox Movement on the Spread of Sarcoptic Mange in Urban Settings–An Individual-based Modelling Approach (Doctoral dissertation).
  • Dimka, J., & Sattenspiel, L. (2022). “We didn't get much schooling because we were fishing all the time”: Potential impacts of irregular school attendance on the spread of epidemics. American Journal of Human Biology, 34(1), e23578.
  • Diouf, E. G., Brévault, T., Ndiaye, S., Faye, E., Chailleux, A., Diatta, P., & Piou, C. (2022). An agent-based model to simulate the boosted Sterile Insect Technique for fruit fly management. Ecological Modelling, 468, 109951.
  • do Amaral, J. V. S., de Carvalho Miranda, R., Montevechi, J. A. B., dos Santos, C. H., & Gabriel, G. T. (2022). Metamodeling-based simulation optimization in manufacturing problems: a comparative study. The International Journal of Advanced Manufacturing Technology, 1-20.
  • Dobson, G. B. (2022). Cyber-Forces, Interactions, Terrain: An agent-based framework for simulating cyber team performance (Doctoral dissertation, Carnegie Mellon University).
  • Dokin, B., Aletdinova, A., & Zheshko, A. (2022). Simulation modeling of the machine and tractor fleet to improve the technical base of the agro-industrial complex. In BIO Web of Conferences (Vol. 52, p. 00014). EDP Sciences.
  • Domino, K., Miszczak, J.A. (2022). Will you infect me with your opinion?, Physica A: Statistical Mechanics and its Applications Vol. 608, 128289 DOI:10.1016/j.physa.2022.128289 arXiv:2208.13426
  • Dong, Y., & Yang, T. (2022). Evolutionary game analysis of promoting the development of green logistics under government regulation. JUSTC, 52(9), 1-13.
  • Dong, Y., Zhen, R., & Yang, T. (2022). Evolutionary game analysis of low-carbon behavior credit supervision of logistics enterprises. JUSTC, 52(10), 1-13.
  • Dos Santos Ribeiro, R. (2022). Como los modelos computacionales permiten representar la epidemia covid-19, ayudando en la comprensión de su dinámica y en la exploración de estrategias de control (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Dragomir, O. E. (2022). MODELLING AND SIMULATION OF DISTRIBUTED SYSTEMS USING INTELLIGENT MULTI-AGENTS. Journal of Science and Arts, 22(2), 471-482.
  • Drechsler, M., Wätzold, F., & Grimm, V. (2022). The hitchhiker's guide to generic ecological-economic modelling of land-use-based biodiversity conservation policies. Ecological Modelling, 465, 109861.
  • Du, H., Han, Q., Sun, J., & Wang, C. C. (2022). Adoptions of prefabrication in residential sector in China: agent-based policy option exploration. Engineering, Construction and Architectural Management.
  • Duan, T., Wang, W., & Wang, T. (2022, August). A Review for Unmanned Swarm Gaming: Framework, Model and Algorithm. In 2022 8th International Conference on Big Data and Information Analytics (BigDIA) (pp. 164-170). IEEE.
  • Dudenhöffer, J. H., Luecke, N. C., & Crawford, K. M. (2022). Changes in precipitation patterns can destabilize plant species coexistence via changes in plant–soil feedback. Nature Ecology & Evolution, 1-9.
  • Dugarte-Peña, G. L., Sánchez-Segura, M. I., Medina-Domínguez, F., de Amescua, A., & González, C. (2022). An instance-based-learning simulation model to predict knowledge assets evolution involved in potential digital transformation projects. Knowledge Management Research & Practice, 1-22.
  • Easter, C., Leadbeater, E., & Hasenjager, M. J. (2022). Behavioural variation among workers promotes feed-forward loops in a simulated insect colony. Royal Society Open Science, 9(3), 220120.
  • Ebrie, A. S., & Kim, Y. J. Investigating Market Diffusion of Electric Vehicles with Experimental Design of Agent-Based Modeling Simulation. Available at SSRN 4019513.
  • Edgerton, E., Wang, H. H., Grant, W. E., & Masser, M. (2022). Aquatic Plant Invasion and Management in Riverine Reservoirs: Proactive Management via a Priori Simulation of Management Alternatives. Diversity, 14(12), 1113.
  • Edris, A. (2022). Identify the Individuals Status During Crowds Movement (Doctoral dissertation, University of Idaho).
  • Egger, R. (2022). Software and Tools. In Applied Data Science in Tourism (pp. 547-588). Springer, Cham.
  • Ehiagwina, F. O., Iromini, N. A., Olatinwo, I. S., Raheem, K., & Mustapha, K. A State-of-the-Art Survey of Peer-to-Peer Networks: Research Directions, Applications and Challenges. management, 14, 19-22.
  • Eilam, B., & Omar, S. Y. (2022). Science Teachers’ Construction of Knowledge About Simulations and Population Size Via Performing Inquiry with Simulations of Growing Vs. Descending Levels of Complexity. Fostering Understanding of Complex Systems in Biology Education: Pedagogies, Guidelines and Insights from Classroom-based Research, 205.
  • El Karkri, J., & Benmir, M. (2022). Some key concepts of mathematical epidemiology. In Mathematical Analysis of Infectious Diseases (pp. 137-162). Academic Press.
  • Elgammal, I., Alhothali, G. T., & Sorrentino, A. (2022). Segmenting Umrah performers based on outcomes behaviors: a cluster analysis perspective. Journal of Islamic Marketing.
  • Elkamel, M., Valencia, A., Zhang, W., Zheng, Q. P., & Chang, N. B. (2022). Multi-Agent Modeling for Linking a Green Transportation System with an Urban Agriculture Network in an Urban Food-Energy-Water Nexus. Sustainable Cities and Society, 104354.
  • Elkhouly, R., Tamaki, E., & Iwasaki, K. (2022). Mitigating crowded transportation terminals nearby mega-sports events. Behaviour & Information Technology, 1-17.
  • Eilam, B., & Omar, S. Y. (2022). Science Teachers’ Construction of Knowledge About Simulations and Population Size Via Performing Inquiry with Simulations of Growing Vs. Descending Levels of Complexity. In Fostering Understanding of Complex Systems in Biology Education (pp. 205-226). Springer, Cham.
  • Eili, M. Y., & Rezaeenour, J. (2022). An approach based on process mining to assess the quarantine strategies' effect in reducing the COVID-19 spread. Library Hi Tech, (ahead-of-print).
  • Esmaeili Avval, A., Dehghanian, F., & Pirayesh, M. (2022). Auction design for the allocation of carbon emission allowances to supply chains via multi-agent-based model and Q-learning. Computational and Applied Mathematics, 41(4), 1-41.
  • Esquivel, K. E., Hesselbarth, M. H., & Allgeier, J. E. Mechanistic support for increased primary production around artificial reefs. Ecological Applications, e2617.
  • Fabio, R. A., D'Agnese, C., & Calabrese, C. (2022). Peace attitude and friendliness influence cooperative choices in context of uncertainty. Peace and Conflict: Journal of Peace Psychology.
  • Fain, B. G., & Dobrovolny, H. M. (2022). GPU acceleration and data fitting: Agent-based models of viral infections can now be parameterized in hours. Journal of Computational Science, 101662.
  • Faiza, S., & Habib, A. H. (2022). Modeling and Simulation of Urban Mobility in a Smart City. In International Conference on Artificial Intelligence and its Applications (pp. 379-394). Springer, Cham.
  • Farahbakhsh, S., Snellinx, S., Mertens, A., Belderbos, E., Bourgeois, L., & Van Meensel, J. (2023). What's stopping the waste-treatment industry from adopting emerging circular technologies? An agent-based model revealing drivers and barriers. Resources, Conservation and Recycling, 190, 106792.
  • Farooq, O., & Singh, P. (2022). Data Analytics and Modeling in IoT-Fog Environment for Resourceconstrained IoT-Applications: A Review. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 15(7), 968-991.
  • Farris, A. V., & Tosun, G. (2022). Computing in Precollege Science, Engineering, and Mathematics Education. In Oxford Research Encyclopedia of Education.
  • Faweya, O., Desai, P. S., & Higgs III, C. F. (2022). Towards an agent-based model to simulate osseointegration in powder-bed 3D printed implant-like structures. Journal of the Mechanical Behavior of Biomedical Materials, 126, 104915.
  • Feng, B., Li, W., & Wang, L. Signal Optimization of Electronic Communication Network Based on Internet of Things. Journal of Sensors, 2022.
  • Ferguson, J. P. (2022). A Peircean Socio-Semiotic Analysis of Science Students’ Creative Reasoning as/Through Digital Simulations. Research in Science Education, 1-31.
  • Ferrão, I. G., Espes, D., Dezan, C., & Branco, K. R. L. J. C. (2022). Security and safety concerns in air taxis: a systematic literature review. Sensors, 22(18), 6875.
  • Fiore, I., Greco, A., & Pluchino, A. (2022). On Damage Identification in Planar Frames of Arbitrary Size. Shock and Vibration, 2022.
  • Fioretti, G. (2022). Emergence of Industrial Stylized Facts out of Innovative and Imitative Entrepreneurship. Available at SSRN 3581357.
  • Fitrianip, W., Edriani, A. F., Hardiansyah, R., Lestyanti, R., & Mase, L. Z. Implementation of Agent Based Modelling to Observe the Evacuating Behavior at Faculty of Engineering Building, University of Bengkulu, Indonesia. In Journal of the Civil Engineering Forum (pp. 179-192).
  • Fitzpatrick, B. G., Federico, P., Kanarek, A., & Lenhart, S. (2022). Control of a consumer‐resource agent‐based model using partial differential equation approximation. Optimal Control Applications and Methods, 43(1), 178-197.
  • Flache, A. (2022). GESIS Spring Seminar 2022: Simulation Techniques in the Social Sciences.
  • Flache, A., Mäs, M., & Keijzer, M. A. (2022). Computational approaches in rigorous sociology: agent-based computational modeling and computational social science. In Handbook of Sociological Science (pp. 57-72). Edward Elgar Publishing.
  • Franceschini, P. B., & Neves, L. O. (2022). A critical review on occupant behaviour modelling for building performance simulation of naturally ventilated school buildings and potential changes due to the COVID-19 pandemic. Energy and Buildings, 111831.
  • Francos, R. M., & Bruckstein, A. M. (2022). Search for Smart Evaders with Swarms of Sweeping Agents-a Resource Allocation Perspective. Journal of Intelligent & Robotic Systems, 106(4), 1-34.
  • Fu, H., Zhang, H., Zhang, M., & Hou, C. (2022). Modeling and Dynamic Simulation of the Public’s Intention to Reuse Recycled Water Based on Eye Movement Data. Water, 15(1), 114.
  • Fuchkina, E., Bielik, M., Schneider, S., Ossenberg-Engels, T., & Hämmerle, A. (2022). Space Matcher-An interactive toolbox for assisting in spatializing & testing office programmes using graph centralities.
  • Fuhao, Z., & Qiuhong, Z. (2022). Comparison of the Response Efficiency Between the Fractal and Traditional Emergency Organizations Based on System Dynamic Simulation. Sustainable Operations and Computers.
  • Fuhrmann, T., Levy, S., Wilensky, U., Blikstein, P., Bumbacher, E., Saba, J., Langbeheim, E., Hel-Or, H., Fernandez, C., de Deus Lopes, R., Klopfer, E., Wendel, D., Wagh, A., & Wilkerson, M. (2022). Developing Accessible and Sustainable Computational Modeling Tools in Learning Science: What is Next? Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Füllsack, M. (2022). LSTM-certainty as early warning signal for critical transitions. Systems Science & Control Engineering, 10(1), 562-571.
  • Fust, P., & Schlecht, E. (2022). Importance of timing: Vulnerability of semi-arid rangeland systems to increased variability in temporal distribution of rainfall events as predicted by future climate change. Ecological Modelling, 468, 109961.
  • Gabbar, M. A., & Hasson, S. T. (2022, June). Analyzing the Connectivity of the Wireless Sensor Networks. In 2022 10th International Conference on Smart Grid (icSmartGrid) (pp. 374-379). IEEE.
  • Gaidarski, I., & Kutinchev, P. (2022). Transformation of UML Design Models of In-formation Security System into Agent-based Simulation Models. Information and Security. 53(1), 65-77
  • Galan, S.F. (2022). Modeling Complex and Intelligent Systems with NetLogo. (Bellisco Ediciones.)
  • Galety, M. G., Al Atroshi, C., Balabantaray, B., & Mohanty, S. N. (Eds.). (2022). Social Network Analysis: Theory and Applications. John Wiley & Sons.
  • Gao, J., Zhang, W., Guan, T., & Feng, Q. (2022). Evolutionary game study on multi-agent collaboration of digital transformation in service-oriented manufacturing value chain. Electronic Commerce Research, 1-22.
  • Garg, V., Tiwari, R., & Shukla, A. (2022, June). Comparative Analysis of Fruit Fly-Inspired Multi-Robot Cooperative Algorithm for Target Search and Rescue. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC) (pp. 444-450). IEEE.
  • Ge, X., & Huang, K. (2022). Designing Online Learning Environments to Support Problem-Based Learning. In Handbook of Open, Distance and Digital Education (pp. 1-18). Singapore: Springer Nature Singapore.
  • Gebrehiwot, A. A., Hashemi-Beni, L., Kurkalova, L. A., Liang, C. L., & Jha, M. K. (2022). Using ABM to Study the Potential of Land Use Change for Mitigation of Food Deserts. Sustainability, 14(15), 9715.
  • Gerard, L., Wiley, K., Debarger, A. H., Bichler, S., Bradford, A., & Linn, M. C. (2022). Self-directed science learning during COVID-19 and beyond. Journal of Science Education and Technology, 31(2), 258-271.
  • Gerdes, L., Rengs, B., & Scholz-Wäckerle, M. (2022). Labor and environment in global value chains: an evolutionary policy study with a three-sector and two-region agent-based macroeconomic model. Journal of Evolutionary Economics, 1-51.
  • Ghafoori, H. R., Sadeghi-Niaraki, A., Alesheikh, A. A., & Choi, S. M. (2022). Ubiquitous GIS based outdoor evacuation assistance: An effective response to earthquake disasters. International Journal of Disaster Risk Reduction, 81, 103232.
  • GHOREISHI, M. (2022). Socio-hydrology from Local to Large Scales: An Agent-based Modeling Approach (Doctoral dissertation, University of Saskatchewan Saskatoon).
  • Ghoreishi, M., Elshorbagy, A., Razavi, S., Blöschl, G., Sivapalan, M., & Abdelkader, A. (2022). Cooperation in a Transboundary River Basin: a Large Scale Socio-hydrological Model of the Eastern Nile. Hydrology and Earth System Sciences Discussions, 1-24.
  • Ghribi, C., Cali, E., Hirsch, C., & Jahnel, B. (2022, March). Agent-based simulations for coverage extensions in 5G networks and beyond. In 2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN) (pp. 1-7). IEEE.
  • Ghumrawi, K. A. (2022). Applying and Accelerating Large-Scale Population Simulations (Doctoral dissertation, Miami University).
  • Gibson, M., Pereira, J. P., Slade, R., & Rogelj, J. (2022). Agent-Based Modelling of Future Dairy and Plant-Based Milk Consumption for UK Climate Targets. Journal of Artificial Societies and Social Simulation, 25(2).
  • Gignoux, J., Davies, I, D., Flint, S.R. (2022). 3Worlds, a simulation platform for ecosystem modeling. Ecological Modeling 2022, 473, 110121. https://doi.org/10.1016/j.ecolmodel.2022.110121
  • Gilbert, N., & Elsenbroich, C. (2022). WATERING Crop Growth Reusable Building Block.
  • Girish, S. (2022). Emergent patterns of affiliative behaviour in group-living lemurs (Doctoral dissertation).
  • Gisen, D. C., Schütz, C., & Weichert, R. B. (2022). Development of behavioral rules for upstream orientation of fish in confined space. PLOS ONE, 17(2), e0263964.
  • Goldenbogen, B., Adler, S. O., Bodeit, O., Wodke, J. A., Escalera‐Fanjul, X., Korman, A., ... & Klipp, E. (2022). Control of COVID‐19 Outbreaks under Stochastic Community Dynamics, Bimodality, or Limited Vaccination. Advanced Science, 2200088.
  • Golmaryami, S., Ferreira, P., & Nunes, M. L. (2022, June). Consumer willingness to participate in demand-side management: An agent-based modeling approach. In Proceedings of the 5th International Conference on Energy and Environment—Bringing Together Economics and Engineering (ICEE), Porto, Portugal (pp. 2-3).
  • Gomes, I., Bot, K., Ruano, M. D. G., & Ruano, A. (2022). Recent Techniques Used in Home Energy Management Systems: A Review. Energies, 15(8), 2866.
  • González Cuevas, A., & Suppi, R. (2022). ABM simulation focused on urban mobility. In X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022).
  • Gouvea, J., Appleby, L., Fu, L., & Wagh, A. (2022). Motivating and Shaping Scientific Argumentation in Lab Reports. CBE—Life Sciences Education, 21(4), ar71.
  • Graham, S. (2022). Mapping the Landscape of our Ignorance. Simulating Roman Economies: Theories, Methods, and Computational Models, 293.
  • Grajdura, S., Borjigin, S., & Niemeier, D. (2022). Fast-moving dire wildfire evacuation simulation. Transportation Research Part D: Transport and Environment, 104, 103190.
  • Grayson, K. L., Hiliker, A. K., & Wares, J. R. (2022). R Markdown as a dynamic interface for teaching: Modules from math and biology classrooms. Mathematical Biosciences, 108844.
  • Grebennik, I., Hubarenko, Y., & Ananiev, M. (2022). Information Technologies for Assessing the Effectiveness of the Quarantine Measures. In International Conference on Information Technology in Disaster Risk Reduction (pp. 160-175). Springer, Cham.
  • Greif, H. (2022). Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence. Minds and Machines, 1-23.
  • Griesemer, M., & Sindi, S. S. (2022). Rules of engagement: a guide to developing agent-based models. In Microbial Systems Biology (pp. 367-380). Humana, New York, NY.
  • Grigoryan, G., Etemadidavan, S., & Collins, A. J. (2022). Computerized agents versus human agents in finding core coalition in glove games. SIMULATION, 00375497221093652.
  • Grimm, V., Berger, U., & Filatova, T. (2022). Reusable building blocks for agent-based modelling: benefits, challenges, and a template for their release.
  • Grewe, J., & Griva, I. (2022). Optimizing Heterogeneous Maritime Search Teams using an Agent-based Model and Nonlinear Optimization Methods. In Proceedings of the 11th International Conference on Operations Research and Enterprise Systems (ICORES 2022), 200-207.
  • Gu, H., Feng, L., & Zhen, X. (2022). Study on the stability of anaerobic digestion of food waste and the waste mushroom substrate based on SBR reactor and Netlogo simulation. Journal of Material Cycles and Waste Management, 1-17.
  • Guia, S. S., Laouid, A., Hammoudeh, M., Bounceur, A., Alfawair, M., & Eleyan, A. (2022). Co-Simulation of Multiple Vehicle Routing Problem Models. Future Internet, 14(5), 137.
  • Gulyás, L. (2022). Spatial Clustering by Schelling’s Ants. In Conference on Computational Collective Intelligence Technologies and Applications (pp. 579-586). Springer, Cham.
  • Gunckel, K. L., Covitt, B. A., Berkowitz, A. R., Caplan, B., & Moore, J. C. (2022). Computational thinking for using models of water flow in environmental systems: Intertwining three dimensions in a learning progression. Journal of Research in Science Teaching.
  • Gunckel, K. L., Covitt, B. A., Love, G., Cooper-Wagoner, J. A., & Moreno, D. (2022). Unplugged to Plugged In Breadcrumb. The Science Teacher, 89(3).
  • Güngör, Ö., Günneç, D., Salman, F. S., & Yücel, E. Prediction of Migration Paths Using Agent-Based Simulation Modeling: The Case of Syria.
  • Hahn Utrero, T. (2022). ¿ Influyen el número de orígenes y los umbrales de confianza en las creencias en la dinámica de difusión de rumores? Una propuesta teórica desde un modelo basado en agentes. Papers: revista de sociologia, 107(2), e2994-e2994.
  • Hajian Heidary, M. (2022). Agent-based simulation-optimization model for a bi-objective stochastic multi-period supply chain design problem. Journal of Industrial Engineering and Management Studies, 8(2), 175-195.
  • Hajiesmaeili, M., Addo, L., Watz, J., Railsback, S. F., & Piccolo, J. J. (2022). Individual‐based modelling of hydropeaking effects on brown trout and Atlantic salmon in a regulated river. River Research and Applications.
  • Hakim, G., & Braun, R. (2022). Wireless Sensor Network Routing for Energy Efficiency. In Advances in Systems Engineering: Proceedings of the 28th International Conference on Systems Engineering, ICSEng 2021, December 14-16, Wrocław, Poland 28 (pp. 329-343). Springer International Publishing.
  • Hamza, M., Iqbal, W., Ahmad, A., Babar, M., & Khan, S. (2022). A social qualitative trust framework for Fog computing. Computers and Electrical Engineering, 102, 108195.
  • Hanine, Y., Malick, M. E. M., Tkiouat, M., Lahrichi, Y., & Alaoui, Y. L. (2022, May). A New Financial Group Lending Based on Smart Contracts: An Agent-based Simulation. In 2022 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-5). IEEE.
  • Hashemi Aslani, Z., Omidvar, B., & Karbassi, A. (2022). Integrated model for land-use transformation analysis based on multi-layer perception neural network and agent-based model. Environmental Science and Pollution Research, 1-14.
  • Hassanpour, S., Gonzalez, V., Liu, J., Zou, Y., & Cabrera-Guerrero, G. (2022). A hybrid hierarchical agent-based simulation approach for buildings indoor layout evaluation based on the post-earthquake evacuation. Advanced Engineering Informatics, 51, 101531.
  • Hassen, F. S., Kalla, M., & Dridi, H. (2022). Using agent-based model and Game Theory to monitor and curb informal houses: A case study of Hassi Bahbah city in Algeria. Cities, 125, 103617.
  • HAYOUN, S., & YAHYAOUI, T. (2022). Une simulation à base d’agents du modèle de Harris et Todaro dans l’explication de la migration des travailleurs au Maroc. Revue Française d'Economie et de Gestion, 3(7).
  • Hebing, Z., & Xiaojing, Z. (2022). E-Commerce Credit Network Control Strategy from a Critical Perspective. Mathematical Problems in Engineering, 2022.
  • Heck, M. V. (2022). Agent-based modelling of the Housesparrow (Master's thesis).
  • Herget, F., Kleppmann, B., Ahrweiler, P., Gruca, J., & Neumann, M. (2022, April). How Perceived Complexity Impacts on Comfort Zones in Social Decision Contexts—Combining Gamification and Simulation for Assessment. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 203). Springer Nature.
  • Herrmann, B., Lang, C., & Philippe, L. (2022, November). Data Synchronization in Distributed Simulation of Multi-Agent Systems. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection: 20th International Conference, PAAMS 2022, L'Aquila, Italy, July 13–15, 2022, Proceedings (Vol. 13616, p. 50). Springer Nature.
  • Hernandez, I., Cohen, D., Gruschow, K., Nowak, A., Gelfand, M. J., & Borkowski, W. (2022). The importance of being unearnest: Opportunists and the making of culture. Journal of Personality and Social Psychology.
  • Heydari Fard, S. (2022). Strategic injustice, dynamic network formation, and social movements. Synthese, 200(5), 1-25.
  • He, C., Jia, G., McCabe, B., Chen, Y., Zhang, P., & Sun, J. (2022). Psychological decision-making process of construction worker safety behavior: an agent-based simulation approach. International journal of occupational safety and ergonomics, 1-13.
  • Hisseine, M. A., Chen, D., & Yang, X. (2022). The Application of Blockchain in Social Media: A Systematic Literature Review. Applied Sciences, 12(13), 6567.
  • Hoffmann, B., Urquhart, N., Chalmers, K., & Guckert, M. (2022). An empirical evaluation of a novel domain-specific language–modelling vehicle routing problems with Athos. Empirical Software Engineering, 27(7), 1-52.
  • Hölzchen, E., Hertler, C., Willmes, C., Anwar, I. P., Mateos, A., Rodríguez, J., ... & Timm, I. J. (2022). Estimating crossing success of human agents across sea straits out of Africa in the Late Pleistocene. Palaeogeography, Palaeoclimatology, Palaeoecology, 110845.
  • Horn, C., Potter, R., & Peternell, M. (2022). Water Flows and Water Accumulations on Bedrock as a Structuring Element of Rock Art. Journal of Archaeological Method and Theory, 1-27.
  • Hoseini, M., Azar, A., Azarfar, A., & Ebadi, A. (2022). An Agent-Based Simulation of Insurance Supply chain Risk Detection and Assessment. Scientific Journal of System Management Studies, 2(4), 11-44.
  • Hosseini, S., & Zandvakili, A. (2022). Information dissemination modeling based on rumor propagation in online social networks with fuzzy logic. Social Network Analysis and Mining, 12(1), 1-18.
  • Housh, K., Hmelo-Silver, C. E., & Yoon, S. A. (2022). Theoretical Perspectives on Complex Systems in Biology Education. In Fostering Understanding of Complex Systems in Biology Education: Pedagogies, Guidelines and Insights from Classroom-based Research (pp. 1-16). Cham: Springer International Publishing.
  • Hu, B., Zhou, C., Tian, Y. C., Du, X., & Hu, X. (2022). Attack Intention Oriented Dynamic Risk Propagation of Cyberattacks on Cyber-Physical Power Systems. IEEE Transactions on Industrial Informatics.
  • Hu, Q., Medina, A., Siciliano, M. D., & Wang, W. Network Structures and Network Effects Across Management and Policy Contexts: A Systematic Review. Public Administration.
  • Huang, G., Yu, X., Long, Q., Huang, L., & Luo, S. (2022). The impact of economic freedom on COVID-19 pandemic control: the moderating role of equality. Globalization and Health, 18(1), 1-17.
  • Huang, J., Cui, Y., Zhang, L., Tong, W., Shi, Y., & Liu, Z. (2022). An Overview of Agent-Based Models for Transport Simulation and Analysis. Journal of Advanced Transportation, 2022.
  • Huang, Q., Zheng, X., Zhang, M., & Zhang, X. (2022). Agent-based modeling of the word-of-mouth effect on promoting brand-name agricultural products. Journal of Economic Interaction and Coordination, 1-22.
  • Hunter, E., & Kelleher, J. D. (2022). Understanding the assumptions of an SEIR compartmental model using agentization and a complexity hierarchy. Journal of Computational Mathematics and Data Science, 4, 100056.
  • Hunter, E., & Kelleher, J. D. (2022). Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland. Algorithms, 15(8), 270.
  • Hutama, I. A. W., & Nakamura, H. (2022). DISASTER EVACUATION ROUTE CHOICES FOR INFORMAL SETTLEMENTS A CONCEPTUAL FRAMEWORK. SEATUC journal of science and engineering, 3(1), 16-29.
  • Ibbotson, P., Jimenez-Romero, C., & Page, K. M. (2022). Dying to cooperate: the role of environmental harshness in human collaboration. Behavioral Ecology, 33(1), 190-201.
  • Ibrahim, A. M. (2022). The conditional defector strategies can violate the most crucial supporting mechanisms of cooperation. Scientific Reports, 12(1), 1-10.
  • Idros, N., Othman, W. A. F. W., Wahab, A. A. A., Alhady, S. S. N., & Bakar, E. A. (2022). Modeling 2-D Solitary Hunting Behavior of Chimpanzee. In Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications (pp. 1077-1082). Springer, Singapore.
  • Ignatenko, O. (2022). Exploratory analysis and models for strategic learning towards equilibrium. Educational Dimension.
  • Imen, B., & Hakima, M. (2022). Is it Trustworthy?: Trusting Clients in a Cloud Based Multi Agent System. In World Conference on Information Systems and Technologies (pp. 155-164). Springer, Cham.
  • Islam, N., Bhuiya, R., Samiur, M. D., Drishty, A. S., Saha, S. S., & Akash, U. D. (2022). Surveillance in Maritime Scenario Using Deep-Learning and Swarm Intelligence (Doctoral dissertation, Brac University).
  • IUA, D. A. (2022). Analisis Pengaruh Risk Attitude terhadap Decision-Making Competence Individu pada Penggunaan Model Simulasi Berbasis Agen (Doctoral dissertation, Universitas Gadjah Mada).
  • Ivanova, Y. (2022). A Methodology for Empirical Research and Analysis in the Field of Cybersecurity. Yearbook Telecommunications 2022, 9, 45-52.
  • Izewski, N. (2022). A NetLogo COVID-19 Virus Simulation Model for Determining Better Strategies at Handling a Virus Outbreak.
  • Jahanbani, M., Vahidnia, M. H., & Aspanani, M. (2022). Geographical agent-based modeling and satellite image processing with application to facilitate the exploration of minerals in Behshahr, Iran. Arabian Journal of Geosciences, 15(9), 1-14.
  • Jensen, P. (2022). Introducing simple models of social systems. American Journal of Physics.
  • Jeong, S., Elliott, J. B., Feng, Z., & Feldon, D. F. (2022). Understanding Complex Ecosystems Through an Agent-Based Participatory Watershed Simulation. Journal of Science Education and Technology, 31(5), 691-705.
  • Jiménez, A. F., Cárdenas, P. F., & Jiménez, F. (2022). Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales. Computers and Electronics in Agriculture, 192, 106635.
  • Jin, K. S., Lee, S. M., & Kim, Y. C. (2022). Adaptive and optimized agent placement scheme for parallel agent‐based simulation. ETRI Journal, 44(2), 313-326.
  • Jin, X., Chen, C., & Zhang, M. (2022). Research on Synergy between Entrepreneurial Service and Financial Support in Crowd Innovation Space Ecosystem. Sustainability, 14(10), 5966.
  • Jing, S., & Yongzhu, W. (2022, March). Application of Small World Network in the Dissemination of Unsafe Behaviors with Masks. In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) (Vol. 6, pp. 1477-1482). IEEE.
  • João Emmanuel, D. Simulation of the Implementation of Domestic Solar Systems Using Multi-agent Systems from Web Scraping. In Proceedings of the 7th Brazilian Technology Symposium (BTSym’21): Emerging Trends in Human Smart and Sustainable Future of Cities (Volume 1) (p. 88). Springer Nature.
  • Juárez, G. E., Menéndez, F. D., Lafuente, C. H., Pérez, J., Franco, L., & Rivero, C. R. (2022, March). Analysis and simulation of social behavior during the COVID-19 pandemic in Argentina, using intelligent agents. In 2022 IEEE World Engineering Education Conference (EDUNINE) (pp. 1-6). IEEE.
  • Jung, C., Ahad, A., Jeon, Y., & Kwon, Y. (2022, May). SWARMFLAWFINDER: Discovering and Exploiting Logic Flaws of Swarm Algorithms. In 2022 IEEE Symposium on Security and Privacy (SP) (pp. 1808-1825). IEEE.
  • Júnior, E. C. B., Rios, V. P., Dodonov, P., Vilela, B., & Japyassú, H. F. (2022). Effect of behavioural plasticity and environmental properties on the resilience of communities under habitat loss and fragmentation. Ecological Modelling, 472, 110071.
  • Jones, B., & Swanson, H. (2022). A Framework for Assessing Teacher’s Readiness for Pedagogical Transformation. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Kadaverugu, R., Biniwale, R., & Matli, C. Process-Based Scenario Analyses of Future Socio-Environmental Systems: Recent Efforts and a Salient Research Agenda for Decision-Making. Modeling and Simulation of Environmental Systems, 319-330.
  • Kafai, Y. B., Xin, Y., Fields, D., & Tofel‐Grehl, C. (2022). Teaching and learning about respiratory infectious diseases: A scoping review of interventions in K‐12 education. Journal of Research in Science Teaching, 59(7), 1274-1300.
  • Kaligotla, C., Yücesan, E., & Chick, S. E. (2022). Diffusion of competing rumours on social media. Journal of Simulation, 16(3), 230-250.
  • Kamath, R., Sun, Z., & Hermans, F. (2022). Policy instruments for green-growth of clusters: Implications from an agent-based model. Environmental Innovation and Societal Transitions, 43, 257-269.
  • Kamiebisu, R., Saso, T., Nakao, J., Liu, Z., Nishi, T., & Matsuda, M. (2022). Use cases of the platform for structuring a smart supply chain in discrete manufacturing. Procedia CIRP, 107, 687-692.
  • Kane, A., Ayllón, D., O’Sullivan, R. J., McGinnity, P., & Reed, T. E. (2022). Escalating the conflict? Intersex genetic correlations influence adaptation to environmental change in facultatively migratory populations. Evolutionary Applications.
  • Kang, X., Wu, Y., Yan, D., Zhu, Y., Yao, Y., & Sun, H. (2022). A novel approach for occupants' horizontal and vertical movement modeling in non-residential buildings using Immersive Virtual Environment (IVE). Sustainable Cities and Society, 104193.
  • Kaniyamattam, K. (2022). 71 Agent-Based Modeling: A Historical Perspective and Comparison to Other Modeling Techniques. Journal of Animal Science, 100(Supplement_3), 32-33.
  • Kappenberger, J., Theil, K., & Stuckenschmidt, H. (2022). Evaluating the Impact of AI-Based Priced Parking with Social Simulation. In International Conference on Social Informatics (pp. 54-75). Springer, Cham.
  • Karkalas, S. (2022). Simplifying authoring and facilitating component reuse of programming tutors (Doctoral dissertation, Birkbeck, University of London).
  • Kaur, H. (2023). Role of Multi-agent Systems in Health Care: A Review. Emerging Technologies in Data Mining and Information Security, 367-378.
  • Kaur, N., & Kaur, H. (2022). A Multi-agent Based Evacuation Planning for Disaster Management: A Narrative Review. Archives of Computational Methods in Engineering, 1-29.
  • Kawai, Y. (2022). Agent-Based Tsunami Crowd Evacuation Simulation for Analysis of Evacuation Start Time and Disaster Rate in Zushi City. In International Conference on Information Technology in Disaster Risk Reduction (pp. 63-75). Springer, Cham.
  • Kelter, J., Wilensky, U., & Potvin, J. (2022). Introducing Land Constraints to Macroeconomic Agent-based Models. Proceedings of the 2022 Conference of The Computational Social Science Society of the Americas.
  • Kelter, J. Wit, J., Conboy, W., Potvin, J., & Wilensky, U. (2022). Poster: A General-Purpose ‘Economic Petri Dish’ ABM with ‘Land’ and ‘Organization’ to Test Indexed Pricing Methods for Stability and Resilience. The Computational Social Science Society of the Americas (CSS) 2022.
  • Khanolkar, O. (2022). An Exploration Of The Relation between Neighborhood Resource, Crime, And The Development Of Paranoia. Modern Psychological Studies, 28(1), 4.
  • Kianpour, M., Kowalski, S. J., & Øverby, H. (2022). Advancing the concept of cybersecurity as a public good. Simulation Modelling Practice and Theory, 102493.
  • Kieu, G. P. S. M. Simulating civil emergency evaluation with Inverse Generative Social Science. In International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) (p. 24).
  • Kilani, R., Zouinkhi, A., Bajic, E., & Abdelkrim, M. N. (2022, June). Socialization of smart communicative objects in Industrial Internet of Things. In 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022.
  • Kim, K., Kaviari, F., Pant, P., & Yamashita, E. (2022). An agent-based model of short-notice tsunami evacuation in Waikiki, Hawaii. Transportation research part D: transport and environment, 105, 103239.
  • Kinner, T., & Whitaker, E. T. (2022). A Framework for the Design and Development of Adaptive Agent-Based Simulations to Explore Student Thinking and Performance in K-20 Science. In International Conference on Human-Computer Interaction (pp. 190-206). Springer, Cham.
  • Khan, A. A., & Abonyi, J. (2022). Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy. Sustainability, 14(15), 9796.
  • Khan, M. A., El Sayed, H., Malik, S., Zia, M. T., Alkaabi, N., & Khan, J. (2022). A Journey towards Fully Autonomous Driving-Fueled by a Smart Communication System. Vehicular Communications, 100476.
  • Khanolkar, O. (2022). An Exploration Of The Relation between Neighborhood Resource, Crime, And The Development Of Paranoia. Modern Psychological Studies, 28(1), 4.
  • Khodr, H., Kothiyal, A., Bruno, B., & Dillenbourg, P. (2022). An Assessment Framework for Complex Systems Understanding. In Proceedings of the 16th international conference on Learning sciences (No. CONF).
  • Killeen, P., Kiringa, I., & Yeap, T. (2022). Unsupervised Dynamic Sensor Selection for IoT-based Predictive Maintenance of a Fleet of Public Transport Buses. ACM Transactions on Internet of Things.
  • Kim, K., Kaviari, F., Pant, P., & Yamashita, E. (2022). An agent-based model of short-notice tsunami evacuation in Waikiki, Hawaii. Transportation Research Part D: Transport and Environment, 105, 103239.
  • Kim, Y., & Cho, N. (2022). A Simulation Study on Spread of Disease and Control Measures in Closed Population Using ABM. Computation, 10(1), 2.
  • Kobbaey, T., & Bilquise, G. (2023). Agent-Based Simulations for Aircraft Boarding: A Critical Review. In International Conference on Emerging Technologies and Intelligent Systems (pp. 42-52). Springer, Cham.
  • Kooshknow, S. M. M., Herber, R., & Ruzzenenti, F. (2022). Are electricity storage systems in the Netherlands indispensable or doable? Testing single-application electricity storage business models with exploratory agent-based modeling. Journal of Energy Storage, 48, 104008.
  • Koralewski, T. E., Wang, H. H., Grant, W. E., Brewer, M. J., & Elliott, N. C. (2022). Evaluation of Areawide Forecasts of Wind-borne Crop Pests: Sugarcane Aphid (Hemiptera: Aphididae) Infestations of Sorghum in the Great Plains of North America. Journal of Economic Entomology.
  • Kořínek, M., & Štekerová, K. (2022). Smart Cities: GIS Data for Realistic Simulations.
  • Kurchyna, V., Rodermund, S., Berndt, J.O., Spaderna, H., Timm, I.J. (2022). KI 2022: Advances in Artificial Intelligence. KI 2022. Lecture Notes in Computer Science(), vol 13404. Springer, Cham. https://doi.org/10.1007/978-3-031-15791-2_12
  • Kurchyna, V., Rodermund, S., Berndt, J. O., Spaderna, H., & Timm, I. J. (2022). Health and Habit: An Agent-based Approach. In German Conference on Artificial Intelligence (Künstliche Intelligenz) (pp. 131-145). Springer, Cham.
  • Kurdi, H., Alzuhair, A., Alotaibi, D., Alsweed, H., Almoqayyad, N., Albaqami, R., ... & Islam, A. A. A. (2022). Crowd Evacuation in Hajj Stoning Area: Planning through Modeling and Simulation. Sustainability, 14(4), 2278.
  • Kusumah, H., & Wasesa, M. (2023). Unraveling the Most Influential Determinants of Residential Segregation in Jakarta: A Spatial Agent-Based Modeling and Simulation Approach. Systems, 11(1), 20.
  • Laatabi, A., Becu, N., Marilleau, N., Amalric, M., Pignon-Mussaud, C., Anselme, B., ... & Rousseaux, F. (2022). LittoSIM-GEN: A generic platform of coastal flooding management for participatory simulation. Environmental Modelling & Software, 105319.
  • Lamarins, A., Fririon, V., Folio, D., Vernier, C., Daupagne, L., Labonne, J., ... & Oddou‐Muratorio, S. (2022). Importance of interindividual interactions in eco‐evolutionary population dynamics: The rise of demo‐genetic agent‐based models. Evolutionary Applications.
  • Lancel, S., Chapurlat, V., Dray, G., & Martin, S. (2022). Emergency evacuation in a supermarket during a terrorist attack: towards a possible modelling of the influence of affordances on the evacuation behavior of agents in a complex virtual environment. Journal of Safety Science and Resilience.
  • Lambert, S. G., Fiedler, B. L., Hershenow, C. S., Abrahamson, D., & Gorlewicz, J. L. (2022). A tangible manipulative for inclusive quadrilateral learning. Journal on Technology & Persons with Disabilities. [Winner: Best Submission—Dr. Arthur I. Karshmer Award for Assistive Technology Research]
  • Lang, D., & Ertsen, M. W. (2022). Conceptualising and Implementing an Agent-Based Model of an Irrigation System. Water, 14(16), 2565.
  • Lancel, S., Chapurlat, V., Dray, G., & Martin, S. (2022). Emergency evacuation in a supermarket during a terrorist attack: towards a possible modelling of the influence of affordances on the evacuation behavior of agents in a complex virtual environment. Journal of Safety Science and Resilience.
  • Lapp, M., & Long, C. (2022). A new approach to agent-based models of Community Resource Management based on the analysis of cheating, monitoring, and sanctioning. Ecological Modelling, 468, 109946.
  • Latif, R., Ahmed, M. U., Tahir, S., Latif, S., Iqbal, W., & Ahmad, A. (2022). A novel trust management model for edge computing. Complex & Intelligent Systems, 8(5), 3747-3763.
  • Lawless, W.F. Interdependent Autonomous Human–Machine Systems: The Complementarity of Fitness, Vulnerability and Evolution. Entropy 2022, 24, 1308. https://doi.org/10.3390/e24091308
  • Lee, S. (2022). Networks and Organizing Processes in Online Social Media. Media and Communication, 10(2), 1-4.
  • Lehmann, A., Mazzetti, P., Santoro, M., Nativi, S., Masò, J., Serral, I., ... & Giuliani, G. (2022). Essential earth observation variables for high-level multi-scale indicators and policies. Environmental Science & Policy, 131, 105-117.
  • Leon, F. (2022). ActressMAS, a. NET Multi-Agent Framework Inspired by the Actor Model. Mathematics, 10(3), 382.
  • Leonard‐Duke, J., Hung, C., Sharma, A., & Peirce, S. M. (2022). Multi‐scale Computational Model of Endothelial Cell‐Pericyte Coupling in Idiopathic Pulmonary Fibrosis. The FASEB Journal, 36.
  • Leoni, S. (2022). An Agent-Based Model for Tertiary Educational Choices in Italy. Research in Higher Education, 63(5), 797-824.
  • Levy, M., Dabholkar, S., Zhao, L., Juhl, S., Levites, L., Mills, J., Wu, S., Peel, A., Horn, M.S., & Wilensky, U. (2022). Conceptualizing and operationalizing equity-focus in designing Computational Thinking (CT) integrated science and mathematics curricula. Paper accepted to the Annual Meeting of the American Educational Research Association (AERA) 2022. San Diego, CA.
  • Levy, M., Peel, A., Dabholkar, S., Zhao, L., Juhl, S., Levites, L., Mills, J., Wu, S., Horn, M.S., & Wilensky, U. (2022). Co-Designing to Understand Equity-Focus in Computational Thinking (CT) Integrated Science Curricula. the 2022 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Li, C., Wang, H., & Song, R. (2022). Mobility-Aware Offloading and Resource Allocation in NOMA-MEC Systems via DC. IEEE Communications Letters.
  • Li, L., Wan, Y., Plewczynski, D., & Zhi, M. Simulation Model on Network Public Opinion Communication Model of Major Public Health Emergency and Management System Design. Scientific Programming, 2022.
  • Li, M., Huang, P., Chen, Z., & Chen, W. (2022, December). Patient Empowerment in Improving Health Care Workers’ Hand Hygiene Compliance: Evidence from an Agent-based Simulation. In 2022 International Conference on Advanced Enterprise Information System (AEIS) (pp. 95-100). IEEE.
  • Li, S., Hui, B., Jin, C., Liu, X., Xu, F., Su, C., & Li, T. (2022). Considering Farmers’ Heterogeneity to Payment Ecosystem Services Participation: A Choice Experiment and Agent-Based Model Analysis in Xin’an River Basin, China. International journal of environmental research and public health, 19(12), 7190.
  • Li, X., Li, J., Huang, Y., He, J., Liu, X., Dai, J., & Shen, Q. (2022). Construction enterprises’ adoption of green development behaviors: An agent-based modeling approach. Humanities and Social Sciences Communications, 9(1), 1-11.
  • Liang, D., Cong, Z., & Cao, G. (2022). Examination of Diffusion Patterns of Tornado Warning using Agent-based Model and Simulation. Weather, Climate, and Society.
  • Libkind, S., Baas, A., Halter, M., Patterson, E., & Fairbanks, J. (2022). An Algebraic Framework for Structured Epidemic Modeling. arXiv preprint arXiv:2203.16345.
  • Liew, C. W., Polanco, L., Manalang, K., & Kurt, R. A. (2022). An experimental and computational approach to unraveling interconnected TLR signaling cascades. Informatics in Medicine Unlocked, 100939.
  • Linares Martínez, F., Miguel Quesada, F. J., & Kohl, M. (2022). Patrones de homofilia resilientes en redes de amistad juvenil: estudio de caso mediante un experimento de simulación computacional. Revista Española de Investigaciones Sociologicas, (117).
  • Lin, J. W., Cheng, T. S., & Linn, G. (2022). The impacts of modelling-based SSI teaching module on preservice teachers’ decision making–a case of Dongfeng Highway route selection. International Journal of Science Education, 1-21.
  • Lindgren, R., & DeLiema, D. (2022). Viewpoint, embodiment, and roles in STEM learning technologies. Educational technology research and development, 1-26.
  • Liu, B., Shao, Y. F., Liu, G., & Ni, D. (2022). An Evolutionary Analysis of Relational Governance in an Innovation Ecosystem. SAGE Open, 12(2), 21582440221093044.
  • Liu, C., Liu, Z., & Chai, Y. (2022). Review of Virtual Simulation of Crowd Motion for Urban Emergency Management. Transportation Research Record, 03611981221141429.
  • Liu, J., Cao, L., Zhang, D., Chen, Z., Lian, X., Li, Y., & Zhang, Y. (2022). Optimization of Site Selection for Emergency Medical Facilities considering the SEIR Model. Computational Intelligence and Neuroscience, 2022.
  • Liu, J., Zhang, M., Xia, Y., Zheng, H., & Chen, C. (2022). Using agent-based modeling to assess multiple strategy options and trade-offs for the sustainable urbanization of cultural landscapes: A case in Nansha, China. Landscape and Urban Planning, 228, 104555.
  • Liu, X., Dong, J., Cui, P., Wang, M., & Guo, X. (2022). Collaborative Supply Mechanism of Government-Subsidized Rental Housing from the Perspective of Tripartite Evolutionary Game in Metropolitan Cities of China. Computational Intelligence and Neuroscience, 2022.
  • Liu, Y., Xiong, Z., Hu, Q., Niyato, D., Zhang, J., Miao, C., ... & Tian, Z. (2022). VRepChain: A Decentralized and Privacy-preserving Reputation System for Social Internet of Vehicles Based on Blockchain. IEEE Transactions on Vehicular Technology.
  • Liu, Y. (2022). Energy consumption inequality in China: What can an agent-based model tell us?. Energy & Environment, 0958305X221120257.
  • Liu, Y., Zhang, C., Yan, Y., Zhou, X., Tian, Z., & Zhang, J. (2022). A semi-centralized trust management model based on blockchain for data exchange in iot system. IEEE Transactions on Services Computing.
  • Liu, Z., & Yang, G. (2022). Large-scale traffic flow simulation based on intelligent PSO. In MATEC Web of Conferences (Vol. 355). EDP Sciences.
  • Livia-Diana, I., & Delcea, C. (2022). Risk Evaluation in Public Spaces Evacuation. In Eurasian Business and Economics Perspectives (pp. 127-141). Springer, Cham.
  • Lu, P., Chen, D., & Li, B. (2022). Simulating Rise and Fall Cycles of Vietnam Empires. Fundamental Research.
  • Lu, P., Chen, D., Li, Y., Wang, X., & Yu, S. (2022). Agent-Based Model of Mass Campus Shooting: Comparing Hiding and Moving of Civilians. IEEE Transactions on Computational Social Systems.
  • Lu, P., Li, Y., Wen, F., & Chen, D. (2022). Social Knowledge Enhances Collective Safety: Computational Models and Simulations. IEEE Transactions on Computational Social Systems.
  • Lu, P., Wen, F., Li, Y., & Chen, D. (2022). Individual behaviors, social learning, and swarm intelligence: Real case and counterfactuals. Expert Systems with Applications, 207, 117878.
  • Lu, P., Zhang, Z., Liu, C., & Li, M. (2022). Unification conditions of human civilization patterns: based on multi-agent modeling of early Chinese history (770 BC to 476 BC). Archaeological and Anthropological Sciences, 14(10), 1-16.
  • Lu, X., Ma, H., & Wang, Z. (2022). Analysis of OODA Loop based on Adversarial for Complex Game Environments. arXiv preprint arXiv:2203.15502.
  • Lu, X. (2022, December). Research on traffic state of private car in urban area based on agent cellular automata model. In International Conference on Smart Transportation and City Engineering (STCE 2022) (Vol. 12460, pp. 856-863). SPIE.
  • Lu, Y., Liu, S., & Li, C. (2022). Understanding the Effect of Management Factors on Construction Workers’ Unsafe Behaviors Through Agent-Based Modeling. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-13.
  • Luan, Z., Zhengjun, L., & Dequn, Z. (2022). Agent-based Research on Power Absorption Simulation Analysis of Renewable Energy. Journal of System Simulation, 34(1), 170.
  • Lucas, L., Helikar, T., & Dauer, J. (2022). Revision as an essential step in modeling to support predicting, observing, and explaining cellular respiration system dynamics. International Journal of Science Education, 1-28.
  • Lungeanu, A., DeChurch, L. A., & Contractor, N. S. (2022). Leading teams over time through space: Computational experiments on leadership network archetypes. The Leadership Quarterly, 101595.
  • Lv, Y., Shi, M., & Hu, Q. (2022). COVID-19 SIR network modeling and prediction. Highlights in Science, Engineering and Technology, 1, 433-440.
  • Lyu, X., Han, Q., & de Vries, B. (2022). A hypothetical urban layout generation model for exploring land use impacts on travel behavior. Travel Behaviour and Society, 28, 317-329.
  • Lyubchik, O. A., Yadlovska, O. S., Vavzhenchuk, S. Y., Korolchuk, O., & Stakhiv, O. (2022). Agent-based models: an effective tool in Ukrainian state formation and legal regulation. Revista Científica General José María Córdova, 20(38), 341-353.
  • Ma, N., Huang, Z., & Qi, Y. (2022). Simulation Study on Complex Systems of Forest Biomass Power Generation Supply Chain in China. Computational Intelligence and Neuroscience, 2022.
  • Ma, X., Wang, D., Zheng, N., & Zhang, S. (2022). Aggregation and Adjustment mechanisms for disaster relief task allocation with uneven distribution. Journal of Industrial & Management Optimization.
  • Ma, Y., & Shen, Z. (2022). Agent-Based Simulation for Decision-Making Support of Spatial Strategy for Large-Scale Shopping Center Development. In Strategic Spatial Planning Support System for Sustainable Development (pp. 101-128). Springer, Cham.
  • Ma, Y., & Shen, Z. (2022). Simulation of Urban Growth and Household Aggregation for Planning Support of Local Spatial Strategic Plan. In Strategic Spatial Planning Support System for Sustainable Development (pp. 39-72). Springer, Cham.
  • Ma, Y., & Shen, Z. (2022). The Environment for Accommodating Agents and Representing Urban Planning Conditions. In Strategic Spatial Planning Support System for Sustainable Development (pp. 21-38). Springer, Cham.
  • Madamba, T., Moreira, R. G., Castell‐Perez, E., Banerjee, A., & da Silva, D. (2022). Agent‐based simulation of cross‐contamination of Escherichia coli O157: H7 on lettuce during processing and temperature fluctuations during storage in a produce facility. Part 2: Model implementation. Journal of Food Process Engineering, e13983.
  • Mahmood, B., & Mahmood, Y. (2022). Network-Based Method for Dynamic Burden-Sharing in the Internet of Things (IoT). In International Conference on Emerging Technology Trends in Internet of Things and Computing (pp. 79-90). Springer, Cham.
  • Mahmoud, R. M., & Youssef, A. M. (2022). A computational framework for supporting architectural education of spaces’ furnishing design. International Journal of Architectural Computing, 14780771221097683.
  • Males, L., Sumic, D., & Rosic, M. (2022, May). A Simulation Model of Autonomous Ship Firefighting. In 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT) (Vol. 1, pp. 130-134). IEEE.
  • Malhotra, R. (2022). More Than Habitat Loss and Fragmentation: The Effect of Human-Modified Landscapes on the Spatiotemporal Use and Interactions of Mesocarnivores (Doctoral dissertation).
  • Malik, J., Mahdavi, A., Azar, E., Putra, H. C., Berger, C., Andrews, C., & Hong, T. (2022). Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings. Building and Environment, 109016.
  • Malik, S., Khan, M. A., El-Sayed, H., Khan, J., & Ullah, O. (2022). How Do Autonomous Vehicles Decide?. Sensors, 23(1), 317.
  • Mallick, R. B. (2022). A Probabilistic Understanding of the Effect of Voids and Layer Thickness on Interconnectivity of Voids in Asphalt Mixes: An Agent-Based Modeling Approach. Journal of Transportation Engineering, Part B: Pavements, 148(2), 06022001.
  • Maltseva, S., Kornilov, V., Barakhnin, V., & Gorbunov, A. (2022). Self-Organization in Network Sociotechnical Systems. Complexity, 2022.
  • Mamada, R. (2022). Spatial Cournot Competitions Revisited: The Effect of the Internalization of the Costs of Point Source Pollution and Congestion by Spatial Cournot Duopolists. Available at SSRN 4221551.
  • Manastîrschi, S., Iapăscurtă, V., & Belîi, A. (2022). System dynamics models for clinical anesthesia (on the example of propofol). Conferinţa ştiinţifică anuală" Cercetarea în biomedicină și sănătate: calitate, excelență și performanță", 2022.
  • Manson, S., An, L., Clarke, K. C., Heppenstall, A., Koch, J. (2022). Methodological Issues of Spatial Agent-Based Models. Journal of Artificial Societies and Social Simulation, 23(1). https://doi.org/10.18564/JASSS.4174
  • Maqbool, A., Mirza, A., Afzal, F., Shah, T., Khan, W. Z., Zikria, Y. B., & Kim, S. W. (2022). System-Level Performance Analysis of Cooperative Multiple Unmanned Aerial Vehicles for Wildfire Surveillance Using Agent-Based Modeling. Sustainability, 14(10), 5927.
  • Maqsood, Z., Clark, J. C., Martin, E. M., Cheung, Y. F. H., Morán, L. A., Watson, S. E., ... & Watson, S. P. (2022). Experimental validation of computerised models of clustering of platelet glycoprotein receptors that signal via tandem SH2 domain proteins. PLOS Computational Biology, 18(11), e1010708.
  • Martin, A. A., & Barnas, A. F. (2022). Sitting ducks: Strategies to increase recruitment in common eiders (Somateria mollissima) facing polar bear (Ursus maritimus) predation.
  • Martínez, F. L., Quesada, F. J. M., & Kohl, M. (2022). Patrones de homofilia resilientes en redes de amistad juvenil: estudio de caso mediante un experimento de simulación computacional. REIS: Revista Española de Investigaciones Sociológicas, (177), 43-68.
  • Martínez, Y. N. E., Santos, F. E. B., & Chavarria, P. S. (2022). La integración de las TIC en la educación superior: Aprendizajes a partir del contexto covid-19. Ciencia Latina Revista Científica Multidisciplinar, 6(2), 4260-4277.
  • Mariam, S. (2022). AGENT-BASED MODELING ON PURCHASE DECISIONS: THE IMPACT OF SOCIAL MEDIA PHENOMENA. Jurnal Ekonomi, 11(03), 1749-1756.
  • Marín Gutiérrez, D. (2022). Integration of social aspects in a multi-agent platform running in a supercomputer (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Maru, V., Krishnan, K., Nannapaneni, S., & Arishi, A. (2022). Decentralized Supply Chain Network for Emerging Issues Using Mechanism Design and Agent-Based Modeling. Industrial and Systems Engineering Review, 10(1), 28-33.
  • Marvuglia, A., Bayram, A., Baustert, P., Gutiérrez, T. N., & Igos, E. (2022). Agent-based modelling to simulate farmers’ sustainable decisions: Farmers’ interaction and resulting green consciousness evolution. Journal of Cleaner Production, 332, 129847.
  • Masuda, S., Bahr, K., Tsuchiya, N., & Takemori, T. (2022). Agent based simulation with data driven parameterization for evaluation of social acceptance of a geothermal development: a case study in Tsuchiyu, Fukushima, Japan. Scientific Reports, 12(1), 1-13.
  • Mayerhoffer, D. M., & Schulz, J. (2022). Perception and privilege. Applied Network Science, 7(1), 1-25.
  • Mayes, R., Owens, D., Dauer, J., & Rittschof, K. (2022). A Quantitative Reasoning Framework and the Importance of Quantitative Modeling in Biology. Applied and Computational Mathematics, 11(1), 1-17.
  • Mazzetti, P., Nativi, S., Santoro, M., Giuliani, G., Rodila, D., Folino, A., ... & Lehmann, A. (2022). Knowledge formalization for Earth Science informed decision-making: The GEOEssential Knowledge Base. Environmental Science & Policy, 131, 93-104.
  • McCulloch, J., Ge, J., Ward, J. A., Heppenstall, A., Polhill, J. G., & Malleson, N. (2022). Calibrating Agent-Based Models Using Uncertainty Quantification Methods. Journal of Artificial Societies and Social Simulation, 25(2).
  • McGough, A., Kavak, H., & Mahabir, R. (2022). Revisiting Linus’ Law in OpenStreetMap: An Agent-Based Approach. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 123-133). Springer, Cham.
  • McIntire, E. J., Chubaty, A. M., Cumming, S. G., Andison, D., Barros, C., Boisvenue, C., ... & Stewart, F. E. (2022). PERFICT: A Re‐imagined foundation for predictive ecology. Ecology Letters.
  • McMullen, P. R. (2022). A Heuristic Search Approach to Multidimensional Scaling. American Journal of Operations Research, 12(5), 179-193.
  • Medeiros-Sousa, A. R., Laporta, G. Z., Mucci, L. F., & Marrelli, M. T. (2022). Epizootic dynamics of yellow fever in forest fragments: An agent-based model to explore the influence of vector and host parameters. Ecological Modelling, 466, 109884.
  • Meles, T. H., & Ryan, L. (2022). Adoption of renewable home heating systems: An agent-based model of heat pumps in Ireland. Renewable and Sustainable Energy Reviews, 169, 112853.
  • Meegada, S. S., & Kandaswamy, S. (2022, April). Comparison of Viral Information Spreading Strategies in Social Media. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 247). Springer Nature.
  • Meese, M. E., Mortuza, M. A., Wharton, M. M., Hala, D., Kaiser, K., Wells, D., ... & Quigg, A. (2022). Project Title: The Fate and Toxicity of Microplastics and Persistent Pollutants in the Shellfish and Fish of Matagorda Bay.
  • Mhamdi, H., Soufiene, B. O., Zouinkhi, A., Ali, O., & Sakli, H. (2022). On this page. Computational Intelligence and Neuroscience, 2, 3.
  • M’hammed, S., Baudry, D., & Mustafee, N. Modelling and simulation of operation and maintenance strategy for offshore wind farms based on. Journal of Intelligent Manufacturing.
  • Miao, Q., Lin, H., Hu, J., & Wang, X. Privacy-Preserved Mobile Crowdsensing for Intelligent Transportation Systems. Intelligent Cyber-Physical Systems for Autonomous Transportation, 267.
  • Mias, A. (2022). UAB Open Labs. Open Education Week.
  • Miguel, L. B. J., Gonzalez-R, P. L., Jose, L., Canca, D., & Calle, M. (2022). A multi-agent approach to the truck multi-drone routing problem. Expert Systems with Applications, 116604.
  • Milne, R. J., Cotfas, L. A., & Delcea, C. (2022). Minimizing health risks as a function of the number of airplane boarding groups. Transportmetrica B: Transport Dynamics, 10(1), 901-922.
  • Minaei, M., & Vahidnia, M. H. (2022). Flood prevention solutions using remote sensing and agent-based modeling (Case study: Shoush city). Journal of Natural Environmental Hazards, 1-1.
  • Mirzaei, A. (2022). Development of a Dynamic Model for Health Information Seeking Behaviour (Doctoral dissertation).
  • Mo, J., & Polly, P. D. The role of dispersal, selection intensity, and extirpation risk in resilience to climate change: A trait‐based modelling approach. Global Ecology and Biogeography.
  • Mohammed, A., & Ukai, T. (2022). Agent-based modelling for spatiotemporal patterns of urban land expansion around university campuses. Modeling Earth Systems and Environment, 1-15.
  • Monti, C., Pangallo, M., Morales, G. D. F., & Bonchi, F. (2022). On learning agent-based models from data. arXiv preprint arXiv:2205.05052.
  • Moore, J.C., R.B. Boone, A. Koyama, and K. Holfeder. (2022). Enzymatic and detrital influences on the structure, function, and dynamics of spatially-explicit model ecosystems. Biochemistry.
  • Monaco, G. Simionato, M. G. C. A. Cimino, G. Vaglini, S. Senatore and G. Caricato, "Using Artificial Immune System to Prioritize Swarm Strategies for Environmental Monitoring," 2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2022, pp. 104-110, doi: 10.1109/CogSIMA54611.2022.9830665.
  • Moradi, M., Fard, K. R., & Akhlaqi, M. Y. (2022). A Recommender System Method for Children’s Education Using Mobile Technology. IEEE Access.
  • Morales‐Márquez, J., & Meloni, F. Soil fauna and its potential use in the ecological restoration of dryland ecosystems. Restoration Ecology, e13686.
  • Morán‐López, T., Benadi, G., Lara‐Romero, C., Chacoff, N., Vitali, A., Pescador, D., ... & Morales, J. M. Flexible diets enable pollinators to cope with changes in plant community composition. Journal of Ecology.
  • Mousavi, S. F., Sepehri, M. M., Khasha, R., & Mousavi, S. H. (2022). Improving vascular access creation among hemodialysis patients: An agent-based modeling and simulation approach. Artificial Intelligence in Medicine, 102253.
  • Moya, D., Copara, D., Amores, J., Muñoz, M., & Pérez-Navarro, Á. (2022). Characterization of energy consumption agents in the residential sector of Ecuador based on a national survey and geographic information systems for modelling energy systems. Enfoque UTE, 13(2), 68-97.
  • Mrela, A., Sokolov, O., Osinska, V., & Duch, W. (2022). Analysis of Dynamics of Emergence and Decline of Scientific Ideas Based on Optimistic and Pessimistic Fuzzy Aggregation Norms. In Asian Conference on Intelligent Information and Database Systems (pp. 327-339). Springer, Singapore.
  • Muñoz, G. A., Gil-Costa, V., & Marin, M. (2022). Efficient simulation of natural hazard evacuation for seacoast cities. International Journal of Disaster Risk Reduction, 103300.
  • Muoghalu, C. N., Achebe, P. N., & Aigbodioh, F. A. (2022). Effect of Increasing Node Density on Performance of Threshold-sensitive Stable Election Protocol. Int. J. Advanced Networking and Applications, 13(06), 5183-5187.
  • Musaeus, L. H., Sørensen, M. L. S. K., Palfi, B. S., Iversen, O. S., Klokmose, C. N., & Petersen, M. G. (2022, October). CoTinker: Designing a Cross-device Collaboration Tool to Support Computational Thinking in Remote Group Work in High School Biology. In Nordic Human-Computer Interaction Conference (pp. 1-12).
  • Musaeus, L. H., Tatar, D., & Musaeus, P. (2022). Computational Modelling in High School Biology: A Teaching Intervention. Journal of Biological Education, 1-17.
  • Musaeus, P. Computational Thinking-et TC på CT. Dansk Universitetspædagogisk Tidsskrift, 17(32), 137-141.
  • Narayanan, B. L. (2022). Complex network theoretical approach to investigate the interdependence between factors affecting subsurface radionuclide migration (Doctoral dissertation).
  • Narkar, A. R., Tong, Z., Soman, P., & Henderson, J. H. (2022). Smart biomaterial platforms: Controlling and being controlled by cells. Biomaterials, 121450.
  • Naugle, A., Russell, A., Lakkaraju, K., Swiler, L., Verzi, S., & Romero, V. (2022). The ground truth program: simulations as test beds for social science research methods. Computational and Mathematical Organization Theory, 1-19.
  • Nazir, M., Haque, H. M. U., & Saleem, K. (2022). A semantic knowledge based context-aware formalism for smart border surveillance system. Mobile Networks and Applications, 27(5), 2036-2048.
  • Nelissen, R. M., Muñoz, I. A., Muñoz, D. C., Kramer, M. R., & Hofstede, G. J. (2022, April). Efficient Redistribution of Scarce Resources Favours Hierarchies. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 3). Springer Nature.
  • Nesar, A. B., Mahmud, T., & Hossain, F. (2022, June). Simulating the Behaviour and Displacement of Women in Water-Stressed Areas. In 2022 IEEE World AI IoT Congress (AIIoT) (pp. 372-378). IEEE.
  • Nespeca, V., Comes, T., & Brazier, F. (2022, April). A Methodology to Develop Agent-Based Models for Policy Design in Socio-Technical Systems Based on Qualitative Inquiry. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 453). Springer Nature.
  • Netz, C., Ramesh, A., Gismann, J., Gupte, P. R., & Weissing, F. J. (2022). Details matter when modelling the effects of animal personality on the spatial distribution of foragers. Proceedings of the Royal Society B, 289(1970), 20210903.
  • Neves, J. E. D. A., Pedro, P. S. M., de Freitas Gomes Hernandez, M., & Junior, L. A. F. (2023). Simulation of the Implementation of Domestic Solar Systems Using Multi-agent Systems from Web Scraping. In Brazilian Technology Symposium (pp. 88-96). Springer, Cham.
  • Neuenfeldt-Júnior, A., & de Oliveira, B. (2022). An agent-based approach to simulate the containership stowage problem. Soft Computing, 1-15.
  • Neumayr, R. (2022). Agent-Based Semiology-Simulating office occupation patterns with conversation-based social models.
  • Nezamoddini, N., & Gholami, A. (2022). A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities. Smart Cities, 5(1), 318-347.
  • Ngo, V. (2022). SOCIAL NETWORK AND THE DIFFUSION OF INVESTMENT BELIEFS: THEORETICAL EXPERIMENT AND THE CASES OF GAMESTOP SAGA. Applied Finance Letters, 11, 36-49.
  • Niehorster-Cook, L. M. (2022). The Spreading-Activation Framework Does not Explain the Effects of Degree and Clustering on Spoken Word Recognition. In Conference of the Computational Social Science Society of the Americas (pp. 112-123). Springer, Cham.
  • Niemann, J. H. (2022). Learning Reduced Models for Large-Scale Agent-Based Systems (Doctoral dissertation).
  • Nikravan, M., & Kashani, M. H. (2022). A review on trust management in fog/edge computing: Techniques, trends, and challenges. Journal of Network and Computer Applications, 103402.
  • Nizamutdinov, M. M., Akhmetzyanova, M. I., & Aitova, Y. S. (2022). Modeling approaches and tools of the mutual influence of the migration activity of the population and economic development of the territories. Экономика промышленности, 15(3), 368.
  • Noeldeke, B. (2022). Promoting Agroforestry in Rwanda: the Effects of Policy Interventions Derived from the Theory of Planned Behaviour (No. dp-693). Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  • Noeldeke, B. C. E. (2022). Simulating human behaviour in social-ecological systems: farmers’ adoption of agricultural innovations (Doctoral dissertation, Hannover: Institutionelles Repositorium der Leibniz Universität Hannover).
  • Nogare, D. D., Chitnis, A.B. (2022). NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium
  • Norman, M. D., Silvey, P. E., Koehler, M. T., & Joe, K. C. (2022). Engineering Decentralized Enterprises: Emergent Mission Accomplishment Without Centralized Command and Control. In Conference of the Computational Social Science Society of the Americas (pp. 124-151). Springer, Cham.
  • Norouzi, D., Bafandeh Zendeh, A., & Honarmand Azimi, M. (2022). Modeling of overdue receivables in the city bank Using factor-based simulation (Northwestern provinces of the country). Journal of System Management.
  • Norton, D. E. (2022). A Course on Mathematical Modeling for the Life Sciences. PRIMUS, 32(2), 154-167.
  • Notestine, J. (2022). Sensitivity and Active Subspace Analysis. North Carolina State University Dissertation.
  • Nouri, A., Saghafian, B., Bazargan-Lari, M. R., Delavar, M., & Hassanjabbar, A. (2022). Impact of Penalty Policy on Farmers’ Overexploitation Based on Agent-Based Modeling Framework. Journal of Water Resources Planning and Management, 148(5), 04022015.
  • Nourisa, J., Zeller‐Plumhoff, B., & Willumeit‐Römer, R. (2022). CppyABM: An open‐source agent‐based modeling library to integrate C++ and Python. Software: Practice and Experience.
  • Novakovic, A., & Marshall, A. H. (2022). The CP-ABM Approach for Modelling COVID-19 Infection Dynamics and Quantifying the Effects of Non-Pharmaceutical Interventions. Pattern Recognition, 108790.
  • Nugroho, A. A., & Asrol, M. (2022). The Impact of Effectiveness of Luggage Arrangement on the Airplane Passengers' Boarding Process. Periodica Polytechnica Transportation Engineering.
  • Nurwidiana, N., Sopha, B. M., & Widyaparaga, A. (2022). Simulating Socio-Technical Transitions of Photovoltaics Using Empirically Based Hybrid Simulation-Optimization Approach. Sustainability, 14(9), 5411.
  • Nwokoye, C. H., & Madhusudanan, V. (2022). Epidemic Models of Malicious-Code Propagation and Control in Wireless Sensor Networks: An Indepth Review. Wireless Personal Communications, 1-30.
  • Nwokoye, C. H., Mbeledogu, N. N., Paul, R. U., & Ugwunna, C. (2022, October). Complementing malware epidemic agent-based models with routing protocols of communication networks using NetLogo. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 833-847). Singapore: Springer Nature Singapore.
  • NYAMI, R., TSHIBUABUA, F., BUSHABU, O. K., BULEWU, B. I., KABAMBI, J. N., ILONDO, J. M., & KABWIKA, J. M. (2022). Analyse et Conception par la méthode GAIA d’un Système Multi-Agent pour la simulation de l’assainissement de l’environnement urbain en RDC. Revue Internationale du Chercheur, 3(2).
  • Obiako, I. V. (2022). Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets and Alternative Fueling Infrastructures in Closed Sociotechnical Environments (Doctoral dissertation, University of South Alabama).
  • Ofițeru, I. D., & Picioreanu, C. (2022). No model is perfect, but some are useful. Science, 376(6596), 914-916.
  • Ogegbo, A. A., & Ramnarain, U. (2022). A systematic review of computational thinking in science classrooms. Studies in Science Education, 58(2), 203-230.
  • Ogunsakin, R., & Mehandjiev, N. (2022). Towards Autonomous Production: Enhanced Meta-heuristics Algorithm. Procedia Computer Science, 200, 1575-1581.
  • Oliver II, E. H. (2022). Assessing Critical Supply Chain Resilience against Critical Infrastructure Disruptions: A Model-Based Systems Engineering Perspective (Doctoral dissertation, The George Washington University).
  • Oliveira, H., Mendes, F., & Henriques, A. (2022). A investigação sobre o ensino e a aprendizagem de temas matemáticos publicada em 30 anos da revista Quadrante. Quadrante, 31(2), 32-62.
  • Orozco-Rivera, J., Ceballos, Y., & Castillo-Grisales, J. A. (2022). Análisis del alto flujo vehicular para una vía de acceso a Medellín usando simulación basada en agentes. Revista UIS Ingenierías, 21(1), 73-82.
  • Pacaux-Lemoine, M. P., Sallak, M., Sacile, R., Flemisch, F., & Leitão, P. (2022). Introduction to the special section humans and industry 4.0. Cognition, Technology & Work, 1-5.
  • Papamichael, I., Pappas, G., Siegel, J. E., & Zorpas, A. A. (2022). Unified waste metrics: A gamified tool in next-generation strategic planning. Science of The Total Environment, 154835.
  • Pavlović, B., Ivezić, D., & Živković, M. (2022). Transition pathways of household heating in Serbia: Analysis based on an agent-based model. Renewable and Sustainable Energy Reviews, 163, 112506.
  • Peel, A., Kelter, J., Zhao, L., Horn, M.S., Wilensky, U.(2022). Conjecture Mapping: An Approach to Conducting Design-Based Research with Embedded Co-design Cycles. Paper accepted to the Annual Meeting of the American Educational Research Association (AERA) 2022. San Diego, CA.
  • Peel, A., Kelter, J., Zhao, L., Horn, M.S., & Wilensky, U. (2022). A Design-Based Research Methodology Utilizing Conjecture Mapping to Frame Embedded Co-design Cycles. the 2022 Annual Meeting of the National Association of Research in Science Teaching (NARST). Vancouver, British Columbia.
  • Peel, A., Kelter, J., Zhao, L., Horn, M., & Wilensky, U. (2022). Designing learning environments with iterative conjecture mapping to support teachers’ computational thinking learning. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Pedroso Fabrin, B. H., & Ferrari, D. (2022). Investigation of fomite exposure risk to infectious diseases during aircraft boarding process using agent-based modeling. In AIAA AVIATION 2022 Forum (p. 3617).
  • Peel, A., Sadler, T. D., & Friedrichsen, P. (2022). Algorithmic Explanations: an Unplugged Instructional Approach to Integrate Science and Computational Thinking. Journal of Science Education and Technology, 1-14.
  • Pellegrino, M. (2022). Evaluation and comparison of calibration techniques for urban mobility behaviour ABM (Master's thesis).
  • Pellegrino, M., Lombardo, G., Cagnoni, S., & Poggi, A. (2022). High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation. Future Internet, 14(3), 83.
  • Peng, Y., Lopez, J. M. R., Santos, A. P., Mobeen, M., & Scheffran, J. (2022). Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil. Cities, 104161.
  • Pereira, J. C., Ferrari, D., Giarola, R., & Pedroso Fabrin, B. H. (2022). Modeling Panic Behavior in Aircraft Evacuation Simulation. In AIAA AVIATION 2022 Forum (p. 3830).
  • Perez, O. (2022). Transnational networked authority. Leiden Journal of International Law, 1-29.
  • Pérez-Martínez, H., Bauzá, F. J., Soriano-Paños, D., Gómez-Gardeñes, J., & Floría, L. M. (2022). Emergence, survival, and segregation of competing gangs. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(8), 083114.
  • Ponziani, F. A. (2022). Water-Based Shields Deployment on Terrain during Wildfire Spread: A Modelling Approach Using Distributed Information through Autonomous Agents. Environmental Sciences Proceedings, 17(1), 83.
  • Phillips, C. V., & Glover, M. (2022). How Much Ongoing Smoking Reduction is an Echo of the Initial Mass Education?. American Journal of Health Behavior, 46(1), 84-95.
  • Pietzsch B.W., Peter F.J. and Berger U. (2021) The Effect of Sanitation Felling on the Spread of the European Spruce Bark Beetle—An Individual-Based Modeling Approach. Front. For. Glob. Change 4:704930. https://doi.org/10.3389/ffgc.2021.704930
  • Pietzsch, B.W., Wudel, C. & Berger, U. Nonparametric upscaling of bark beetle infestations and management from plot to landscape level by combining individual-based with Markov chain models. Eur J Forest Res (2022). https://doi.org/10.1007/s10342-022-01512-1
  • Pike, T. D., Golden, S., Lowdermilk, D., Luong, B., & Rosado, B. (2022). Growing the simulation ecosystem: introducing Mesa Data to provide transparent, accessible, and extensible data pipelines for simulation development. SIMULATION, 00375497221077425.
  • Piou, C. (2022). La modélisation écologique pour la gestion des populations de locustes (Doctoral dissertation, Université de Montpellier).
  • Pitman, L., Nandakumar, G., & Richman, J. (2022). A Gamified Synthetic Environment for Evaluation of Counter-disinformation Solustions. Journal of Simulation Engineering.
  • Plikynas, D., Miliauskas, A., Laužikas, R., Dulskis, V., & Sakalauskas, L. (2022). The cultural impact on social cohesion: an agent-based modeling approach. Quality & Quantity, 1-32.
  • Poaquiza Yumbulema, K. M. (2022). Modelización basada en el individuo de un reactor PFR de lodos activados en fase aerobia y anaerobia utilizando el programa Netlogo (Bachelor's thesis, Quito: UCE).
  • Prandi, L., & Primiero, G. (2022). A logic for biassed information diffusion by paranoid agents in social networks. Journal of Logic and Computation.
  • Prata, J. C., Silva, C., Serpa, D., Soares, A. M., Gravato, C., & Silva, A. L. P. Mechanisms Influencing the Impact of Microplastics on Freshwater Benthic Invertebrates: Uptake Dynamics and Adverse Effects on Chironomus Riparius. Available at SSRN 4223058.
  • Pruett, L. J., Taing, A. L., Singh, N. S., Peirce, S. M., & Griffin, D. R. (2022). In silico optimization of heparin microislands in microporous annealed particle (MAP) hydrogel for endothelial cell migration. Acta Biomaterialia.
  • Qiu, H., Chen, Y., Zhang, H., Yi, W., & Li, Y. (2022). Evolutionary digital twin model with an agent-based discrete-event simulation method. Applied Intelligence, 1-17.
  • Queen, O., Jodoin, V., Pearcy, L. B., & Strickland, W. C. (2022). Agent-based Dynamics of a SPAHR Opioid Model on Social Network Structures. arXiv preprint arXiv:2202.12261.
  • Raab, R., Lenger, K., Stickler, D., Granigg, W., & Lichtenegger, K. (2022, May). An Initial Comparison of Selected Agent-Based Simulation Tools in the Context of Industrial Health and Safety Management. In Proceedings of the 2022 8th International Conference on Computer Technology Applications (pp. 106-112).
  • Rabb, N., Cowen, L., de Ruiter, J. P., & Scheutz, M. (2022). Cognitive cascades: How to model (and potentially counter) the spread of fake news. PloS one, 17(1), e0261811.
  • Radeef, H. R., Hassan, N. A., Abidin, A. R. Z., Mahmud, M. Z. H., Ismail, C. R., Abbas, H. F., & Mashros, N. (2022). Mixture design and test parameter effect on fracture performance of asphalt: a review. ASEAN Engineering Journal, 12(1), 27-39.
  • Radzvilas, M., De Pretis, F., Peden, W., Tortoli, D., & Osimani, B. (2022). Incentives for Research Effort: An Evolutionary Model of Publication Markets with Double-Blind and Open Review. Computational Economics, 1-44.
  • Ramadhana, W., Astawaa, I. G. S., Astutia, L. G., Putria, L. A. A. R., Suputraa, I. P. G. H., & Santiyasaa, I. W. Pengembangan Aplikasi Optimasi Rute Destinasi Wisata di Banyuwangi Menggunakan Modern Android Development (MAD) Pattern. Jurnal Elektronik Ilmu Komputer Udayana p-ISSN, 2301, 5373.
  • RAHMANI, A., & KOHILI, M. (2022). Modélisation et simulation système multi-agent de la Propagation d’une épidémie covid-19 (Doctoral dissertation, UNIVERSITE AHMED DRAIA-ADRAR).
  • Rahmoeller, M., & Steinweg, J. M. (2022). Implementation of a New Quantitative Biology Course: Assessment of Students’ Abilities and Confidence. PRIMUS, 32(3), 346-366.
  • Rajbanshi, B., & Guruacharya, A. (2022). Panama: An Open-Source Educational App for Ion Channel Biophysics Simulation. Frontiers in neuroinformatics, 16.
  • Rajendran, V., Ramasamy, R. K., & Mohd-Isa, W. N. (2022). Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach. Future Internet, 14(2), 56.
  • Ramírez-Ávila, G. M., Depickère, S., Deneubourg, J. L., & Kurths, J. (2022). A simple game and its dynamical richness for modeling synchronization in firefly-like oscillators. The European Physical Journal Special Topics, 1-10.
  • Rankin, Naomi. (2022). An Agent-Based Model of COVID-19 on the Diamond Princess Cruise Ship. SIAM Undergraduate Research Online. 15. 10.1137/21S1462520.
  • Rappel, O., Ben-Asher, J., & Bruckstein, A. (2022). Exploration and Coverage with Swarms of Settling Agents. arXiv preprint arXiv:2209.05512.
  • Rates, C. A., Mulvey, B. K., Chiu, J. L., & Stenger, K. (2022). Examining ontological and self-monitoring scaffolding to improve complex systems thinking with a participatory simulation. Instructional Science, 1-23.
  • Ray, S. K., Alani, M. M., & Ahmad, A. (2022). Big Data for Educational Service Management. In Big Data and Blockchain for Service Operations Management (pp. 139-161). Springer, Cham.
  • Razzaq, S., Dar, A. R., Shah, M. A., Khattak, H. A., Ahmed, E., El-Sherbeeny, A. M., ... & Rauf, H. T. (2022). Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles. Applied Sciences, 12(3), 1049.
  • Razzouqi, M., & Boulmakoul, A. Multiagent Modeling for Pedestrian Risk Assessment. In Smart Trajectories (pp. 317-330). CRC Press.
  • Reda, M., Noël, C., Settembre, N., Chambert, J., Lejeune, A., Rolin, G., & Jacquet, E. (2022). An agent-based model of vibration-induced intimal hyperplasia. Biomechanics and Modeling in Mechanobiology, 1-25.
  • Regnath, F., Berger, C., & Mahdavi, A. (2022, May). The impact of occupants' energy awareness and thermal preferences on buildings' performance. In CLIMA 2022 conference.
  • Reinhardt, O., Warnke, T., & Uhrmacher, A. M. (2022). A Language for Agent-based Discrete-event Modeling and Simulation of Linked Lives. ACM Transactions on Modeling and Computer Simulation (TOMACS), 32(1), 1-26.
  • Reinhardt, O., Warnke, T., & Uhrmacher, A. M. (2022). Agent-Based Modelling and Simulation with Domain-Specific Languages. In Towards Bayesian Model-Based Demography (pp. 113-134). Springer, Cham.
  • Ribas-Xirgo, L. (2022). A state-based multi-agent system model of taxi fleets. Multimedia Tools and Applications, 1-20.
  • Ribeiro, S., Breda, A., & Rocha, E. (2022). COMPUTATIONAL THINKING IN UPPER-SECONDARY EDUCATION: A SYSTEMATIC LITERATURE REVIEW. INTED2022 Proceedings, 2650-2659.
  • Richman, J., Pitman, L., & Nandakumar, G. S. (2022). A Gamefied Synthetic Environment for Evaluation of Counter-Disinformation Solutions. Journal of Simulation Engineering, 3, 7-1.
  • Rivera, M., Toledo-Jacobo, L., Romero, E., Oprea, T. I., Moses, M. E., Hudson, L. G., ... & Grimes, M. M. (2022). Agent-based modeling predicts Rac1 is critical for ovarian cancer metastasis. Molecular Biology of the Cell, mbc-E21.
  • Rivera-Rogel, D., Cajamarca, A. O., & Beltrán-Flandoli, A. M. (2022). Experiencia de TV educativa en Ecuador en el marco de la pandemia. Edição/Edition, 265.
  • Robeva, R., Comar, T. D., & Eaton, C. D. (2022). Can We Bridge the Gap? Mathematics and the Life Sciences, Part 1–Calculus-Based Modules, Programs, Curricula. PRIMUS, 32(2), 117-123.
  • Robins, A., Burrows, A., & Borowczak, M. (2022, August). On the Development of Cybersecurity and Computing Centric Professional Developments and the Subsequent Implementation of Topics in K12 Lesson Plans (RTP). In 2022 ASEE Annual Conference & Exposition.
  • Robles Cuesta, T. M. (2022). Modelización basada en el individuo utilizando NetLogo de un reactor biológico de membrana (MBR) aerobio (Bachelor's thesis, Quito: UCE).
  • Röchert, D., Cargnino, M., & Neubaum, G. (2022). Two sides of the same leader: an agent-based model to analyze the effect of ambivalent opinion leaders in social networks. Journal of Computational Social Science, 1-47.
  • Roci, M., Salehi, N., Amir, S., Shoaib-ul-Hasan, S., Asif, F. M., Mihelič, A., & Rashid, A. (2022). Towards Circular Manufacturing Systems implementation: A Complex Adaptive Systems perspective using modelling and simulation as a quantitative analysis tool. Sustainable Production and Consumption.
  • Rodriguez-Ulloa, R. (2022). Cybernetic governance of the Peruvian State: a proposal. AI & SOCIETY, 1-23.
  • Rojas-Domínguez, A., Arroyo-Duarte, R., Rincón-Vieyra, F., & Alvarado-Mentado, M. (2022). Modeling cancer immunoediting in tumor microenvironment with system characterization through the ising-model Hamiltonian. BMC bioinformatics, 23(1), 1-25.
  • Romanowska, I., Carrignon, S., Coto-Sarmiento, M., Montanier, J. M., & Rubio-Campillo, X. (2022). From Counting Pots to Reconstructing Economy: Computational Tools Developed in the EPNet Project. Arqueología y Téchne: Métodos formales, nuevos enfoques: Archaeology and Techne: Formal methods, new approaches, 27.
  • Romero Romero, M. H. (2022). Modelización del ciclo de vida de la mosca soldado negro (Hermetia illucens) desarrollándose sobre desechos orgánicos (Bachelor's thesis, Quito: UCE).
  • Rubio, M. Á. G., Millán, N. D. C. O., Soto, M. D. C. S., & Parra, J. M. F. (2022). La simulación computacional como propuesta para el apoyo en la toma de decisiones contra la deserción escolar en Tijuana Baja California. Revista Ibérica de Sistemas e Tecnologias de Informação, (E47), 277-287.
  • Rukomojnikov, K. P., Sergeeva, T. V., Gilyazova, T. A., & Komisar, V. P. (2022, May). Computer modeling to support management and organizational decisions in the use of a forest harvester. In Computer Applications for Management and Sustainable Development of Production and Industry (CMSD2021) (Vol. 12251, pp. 144-148). SPIE.
  • Ruiz-Ledezma, E. R., Acosta-Magallanes, F., & del Socorro Valero-Cázarez, M. (2022). Una Aproximación Interdisciplinar STEM con Recursos Tecnológicos para el Tratamiento de Conceptos de Física y Matemáticas. Cultura Científica y Tecnológica, 19(2), E13-E22.
  • Rup, C., Bajic, E., & Mekki, K. (2022, October). Snowball: An Asynchronous Probabilistic Protocol for Neighbour Discovery in Mobile BLE Network. In IEEE 8th World Forum on Internet of Things.
  • Saba, J., Langbeheim, E., Hel‐Or, H., & Levy, S. T. (2022). Identifying aspects of complex and technological systems in the mental models of students who constructed computational models of electric circuits. Journal of Research in Science Teaching.
  • Sadaf, M., Jabbar, A., & Zaman, A. (2022). An An Agent Based Model for Combining the Climatic, Physical and Behavioral Response to Logging, Salinity, and Farmers Earnings in Irrigated Agriculture of Pakistan. Pakistan Journal of Economic Studies (PJES), 5(1), 135-165.
  • Saeed, A. K. (2022). Agent-based simulations on Catalan interprovincial migrations.
  • Sagar, S., Mahmood, A., Sheng, Q. Z., Pabani, J. K., & Zhang, W. E. (2022). Understanding the Trustworthiness Management in the Social Internet of Things: A Survey. arXiv preprint arXiv:2202.03624.
  • Saha, B., Martínez-García, M., Bhattacharya, S. N., & Joshi, R. (2022). Overcoming Choice Inertia through Social Interaction—An Agent-Based Study of Mobile Subscription Decision. Games, 13(3), 47.
  • Salau, K. R., Baggio, J. A., Shanafelt, D. W., Janssen, M. A., Abbott, J. K., & Fenichel, E. P. (2022). Taking a moment to measure networks—an approach to species conservation. Landscape Ecology, 1-19.
  • Salawu, G. A. (2022). The impact of disruptive technology on the manufacturing process, and productivity, in an advanced manufacturing environment (Doctoral dissertation).
  • Saleem, K., Saleem, M., Zeeshan, R., Javed, A. R., Alazab, M., Gadekallu, T. R., & Suleman, A. (2022). Situation-aware BDI Reasoning to Detect Early Symptoms of Covid 19 using Smartwatch. IEEE Sensors Journal.
  • Salmon, P. M., Stanton, N. A., Walker, G. H., Hulme, A., Goode, N., Thompson, J., & Read, G. J. (2022). Agent-Based Modelling (ABM). In Handbook of Systems Thinking Methods (pp. 253-269). CRC Press.
  • Sànchez-Marrè, M. (2022). Tools for IDSS Development. In Intelligent Decision Support Systems (pp. 533-582). Springer, Cham.
  • Santos, M. V., Mota, I., & Campos, P. (2022). Analysis of online position auctions for search engine marketing. Journal of Marketing Analytics, 1-17.
  • Sanz, V., & Urquia, A. (2022). Combining PDEVS and Modelica for describing agent-based models. SIMULATION, 00375497221094873.
  • Sarin, A. (2022). The Kolam Drawing: A Point Lattice System. Design Issues, 38(3), 34-54.
  • Sarnatskyi, V., & Baklan, I. (2023). CTrace: Language for Definition of Epidemiological Models with Contact-Tracing Transmission. In International Scientific Conference “Intellectual Systems of Decision Making and Problem of Computational Intelligence” (pp. 426-448). Springer, Cham.
  • Scharf, A., Mitteldorf, J., Armstead, B., Schneider, D., Jin, H., Kocsisova, Z., ... & Kornfeld, K. (2022). A laboratory and simulation platform to integrate individual life history traits and population dynamics. Nature Computational Science, 2(2), 90-101.
  • Schimpf, C., & Castellani, B. (2022). Approachable modeling and smart methods: a new methods field of study. International Journal of Social Research Methodology, 1-15.
  • Schmitt, J. (2022). Von komplexen Systemen und theoretischen Riesen. In Mechanismen der Polarisierung von Parteiensystemen (pp. 115-301). Springer VS, Wiesbaden.
  • Schneider, A. (2022). Meinungsdynamik und-manipulation durch Social Bots: Eine Untersuchung sozialer Online-Netzwerke auf Basis eines agentenbasierten Modells (Vol. 28). Tectum Wissenschaftsverlag.
  • Schooltink, F. (2022). A simulation of the ecological impact of three smartphone strategies (Bachelor's thesis, University of Twente).
  • Schroeder, S. A., Vendome, C., Giabbanelli, P. J., & Montfort, A. M. (2022, December). Towards reusable building blocks to develop COVID-19 simulation models. In 2022 Winter Simulation Conference (WSC) (pp. 569-580). IEEE.
  • Schutte, S., & Kelling, C. (2022). A Monte Carlo analysis of false inference in spatial conflict event studies. PloS one, 17(4), e0266010.
  • Seid, E. A., & Jin, K. Y. (2022). Agent-Based Modeling for Market Penetration of Electric Vehicles. 대한산업공학회 춘계공동학술대회 논문집, 766-779.
  • Sells, S. N., Mitchell, M. S., Ausband, D. E., Luis, A. D., Emlen, D. J., Podruzny, K. M., & Gude, J. A. (2022). Economical defence of resources structures territorial space use in a cooperative carnivore. Proceedings of the Royal Society B, 289(1966), 20212512.
  • Semboloni, F. (2022). From complex dynamics to the architecture of the city. In Lake Como School of Advanced Studies Complexity and Emergence: Ideas, Methods, with special attention to Economics and Finance (pp. 137-162). Springer, Cham.
  • Şendurur, P., & Sendurur, E. (2022). Students as Gamers: Design, Code, and Play. In Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning (pp. 868-887). IGI Global.
  • Shaaban, M., Scheffran, J., Elsobki, M. S., & Azadi, H. (2022). A Comprehensive Evaluation of Electricity Planning Models in Egypt: Optimization versus Agent-Based Approaches. Sustainability, 14(3), 1563.
  • Shane, R. (2022, September). Revisiting Linus’ Law in OpenStreetMap: An Agent-Based Approach. In Social, Cultural, and Behavioral Modeling: 15th International Conference, SBP-BRiMS 2022, Pittsburgh, PA, USA, September 20–23, 2022, Proceedings (Vol. 13558, p. 123). Springer Nature.
  • Shapiro, B., & Crooks, A. (2022). Drone strikes and radicalization: an exploration utilizing agent-based modeling and data applied to Pakistan. Computational and Mathematical Organization Theory, 1-19.
  • Sharma, D., Chaturvedi, S., Chaudhary, V., Kaul, A., & Mishra, A. K. (2022). Emerging Scope of Computer-Aided Drug Design in Targeting ROS in Cancer Therapy.
  • Shen, Y., Yang, F., Gao, M., & Dong, W. (2022). Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model. arXiv preprint arXiv:2205.02332.
  • Shiang, C. W., & Hussain, N. (2022). Modelling of Crowd Evacuation with Communication Strategy using Social Force Model. Journal of Optimization in Industrial Engineering, 15(1), 233-241.
  • Shin, H. (2022). Quantifying the Health Effects of Exposure to Non-Exhaust Road Emissions using Agent-based Modelling (ABM). MethodsX, 101673.
  • Shin, H., & Bithell, M. (2022). Exposure to Non-exhaust Emission in Central Seoul Using an Agent-based Framework. In Advances in Social Simulation (pp. 343-354). Springer, Cham.
  • Shin, H. C., Vallury, S., Janssen, M. A., & Yu, D. J. (2022). Joint effects of voluntary participation and group selection on the evolution of altruistic punishment. PloS one, 17(5), e0268019.
  • Shin, J., Dobson, G. B., Carley, K. M., & Carley, L. R. (2022). OSIRIS: Organization Simulation in Response to Intrusion Strategies. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 134-143). Springer, Cham.
  • Shu, Q., Rötzer, T., Detter, A., & Ludwig, F. (2022). Tree Information Modeling: A Data Exchange Platform for Tree Design and Management. Forests 2022, 13, 1955.
  • Shukla, M. K., Singh, L., Vidya, S., Quasim, H., & Bhandari, R. (2022). Pollination System for Greenhouse Flowering Plants Using Nano Drones. In Advances in Mechanical Engineering and Technology (pp. 157-162). Springer, Singapore.
  • Silva, A. J. P. D. Visualização em química e temporalidade: um estudo de caso sobre práticas e percepções na formação inicial de professores (Doctoral dissertation, Universidade de São Paulo).
  • Silva Junior, W. F. D. Cultura de células 3D in silico por meio de técnicas de Modelagem Baseada em Agentes: aplicações em engenharia de tecidos (Doctoral dissertation, Universidade de São Paulo).
  • Silva, V., Calderon, A., Todorovic, L., & Martins, L. (2022). Incorporating Future Earthquake Risk in Disaster Risk Management. In European Conference on Earthquake Engineering and Seismology (pp. 179-196). Springer, Cham.
  • Sirizzotti, M. (2022). Agent-based Modelling and Big Data: Applications for Maritime Traffic Analysis.
  • Sissa, G., & Franza, G. (2022, June). From micro behaviors to macro effects–Agent Based Modeling of environmental awareness spread and its effects on critical resource consumption. In 2022 International Conference on ICT for Sustainability (ICT4S) (pp. 99-108). IEEE.
  • Sivakumar, N., Warner, H. V., Peirce, S. M., & Lazzara, M. J. (2022). A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion. PLOS Computational Biology, 18(11), e1010701.
  • SOCIAL, W. (2022). Construction of Dialogue among Couples in Yazd: Moving towards a Grounded Theory. SOCIAL WELFARE, 22(84).
  • Sokolov, O., Mrela, A., Bieniek-Majka, M., Osinska, V., & Duch, W. (2022, September). Model of Trust Dissemination of Products Based on Fuzzy Aggregation Norms. In 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS) (pp. 741-744). IEEE.
  • Song, C., Shao, Q., Zhu, P., Dong, M., & Yu, W. (2022). An emergency aircraft evacuation simulation considering passenger overtaking and luggage retrieval. Reliability Engineering & System Safety, 108851.
  • Sørensen, M. L. S. K., Fog, B. V., Musaeus, L. H., & Petersen, M. G. (2022, October). KnitxCode: Exploring a Craftsmanship-driven Approach to Computational Thinking. In Adjunct Proceedings of the 2022 Nordic Human-Computer Interaction Conference (pp. 1-5).
  • Sotnik, G., Shannon, T., & Wakeland, W. (2022). A new agent-based model offers insight into population-wide adoption of prosocial common-pool behavior. The Journal of Mathematical Sociology, 1-28.
  • Sprinz, J. (2022). " Y'all are just too sensitive": A computational ethics approach to understanding how prejudice against marginalized communities becomes epistemic belief. arXiv preprint arXiv:2207.01017.
  • Srinivasan, A., & Namilae, S. Infection Risk Mitigation Using Pedestrian Dynamics. In Architectural Factors for Infection and Disease Control (pp. 93-108). Routledge.
  • Stanojevic, A., Cherubini, G., Woźniak, S., & Eleftheriou, E. (2022). Time-encoded multiplication-free spiking neural networks: application to data classification tasks. Neural Computing and Applications, 1-17.
  • Starr, J., Kain, M., & Bhatia, S. An Agent-Based Model for Localized COVID-19 Transmission Dynamics and Intervention Impacts.
  • Steffens, B., Corlay, Q., Suurmeyer, N., Noglows, J., Arnold, D., & Demyanov, V. (2022). Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects. Energies, 15(3), 902.
  • Stern, J. L., Valencia-Romero, A., & Grogan, P. T. (2022). Strategic robustness in bi-level system-of-systems design. Design Science, 8.
  • Student, J. (2022). Agent-Based Modelling. In Applied Data Science in Tourism (pp. 481-511). Springer, Cham.
  • Su, P., Chen, M., & Wang, Y. (2022). Agent-based model: A method worthy of promotion in Library and Information Science. Journal of Information Science, 01655515211061867.
  • Sulis, E., & Taveter, K. (2022). Multi-Agent Systems and Business Process Management. In Agent-Based Business Process Simulation: A Primer with Applications and Examples (pp. 131-140). Cham: Springer International Publishing.
  • Sulis, E., & Taveter, K. (2022). Agent-Based Business Process Simulation: A Primer with Applications and Examples. Springer Nature.
  • Sullivan, F. R., Duan, L., & Pektas, E. (2022, June). Design of an Evaluative Rubric for CT Integrated Curriculum in the Elementary Grades. In CTE-STEM 2022 conference.
  • Sun, L., & Fu, Z. (2022). Research on Universities’ Control of Online Discourse Power in the Period of COVID-19: A Case Study of Shanghai Universities. In INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND APPLICATIONS (pp. 95-103). Springer, Singapore.
  • Sung, R., & Park, J. (2022). Economic Sanctions and Consumer Behavior in Target States: An Agent-Based Model of Boycott Movements. In Conference of the Computational Social Science Society of the Americas (pp. 43-55). Springer, Cham.
  • Sunxin, W. A. N. G., Yanming, W. A. N. G., Jie, K. O. N. G., & Gaopan, S. H. E. N. (2022). A flexible scaling self-healing method for morphology of swarm robots. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 40(1), 206-214.
  • \
  • Sun, M. C., Sakai, K., Chen, A. Y., & Hsu, Y. T. (2022). Location problems of vertical evacuaiton structures for dam-failure floods: Considering shelter-in-place and horizontal evacuation. International Journal of Disaster Risk Reduction, 103044.
  • Swanson, H., Sherin, B., & Wilensky, U. (2022). Tuning Perceptions and Inferences to See a Graph in a New Way. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Sweety, S. A., Khan, M., Haque, A., & Salehin, M. (2022). An Agent Based Model of Mangrove Social-Ecological System for Livelihood Security Assessment. In Water Management: A View from Multidisciplinary Perspectives (pp. 319-349). Springer, Cham.
  • Syed, M., Cagely, M., Dogra, P., Hollmer, L., Butner, J. D., Cristini, V., & Koay, E. J. (2022). Immune‐checkpoint inhibitor therapy response evaluation using oncophysics‐based mathematical models. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, e1855.
  • Symons, S. L., Harvey, C. T., & Eyles, C. H. (2022). Innovating, integrating, and influencing: A science program for the 21st century. Where learning deeply matters: Reflections on the past, present, and future of teaching at McMaster University, 1(1).
  • Szangolies, L., Rohwäder, M. S., & Jeltsch, F. (2022). Single Large AND Several Small habitat patches: A community perspective on their importance for biodiversity. Basic and Applied Ecology.
  • Szczepanska, T., Angourakis, A., Graham, S., & Borit, M. (2022, April). Quantum Leaper: A Methodology Journey From a Model in NetLogo to a Game in Unity. In Advances in Social Simulation: Proceedings of the 16th Social Simulation Conference, 20–24 September 2021 (p. 191). Springer Nature.
  • Tan, W. C., & Sidhu, M. S. (2022). Review of RFID and IoT integration in supply chain management. Operations Research Perspectives, 100229.
  • Tan, X. J., Cheor, W. L., Yeo, K. S., & Leow, W. Z. (2022). Expert systems in oil palm precision agriculture: A decade systematic review. Journal of King Saud University-Computer and Information Sciences.
  • Taraktaş, B. (2022). Incorporating Computational Social Science in Political Science. In Opportunities and Challenges for Computational Social Science Methods (pp. 23-44). IGI Global.
  • Taranum, F., Sridevi, K., Hijab, M., Sayeedunissa, S. F., Kaleem, A., & Niraja, K. S. (2022). Role of an Optimal Multiagent Scheduling in Different Applications Using ML. In Multi-Agent Technologies and Machine Learning. IntechOpen.
  • Taylor, R., Forrester, J., Pedoth, L., & Zeitlyn, D. (2022). Structured output methods and environmental issues: perspectives on co-created bottom-up and ‘sideways’ science. Humanities and Social Sciences Communications, 9(1), 1-11.
  • Tedeschi, L. O. (2022). ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: The progression of data analytics and artificial intelligence in support of sustainable development in animal science. Journal of Animal Science.
  • Terán, O., Leger, P., & López, M. (2022). Modeling and simulating Chinese cross-border e-commerce: an agent-based simulation approach. Journal of Simulation, 1-18.
  • Termos, A., & Yorke-Smith, N. (2022). Urbanism and Geographic Crises: A Micro-Simulation Lens on Beirut. Urban Planning, 7(1), 87-100.
  • Thakur, C., & Gupta, S. (2022). Multi-Agent System Applications in Health Care: A Survey. Multi Agent Systems: Technologies and Applications towards Human-Centered, 139.
  • Then, A., Ewald, J., Söllner, N., Cooper, R. E., Küsel, K., Ibrahim, B., & Schuster, S. (2022). Agent-based modelling of iron cycling bacteria provides a framework for testing alternative environmental conditions and modes of action. Royal Society Open Science, 9(5), 211553.
  • Thomas, S. R., & Trinh, M. P. (2022, July). Effects of Transformational Leadership on Employability and Employee Retention. In Academy of Management Proceedings. Academy of Management Briarcliff Manor, NY 10510.
  • Thompson, J., & Cruz-Gambardella, C. (2022). Development of a Computational Policy Model for Comparing the Effect of Compensation Scheme Policies on Recovery After Workplace Injury. Journal of Occupational Rehabilitation, 1-11.
  • Torrens, P. M. (2022). Agent models of customer journeys on retail high streets. Journal of Economic Interaction and Coordination, 1-42.
  • Toth, C. A., Pauli, B. P., McClure, C. J., Francis, C. D., Newman, P., Barber, J. R., & Fristrup, K. (2022). A stochastic simulation model for assessing the masking effects of road noise for wildlife, outdoor recreation, and bioacoustic monitoring. Oecologia, 1-12.
  • Tracy, M., Gordis, E., Strully, K., Marshall, B. D., & Cerdá, M. (2022). Applications of agent-based modeling in trauma research. Psychological Trauma: Theory, Research, Practice, and Policy.
  • Trejo, J. A. O., Razon, T. G. N., de los Angeles Cobian, C., Ojeda, A. L., & Aguayo, M. G. B. (2022). Fase de regreso a clases en ciudades inteligentes, simulación de modelo epidemiológico SIR en estudio de caso de la universidad de Guadalajara, en CUCEA. Ciencia Latina Revista Científica Multidisciplinar, 6(5), 1586-1602.
  • Trentesaux, D., & Karnouskos, S. (2022). Engineering ethical behaviors in autonomous industrial cyber-physical human systems. Cognition, Technology & Work, 24(1), 113-126.
  • Trivedi, A., & Pandey, M. (2022). Testing and evaluation of crowd management strategies at religious gatherings in India using agent-based modelling and simulation. International Journal of Advanced Intelligence Paradigms, 22(3-4), 379-415.
  • Truong, V. T., Baverel, P. G., Lythe, G. D., Vicini, P., Yates, J. W., & Dubois, V. F. (2022). Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models. CPT: pharmacometrics & systems pharmacology, 11(2), 133-148.
  • Turchaninov, I., van Dam, K. H., Bustos-Turu, G., & Acha, S. Transport electrification and fast-charging expansion: A case study in Alaska. In International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) (p. 55).
  • Twardawa, M. Zjawiska emergentne. Biologia, 1, 26.
  • Tzouras, P. G., Mitropoulos, L., Stavropoulou, E., Antoniou, E., Koliou, K., Karolemeas, C., ... & Kepaptsoglou, K. (2022). Agent-based models for simulating e-scooter sharing services: A review and a qualitative assessment. International Journal of Transportation Science and Technology.
  • Uchmański, J., Niewolski, M., & Janiszewski, J. (2022). Interspecific competition in perennial sedentary organisms: An individual‐based model. Population Ecology.
  • Uddin, M. N., Chi, H. L., Wei, H. H., Lee, M., & Ni, M. (2022). Influence of interior layouts on occupant energy-saving behaviour in buildings: An integrated approach using Agent-Based Modelling, System Dynamics and Building Information Modelling. Renewable and Sustainable Energy Reviews, 161, 112382.
  • Ullah, K. M., & Dwivedi, P. Ascertaining Land Allocation Decisions of Farmers about the Adoption of Carinata as a Potential Crop for Sustainable Aviation Fuel Production in the Southern United States. GCB Bioenergy.
  • Umlauft, M., Schranz, M., & Elmenreich, W. SwarmFabSim: A Simulation Framework for Bottom-up Optimization in Flexible Job-Shop Scheduling Using NetLogo.
  • Umlauft, M., Schranz, M., & Elmenreich, W. (2022, July). Simulation of Swarm Intelligence for Flexible Job-Shop Scheduling with SwarmFabSim: Case Studies with Artificial Hormones and an Ant Algorithm. In International Conference on Simulation and Modeling Methodologies, Technologies and Applications (pp. 133-155). Cham: Springer International Publishing.
  • Utrero, T. H. (2022). ¿ Influyen el número de orígenes y los umbrales de confianza en las creencias en la dinámica de difusión de rumores? Una propuesta teórica desde un modelo basado en agentes. Papers. Revista de Sociologia, 107(2), e2994-e2994.
  • Vakil, S., Reith, A., & Melo, N. A. (2022). Jamming power: Youth agency and community‐driven science in a critical technology learning program. Journal of Research in Science Teaching.
  • Valdez, A. C. Towards an Understanding of Opinion Formation on the Internet. group, 4, 8.
  • Vallés, A. D. (2022). El Efecto ee Impuestos y Subsidios en la Difusión de Fotovoltaicos en Puerto Rico: Análisis con un Modelo de Agentes (Doctoral dissertation, University of Puerto Rico, Rio Piedras (Puerto Rico)).
  • van der Borgh, M., Schäfers, T., Lindgreen, A., & Di Benedetto, C. A. (2022). Moving the needle: Publishing academic-practitioner research in Industrial Marketing Management. Industrial Marketing Management.
  • van der Zwet, K., Barros, A. I., van Engers, T. M., & Sloot, P. M. (2022). Promises and pitfalls of computational modelling for insurgency conflicts. The Journal of Defense Modeling and Simulation, 15485129211073612.
  • Van Dyke Parunak, H. (2022). Learning Actor Preferences by Evolution. In Conference of the Computational Social Science Society of the Americas (pp. 85-97). Springer, Cham.
  • van Haeringen, E., Liistro, G., & Gerritsen, C. (2022). An Agent-Based Model of Emotion Contagion and Group Identification: A Case Study in the Field of Football Supporters. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 410-422). Springer, Cham.
  • van Klingeren, F. (2022). Using player types to understand cooperative behaviour under economic and sociocultural heterogeneity in common-pool resources: Evidence from lab experiments and agent-based models. PloS one, 17(5), e0268616.
  • van Roekel, G., & Smit, M. (2022). Herd behaviour and the emergence of clusters. Spatial Economic Analysis, 1-21.
  • Vander Linden, M. (2022). Iza Romanowska, Colin D. Wren and Stefani A. Crabtree. Agent-Based Modelling for Archaeology: Simulating the Complexity of Society (Santa Fe: The Santa Fe Institute Press. 2021. xiii and 429 pp., numerous illustr., pbk, ISBN 978-1-947864-25-2). European Journal of Archaeology, 25(4), 547-549.
  • Vargas Guarnizo, M. P., & Bohórquez Arévalo, L. E. (2022). Design of a simulation model that represents the collective intelligence genome of (malone et al., 2010). Tecnura, 26(72), 59-77.
  • Vargas Guarnizo, M. P., & Bohórquez Arévalo, L. E. (2022). Diseño de modelo de simulación que representa el genoma de inteligencia colectiva de Malone, Laubacher y Dellarocas. Tecnura, 26(72), 59-77.
  • Vega-Sánchez, R., & Herrera, J. M. (2022). Agent-based modelling for the study of shipwreck site formation processes: A theoretical framework and conceptual model. F1000Research, 11(1525), 1525.
  • Veldboer, T. (2022). A pure coordination game for the multi-agent card game The Mind (Doctoral dissertation).
  • Verhagen, P. (2022). Modelling the Basics of Roman Demography. Simulating Roman Economies: Theories, Methods, and Computational Models, 271.
  • Verleger, M., Stansbury, R., Akbas, M., & Craiger, P. (2022, August). An Undergraduate Research Experience in Unmanned Aircraft Systems (UAS) Cybersecurity–Outcomes and Lessons Learned. In 2022 ASEE Annual Conference & Exposition.
  • Vermeer, W., Gurkan, C., Hjorth, A., Benbow, N., Mustanski, B. M., Kern, D., Brown, C. H., & Wilensky, U. (2022). Agent-based model projections for reducing HIV infection among MSM: Prevention and care pathways to end the HIV epidemic in Chicago, Illinois. PloS one, 17(10), e0274288. https://doi.org/10.1371/journal.pone.0274288
  • Vermeer, W.H., Smith, J.D., Wilensky, U. et al. High-Fidelity Agent-Based Modeling to Support Prevention Decision-Making: an Open Science Approach. Prev Sci 23, 832–843 (2022). https://doi.org/10.1007/s11121-021-01319-3
  • Vidanaarachchi, R., Thompson, J., Godic, B., & McClure, R. AgentsX. jl—An Extended Julia Framework for Exploring Urban and Social Systems. In International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) (p. 16).
  • Vieira, L. S., & Laubenbacher, R. C. (2022). Computational models in systems biology: standards, dissemination, and best practices. Current Opinion in Biotechnology, 75, 102702.
  • Vinh, P. C. (2022). Context-awareness and Nature of Computation and Communication. Mobile Networks and Applications, 1-3.
  • Volpe, R., Catrini, P., Piacentino, A., & Fichera, A. (2022). An agent-based model to support the preliminary design and operation of heating and power grids with cogeneration units and photovoltaic panels in densely populated areas. Energy, 261, 125317.
  • Vriens, E., & Buskens, V. (2022). Sharing Risk under Heterogeneity: Participation in Settings of Incomplete Information. Journal of Artificial Societies & Social Simulation, 25(2).
  • Wagh, A., Fuhrmann, T., Eloy, A. A. D. S., Wolf, J., Bumbacher, E., Blikstein, P., & Wilkerson, M. (2022, June). MoDa: Designing a Tool to Interweave Computational Modeling with Real-world Data Analysis for Science Learning in Middle School. In Interaction Design and Children (pp. 206-211).
  • Wahyudiono, S., Darmawan, A. A., & Burhan, M. S. (2022). Pemodelan Shift Kerja dalam Proyek Konstruksi menggunakan NetLogo dalam meminimalkan Penyebaran Covid-19. Jurnal Ilmiah Universitas Batanghari Jambi, 22(2), 1256-1263.
  • Wan, S., Chen, Z., Lyu, C., Li, R., Yue, Y., & Liu, Y. (2022). Research on disaster information dissemination based on social sensor networks. International Journal of Distributed Sensor Networks, 18(3), 15501329221080666.
  • Wan, S., & Liu, Y. (2022). A security detection approach based on autonomy-oriented user sensor in social recommendation network. International Journal of Distributed Sensor Networks, 18(3), 15501329221082415.
  • Wang, H., Qiu, L., Chen, Z., Li, F., Jiang, P., Zhang, A., & Nie, X. (2022). Is rationality or herd more conducive to promoting farmers to protect wetlands? A hybrid interactive simulation. Habitat International, 128, 102647.
  • Wang, J., & Kim, Y. J. (2022). Evolutionary Characteristics of Microstructural Hydration and Chloride Diffusion in UHPC. Materials & Design, 111528.
  • Wang, K. (2022). Feasibility analysis and Research on Intelligent trusted modeling of network architecture software. In Journal of Physics: Conference Series (Vol. 2173, No. 1, p. 012027). IOP Publishing.
  • Wang, K. D., Cock, J. M., Käser, T., & Bumbacher, E. (2022). A systematic review of empirical studies using log data from open‐ended learning environments to measure science and engineering practices. British Journal of Educational Technology.
  • Wang, L. C., Yang, M., Li, Y., & Hou, Y. Q. (2022). A model of lane-changing intention induced by deceleration frequency in an automatic driving environment. Physica A: Statistical Mechanics and its Applications, 127905.
  • Wang, M., & Peng, S. (2022, October). COVID-19 Visualization Platform Based on Population Density Propagation Model. In 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA) (pp. 406-411). IEEE.
  • Wang, R., Ye, Z., Lu, M., & Hsu, S. C. (2022). Understanding post-pandemic work-from-home behaviours and community level energy reduction via agent-based modelling. Applied Energy, 322, 119433.
  • Wang, Y. (2022, May). Robust Information Center Implementation of College English Training under the Network Computer Aided System Environment Considering QoS. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 676-680). IEEE.
  • Wang, Y., Yao, X., Song, L., & Zhang, S. (2022). The Behaviour and Influence Mechanism of the Subject of IUR Collaborative Innovation: A Simulation Study Based on NetLogo. Discrete Dynamics in Nature and Society, 2022.
  • Wang, Z., & Jia, G. (2022). Sensitivity Analysis of Tsunami Evacuation Risk with Respect to Epistemic Uncertainty. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8(3), 04022037.
  • Wang, Z., & Jia, G. (2022). Simulation-based and risk-informed assessment of the effectiveness of tsunami evacuation routes using agent-based modeling: a case study of seaside, Oregon. International Journal of Disaster Risk Science, 13(1), 66-86.
  • Watson, J. W., Boyd, R., Dutta, R., Vasdekis, G., Walker, N. D., Roy, S., ... & Sibly, R. M. (2022). Incorporating environmental variability in a spatially-explicit individual-based model of European sea bass. Ecological Modelling, 466, 109878.
  • Watts, K. (2022). Using in vitro, in silico, and in-Classroom Techniques to Address the Gender Data Gap in Health Care.
  • Wei, C. H. E. N., En-hua, H. U., Hong-mei, S. H. A. N., & Long, Z. H. A. N. G. (2022). Study on the Influence of Union-Enterprise Relation Modes on the Performance of Enterprises of Different Scales: A Multi-agent-based Simulation Model. Operations Research and Management Science, 31(1), 232.
  • Wei, L., Yang, Y., Wu, J., Long, C., & Lin, Y. B. (2022). A Bidirectional Trust Model for Service Delegation in Social Internet of Things. Future Internet, 14(5), 135.
  • Wei, Y., Jang, N., Zhang, Z., Zeng, M., & Yang, Z. Research on combat simulation agent modelling methods combined with reinforcement learning. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-12.
  • Weisberg, M. (2022).Understanding the Emergence of Population Behavior in Individual-Based Models. Philosophy of Science.
  • Weisburd, D., Wolfowicz, M., Hasisi, B., Paolucci, M., & Andrighetto, G. (2022). What is the best approach for preventing recruitment to terrorism? Findings from ABM experiments in social and situational prevention. Criminology & Public Policy, 21(2), 461-485.
  • Wei-qiang, O. U., & Bin, Z. H. U. (2022). Research on Dynamic Allocation Mechanism of Mainstream and Newstream Innovation Resources Based on ABMS. Operations Research and Management Science, 31(6), 182.
  • Werntz, S., & Oppenheimer, D. (2022). In the Dark: Agent-Based Modeling of Uninformed Individuals within Polarized Groups. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 44, No. 44).
  • Weyer, J., Philipp, M., & Adelt, F. Agent-Based Modelling of Infrastructure Systems. Metropolitan Research, 155.
  • Whitenack, L., & Mahabir, R. (2022, April). A tool for optimizing the efficiency of drive-thru services. In 2022 Systems and Information Engineering Design Symposium (SIEDS) (pp. 151-156). IEEE.
  • Whitney, E. (2022). Uses of Agent-Based Modeling and Simulation to Support Business Decision-Making for Healthcare Industry Leadership in the United States of America (Doctoral dissertation, Northcentral University).
  • Widiyanto, S., Adi, D., & Soans, R. V. (2022). Agent-Based Simulation Disaster Evacuation Awareness on Night Situation in Aceh. IPTEK The Journal of Engineering, 8(1), 36-43.
  • Wijermans, N., Schill, C., Lindahl, T., & Schlüter, M. (2022). Combining approaches: Looking behind the scenes of integrating multiple types of evidence from controlled behavioural experiments through agent-based modelling. International Journal of Social Research Methodology, 1-13.
  • Wilson, A. (2022). Positioning Computational Modelling in Roman Studies. Simulating Roman Economies: Theories, Methods, and Computational Models, 308.
  • Wing, A. K. (2022). Building a Framework for Apron Planning, Design, Optimization, Future Proofing and Expansion (Doctoral dissertation, University of South Alabama).
  • Winzar, H., Baumann, C., Soboleva, A., Park, S. H., & Pitt, D. (2022). Competitive Productivity (CP) as an emergent phenomenon: Methods for modelling micro, meso, and macro levels. International Journal of Hospitality Management, 105, 103252.
  • Witeck, G. R., Rocha, A. M. A., Silva, G. O., Silva, A., Durães, D., & Machado, J. (2022). A Bibliometric Review and Analysis of Traffic Lights Optimization. In International Conference on Computational Science and Its Applications (pp. 43-54). Springer, Cham.
  • Wojcieszak, M., Sobkowicz, P., Yu, X., & Bulat, B. (2022). What Information Drives Political Polarization? Comparing the Effects of In-group Praise, Out-group Derogation, and Evidence-based Communications on Polarization. The International Journal of Press/Politics, 27(2), 325-352.
  • Wood, N. Grassroots Citizen Participation with Government Agencies in Disaster Response Activities. The Cupola, 342.
  • Wozniak, M., & Dziecielski, M. (2022). Should I Turn or Should I Go? Simulation of Pedestrian Behaviour in an Urban Environment. Journal of Simulation, 1-15.
  • Wright, I. D., Reimherr, M., & Liechty, J. A Machine Learning Approach to Classification for Traders in Financial Markets. Stat, e465.
  • Wu, R., Han, Y., Guo, K., & Liu, K. (2022, July). Information dissemination model of recommendation platform based on network community. In 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC) (pp. 633-641). IEEE.
  • Wu, S., Lei, Y., & Jin, W. (2022). An Interdisciplinary Approach to Quantify the Human Disaster Risk Perception and Its Influence on the Population at Risk: A Case Study of Longchi Town, China. International Journal of Environmental Research and Public Health, 19(24), 16393.
  • Wu, S., Swanson, H., Sherin, B., & Wilensky, U. (2022). Students’ Prior Knowledge of Disease Spread and Prevention. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Wu, S., Peel, A., Zhao, L., Horn, M., & Wilensky, U. (2022). A professional development that helps teachers integrate computational thinking into their STEM classrooms. Innovations in Science Teacher Education.
  • Wu, S., Swanson, H., Sherin, B., & Wilensky, U. (2022). Investigating Student Learning about Disease Spread and Prevention in the Context of Agent-Based Computational Modeling. Proceedings of the International Conference for the Learning Sciences (ICLS 2022), Hiroshima, Japan: ISLS.
  • Wu, Z., & Forget, G. (2022). PlanktonIndividuals. jl: A GPU supported individual-based phytoplankton life cycle model. Journal of Open Source Software, 7(73), 4207.
  • Wu, Z., & Huang, S. (2022, December). The Impact of Heterogeneous Reputation Evaluation Laws on Cooperation Based on Net Logo. In 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) (pp. 123-133). Atlantis Press.
  • Wurzer, G., Reismann, M., Marschnigg, C., Dorfmeister, A., Tauböck, S., Ledermüller, K., & Spörk, J. (2022). PASSt-A: Agent-based student analytics aimed at improved feasibility and study success. IFAC-PapersOnLine, 55(20), 361-366.
  • Xi, J., Chan, W. (2022). Reopening Universities without Testing During COVID-19: Evaluating a Possible Alternative Strategy in Low Risk Countries. Archives of Clinical and Biomedical Research 6 (2022): 971-981.
  • Xia, M., Lu, Z., Xu, L., Shi, Y., Ma, Q., Wu, Y., & Sheng, B. (2022). Impact of Regional Differences in Risk Attitude on the Power Law at the Urban Scale. Land, 11(10), 1791.
  • Xiang, L., & Diamond, S. (2022). Developing and Using Computer Models to Understand Epidemics Breadcrumb. The Science Teacher, 89(3).
  • Xiao, J. (2022). A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware (Doctoral dissertation, Technische Universität München).
  • Xiaobei, X. U., & Hongping, Y. U. A. N. An Agent-based Modeling Approach for Investigating the Diffusion of BIM Technology. Industrial Engineering Journal, 24(6), 57.
  • Xie, J., Tian, S., Liu, J., Cao, R., Yue, P., Cai, X., ... & Zhang, D. K. (2022). Dual role of the nasal microbiota in neurological diseases—An unignorable risk factor or a potential therapy carrier. Pharmacological Research, 106189.
  • Xin, Z., Li, J., Wang, Z., & Li, J. (2023). A Hardware-in-the-Loop Simulation Platform for UAV Swarm Decision-Making. In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control (pp. 189-199). Springer, Singapore.
  • Xin-gang, Z., Yi, Z., Hui, W., & Zhen, W. (2022). How can the cost and effectiveness of renewable portfolio standards be coordinated? Incentive mechanism design from the coevolution perspective. Renewable and Sustainable Energy Reviews, 158, 112096.
  • Xiong, M., Wang, Y., & Cheng, Z. (2021, December). Research on Modeling and Simulation of Information Cocoon Based on Opinion Dynamics. In 2021 The 9th International Conference on Information Technology: IoT and Smart City (pp. 161-167).
  • Xu, H., Wang, Y., & Yan, Y. (2022). Study on the Optimum Process Conditions for Preparation of C4 Olefins by Ethanol Coupling. Academic Journal of Science and Technology, 3(1), 50-57.
  • Xu, L., Ding, R., & Wang, L. (2022). How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles. Scientometrics, 1-27.
  • Xu, Z., Zhang, H., & Huang, Z. (2022). A Continuous Markov-Chain Model for the Simulation of COVID-19 Epidemic Dynamics. Biology, 11(2), 190.
  • Xue, X., Yu, X. N., Zhou, D. Y., Wang, X., Zhou, Z. B., & Wang, F. Y. (2022). Computational Experiments: Past, Present and Future. arXiv preprint arXiv:2202.13690.
  • Yadav, A., Caeli, E. N., Ocak, C., & Macann, V. (2022, July). Teacher Education and Computational Thinking: Measuring Pre-service Teacher Conceptions and Attitudes. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1 (pp. 547-553).
  • Yadav, A., & Lachney, M. (2022). Teaching with, about, and through technology: Visions for the future of teacher education. Journal of Technology and Teacher Education, 30(2), 189-200.
  • Yang, H., Wu, X., Zhao, S., Madani, H., Chen, J., & Chen, Y. (2022). An Agent-based Model Study on Subsidy Fraud in Technological Transition. In ICAART (1) (pp. 353-358).
  • Yang, J. (2022). Xi, Wai Kin (Victor) Chan. Reopening Universities without Testing During COVID-19: Evaluating a Possible Alternative Strategy in Low Risk Countries. Archives of Clinical and Biomedical Research, 6, 971-981.
  • Yang, K., Yang, H., Zhang, J., & Kang, R. (2022). Effects on Taxiing Conflicts at Intersections by Pilots’ Sensitive Speed Adjustment. Aerospace, 9(6), 288.
  • Yang, L. (2022). Double-edged Effects of Pricing on Diffusion of Green Products. Journal of Cleaner Production, 132109.
  • Yang, L., Iwami, M., Chen, Y., Wu, M., & van Dam, K. H. (2022). Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review. Progress in Planning, 100657.
  • Yang, R., Wang, A., & Bai, Y. Evolutionary Mechanism of Social Interaction and Green Travel Behavior of Urban Residents: Based on Scale-Free Network. Available at SSRN 4094750.
  • Yang, Y., Yin, J., Wang, D., Liu, Y., Lu, Y., Zhang, W., & Xu, S. (2022). ABM-based emergency evacuation modelling during urban pluvial floods: A “7.20” pluvial flood event study in Zhengzhou, Henan Province. Science China Earth Sciences, 1-10.
  • Yang, Z., Jinling, L., Haixiang, G., & Weiming, C. (2022). Research on Emotional Contagion and Intervention Strategy of Indoor Evacuation Based on Risk Perception. Journal of System Simulation, 34(12), 2691.
  • Yao, Z., Wu, X., & Li, N. (2022, May). Simulation of indirect-reciprocity-based lane-changing in Internet of Vehicles environment. In 2nd International Conference on Internet of Things and Smart City (IoTSC 2022) (Vol. 12249, pp. 17-23). SPIE.
  • Yazan, D. M., van Capelleveen, G., & Fraccascia, L. (2022). Decision-Support Tools for Smart Transition to Circular Economy. In Smart Industry–Better Management (Vol. 28, pp. 151-169). Emerald Publishing Limited.
  • Ye, X., Du, J., Han, Y., Newman, G., Retchless, D., Zou, L., ... & Cai, Z. (2022). Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda. Journal of Planning Literature, 08854122221137861.
  • Yegenoglu, A., Subramoney, A., Hater, T., Jimenez-Romero, C., Klijn, W., Martin, A. P., ... & Diaz-Pier, S. (2022). Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn. arXiv preprint arXiv:2202.13822.
  • Yi-Chen, H., Tak-Yu, C., & Chie, B. T. (2022). The Effect of Dishonest Sellers on E-commerce: An Agent-Based Modeling Approach. Advances in Management and Applied Economics, 12(4).
  • Yin, D., & Gong, B. (2022). Auto-Adaptive Trust Measurement Model Based on Multidimensional Decision-Making Attributes for Internet of Vehicles. Wireless Communications and Mobile Computing, 2022.
  • Yin, S., Xu, Y., Xu, M., de Jong, M. C., Huisman, M. R., Contina, A., ... & de Boer, W. F. (2022). Habitat loss exacerbates pathogen spread: An Agent-based model of avian influenza infection in migratory waterfowl. PLoS computational biology, 18(8), e1009577.
  • Yu, S. (2022). Agent-based modelling using survey data to simulate occupancy patterns and occupant interactions for workplace design. Building and Environment, 224, 109519.
  • Yu, Y., Yazan, D. M., Junjan, V., & Iacob, M. E. (2022). Circular economy in the construction industry: A review of decision support tools based on Information & Communication Technologies. Journal of Cleaner Production, 131335.
  • Zadbood, A., & Hoffenson, S. (2022). Social Network Word-of-Mouth Integrated into Agent-Based Design for Market Systems Modeling. Journal of Mechanical Design, 1-17.
  • Zakaria, N. (2022). Action network: a probabilistic graphical model for social simulation. SIMULATION, 98(4), 335-346.
  • Zargar, S. H., Sadeghi, J., & Brown, N. C. (2022). Agent-based modelling for early-stage optimization of spatial structures. International Journal of Architectural Computing, 14780771221143493.
  • Zauner, G., & Weidinger, W. (2022). Modelling and. The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics, 347.
  • Zejin, T., & Pyung-hoi, K. O. O. (2022). A Study Of Supply Chain Risk Decisions With Consideration Of Risk Preference Based On Abm Simulation. 한국경영과학회 학술대회논문집, 4151-4156.
  • Zellner, M. L., Milz, D., Lyons, L., Hoch, C. J., & Radinsky, J. (2022). Finding the Balance Between Simplicity and Realism in Participatory Modeling for Environmental Planning. Environmental Modelling & Software, 157, 105481.
  • Zhang, B., Zhang, H., & Yang, X. (2023). Research on Social Atomization Risk and Governance Decision Making in Emerging Community. In International Conference on Decision Science & Management (pp. 119-126). Springer, Singapore.
  • Zhang, C., Wu, X., Zhao, S., Madani, H., Chen, J., & Chen, Y. (2022). Simulation study on the low carbon transition process in Japan’s electricity market. Green Technologies and Sustainability, 100006.
  • Zhang, G., Li, H., He, R., & Lu, P. (2022). Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions. Complex & Intelligent Systems, 8(2), 1369-1387.
  • Zhang, H., Xiong, H., Wang, G., & Jiang, P. (2022). How institutional pressures improve environmental management performance in construction projects: an agent-based simulation approach. Environment, Development and Sustainability, 1-31.
  • Zhang, J., & Robinson, D. T. (2022). Investigating path dependence and spatial characteristics for retail success using location allocation and agent-based approaches. Computers, Environment and Urban Systems, 94, 101798.
  • Zhang, Q., Wu, X., & Chen, Y. (2022). Is economic crisis an opportunity for realizing the low-carbon transition? A simulation study on the interaction between economic cycle and energy regulation policy. Energy Policy, 168, 113114.
  • Zhang, L., & Jiang, X. (2022, September). Comprehensive evaluation model based on data and analysis system. In International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022) (Vol. 12332, pp. 458-464). SPIE.
  • Zhang, W., Yuan, J., Zhang, H. N., & Li, B. Y. Simulation research on blockchain bidding model. In Proc. of SPIE Vol (Vol. 12160, pp. 121600Q-1).
  • Zhang, Y., Gao, J., Bilgihan, A., & Lorenz, M. (2022). A holistic assessment of eWOM management effectiveness with agent-based modeling. International Journal of Contemporary Hospitality Management, (ahead-of-print).
  • Zhang, Y., Xu, L., Gong, Z., Wang, Y., & Wang, Z. (2022). Study Blockchain-Based Supply Chain Finance System of the Construction Industrialization. In International Symposium on Advancement of Construction Management and Real Estate (pp. 804-816). Springer, Singapore.
  • Zhao, B., Lyu, X., & Qi, N. (2022). Construction and Optimization of Transboundary Business Financial Credit Network in the Era of 5G Communication. Wireless Communications and Mobile Computing, 2022.
  • Zhao, L., Peel, A., Horn, M.S., Wilensky, U. (2022). Student perceptions of computational thinking practices in a CT-integrated environmental science unit. Paper accepted to the Annual Meeting of the American Educational Research Association (AERA) 2022. San Diego, CA.
  • Zhao, L., Peel, A., Horn, M. S., & Wilensky, U. (2022). Student Perceptions of Computational Thinking Practices in a CT-integrated Environmental Science Unit. the National Association for Research in Science Teaching (NARST) 2022. Vancouver, British Columbia.
  • Zhao, N., Chong, H. Y., & Li, Q. (2022). Agent-based modelling of helping behaviour diffusion in project teams as an evolutionary process. Journal of Simulation, 1-18.
  • Zhao, N., Lei, C., Liu, H., & Wu, C. (2022). Improving the Effectiveness of Organisational Collaborative Innovation in Megaprojects: An Agent-Based Modelling Approach. Sustainability, 14(15), 9070.
  • Zhong, J., Li, D., Huang, Z., Lu, C., & Cai, W. (2022). Data-driven Crowd Modeling Techniques: A Survey. ACM Transactions on Modeling and Computer Simulation (TOMACS), 32(1), 1-33.
  • Zhong, Q., Hilbert, M., & Frey, S. (2022). Breaking the Structural Reinforcement: An Agent-Based Model on Cultural Consumption and Social Relations. Social Science Computer Review, 08944393211056501.
  • Zhou, J., Kofinas, G. P., Kielland, K., Boone, R. B., Prugh, L., & Tape, K. D. (2022). Climate change, moose, and subsistence harvest: social-ecological assessment of Nuiqsut, Alaska. Ecology and Society, 27(3), 29.
  • Zhou, Q. (2022, October). Effectiveness of Different Control Measures on the Spread of COVID-19. In Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences (pp. 220-226).
  • Zhuo, Q. (2022, October). Predicting the Future of Achieving Herd Immunity in New York City. In Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences (pp. 579-582).
  • Zhou, Y., Shi, S., & Wang, S. (2022). A Multi-Agent Model-Based Evolutionary Model of Port Service Value Network and Decision Preferences. Sustainability, 14(6), 3565.
  • Zhu, G., Xing, W., Popov, V., Li, Y., Xie, C., & Horwitz, P. (2022). Using Learning Analytics to Understand Students' Discourse and Behaviors in STEM Education. In Artificial Intelligence in STEM Education (pp. 225-240). CRC Press.
  • Živojinović, T., & Zornić, N. (2022). Anticipating the impact of sharing economy drivers on consumer intention for using a sharing economy service. Journal of East European Management Studies, 27(2), 233-258.
  • Zu, C., Zeng, H., Zhou, X. (2022).Computational Simulation of Team Creativity: the Benefit of Member Flow. Frontiers in Psychology.
  • Zuccotti, C. V., Lorenz, J., Paolillo, R., Rodríguez Sánchez, A., & Serka, S. (2022). Exploring the dynamics of neighbourhood ethnic segregation with agent-based modelling: an empirical application to Bradford, UK. Journal of Ethnic and Migration Studies, 1-22.
  • Zusai, D., Sawa, R., Cheung, M. W., Lahkar, R., & Wu, J. (2022). Tributes to Bill Sandholm. Journal of Dynamics and Games.
  • Zvereva, O., Ershova, I., Goldstein, S., Shangina, E., & Tebaikina, N. (2022, April). Agent-based model implementing for investigation of economic agents’ behavior influence on autonomous community viability. In AIP Conference Proceedings (Vol. 2425, No. 1, p. 110015). AIP Publishing LLC.
  • Сіницький, М. Є. ПРОГРАМНЕ ЗАБЕЗПЕЧЕННЯ ЕКОНОМІСТІВ У ЦИФРОВУ ЕПОХУ. Стратегія розвитку України: фінансово-економічний та гуманітарний аспекти: матеріали VIII Міжнародної науково-практичної конференції. Київ,«Інформаційно-аналітичне агентство», 2021. 309 c., 251.
  • Карчевський, М. В. (2022). ОБЧИСЛЮВАЛЬНЕ КРИМІНОЛОГІЧНЕ АРГУМЕНТУВАННЯ: ПОНЯТТЯ, МОЖЛИВОСТІ ТА ПЕРСПЕКТИВИ ВИКОРИСТАННЯ. Вісник Луганського державного університету внутрішніх справ імені ЕО Дідоренка, 4(100), 112-126.
  • ЕРШОВ, Н. М. (2022). РАЗРАБОТКА И ИССЛЕДОВАНИЕ РАСПРЕДЕЛЕННЫХ АЛГОРИТМОВ УПРАВЛЕНИЯ СИСТЕМАМИ РОЕВОГО ИНТЕЛЛЕКТА. COMPUTATIONAL NANOTECHNOLOGY Учредители: ООО" Издательский дом" Юр-ВАК", 9(2), 21-34.
  • Макаров, В. Л., Бахтизин, А. Р., Бекларян, Г. Л., Акопов, А. С., & Стрелковский, Н. В. (2022). МОДЕЛИРОВАНИЕ МИГРАЦИОННЫХ И ДЕМОГРАФИЧЕСКИХ ПРОЦЕССОВ С ИСПОЛЬЗОВАНИЕМ FLAME GPU. Бизнес-информатика, 16(1), 7-21.
  • Кабанов, А. А. (2022). МОДЕЛЬНО-ОРИЕНТИРОВАННАЯ РАЗРАБОТКА ПРОИЗВОДСТВ РАКЕТНО-КОСМИЧЕСКИХ СИСТЕМ В КОНТЕКСТЕ ИЗДЕЛИЙ В АЭРОКОСМИЧЕСКИХ ВУЗАХ. Космические аппараты и технологии, 6(3 (41)), 195-205.
  • 西田遼, 重中秀介, 加藤優作, & 大西正輝. (2022). 群集シミュレーションによる歩行空間設計と制御に関する研究動向. 人工知能学会論文誌, 37(2), J-LB1_1.
  • 王奇, 王刚桥, 陈永强, & 刘奕. 面向社会计算的集成建模方法与应用系统. 计算机科学, 49(4), 25-29.
  • 李春发, 曹颖颖, 王聪, & 郝琳娜. (2022). 平台规制下直播电商三方策略演化博弈与仿真. 复杂系统与复杂性科学, 19(1), 34-44.
  • 犬飼佳吾. (2022). マルチエージェントのための行動科学: 実験経済学からのアプローチ. 行動経済学, 15, 1-3.
  • 席周慧, 孟德霖, & 赵继军. (2022). 钻石公主号邮轮上 COVID-19 传播动态的研究. 复杂系统与复杂性科学, 19(1), 67-73.
  • 冯楠, 任彬彬, 黄梓宸, & 李敏强. (2022). 数字经济下工业互联网平台信息共享激励机制研究. 北京交通大学学报 (社会科学版), 21(02), 1.
  • 丁伟, 明振军, 王国新, & 阎艳. (2022). 基于多层次 LSTM 网络的多智能体攻防效能动态预测模型. 兵工学报, 0.
  • 范春梅, 吴阳, & 李华强. (2022). 奖惩机制和游客参与下的低价游监管——基于三方演化博弈视角. 管理评论, 34(3), 290.
  • 毕崇武, 贠婕, 周静虹, & 叶光辉. (2022). 引文内容视角下的引文网络知识流动网络分析. 情报科学, 39(1), 79.
  • 王荪馨, 王彦明, 孔杰, & 申高攀. (2022). 一种柔性缩放的群机器人形态自修复方法. 西北工业大学学报, 40(1).
  • 毕崇武, 贠婕, 周静虹, & 叶光辉. (2022). 引文内容视角下的引文网络知识流动网络分析. 情报科学, 39(1), 79.
  • 袁博. (2022). 基于动态博弈的网络防御策略选取方法研究 (Master's thesis, 郑州轻工业大学).
  • 曾杨, 黎金玲, 郭海湘, & 陈卫明. (2022). 基于风险感知的室内疏散情绪传染与干预策略研究. 系统仿真学报, 34(12), 2691.
  • 薛霄, 于湘凝, 周德雨, 彭超, 王晓, 周长兵, & 王飞跃. (2022). 计算实验方法的溯源, 现状与展望. 自动化学报, 48, 1-26.
  • 张鑫. (2022). 信息激励对居民垃圾分类行为影响机理及政策仿真研究 (Master's thesis, 中国矿业大学).
  • 加藤大望, 矢田昇平, & 倉橋節也. (2022). Multi-agent system を用いた工場内 AGV 搬送システムの解析. In 人工知能学会全国大会論文集 第 36 回 (2022) (pp. 3O4GS502-3O4GS502). 一般社団法人 人工知能学会.
  • \
  • جعفری, جیریایی, مسگری, & محمد سعدی. مدلسازی عامل مبنای گسترش بیماری مالاریا. Journal of Geomatics Science and Technology, 11(1), 205-219.‎
  • رمضانی, محمد, میرزاحسین, رصافی, & امیرعباس. (2022). مدل عامل-مبنای سوخت‌گیری وسایل نقلیه‌ی شخصی با رویکرد مدیریت تقاضا و مقایسه‌ی نتایج آن با رجحان بیان‌شده‌ی کاربران: مطالعه‌ی موردی کلان‌شهر تهران. نشریه مهندسی عمران امیرکبیر.‎
  • رحمان آرش. فشار همتایان در استعمال دخانیات و تاثیر آن بر رفاه اجتماعی؛ یک بررسی با مدل سازی و شبیه سازی مبتنی بر عامل.
  • رضائی, & وحیدنیا. (2022). راهکارهای بازدارنده برای جلوگیری از سیل به کمک سنجش از دور و رویکردهای تلفیقی منطق فازی و مدل سازی عامل مبنا. فصلنامه علمی-پژوهشی اطلاعات جغرافیایی «سپهر», 31(121), 111-125.
  • عباسی سیر, سلمان, هاشمی گهر, فیضی, & عمّار. (2022). مدل‌سازی عامل‌بنیان رفتار سهامداران در بازار اوراق بهادار تهران (مورد مطالعه: شرکت فولاد مبارکه اصفهان). پژوهش های نوین در تصمیم گیری, 7(1), 88-114.‎
  • حسینی, آذر, عادل, آذرفر, & عبادی. (2022). شناسایی و ارزیابی ریسک‌های زنجیره تأمین صنعت بیمه با رویکرد شبیه‌سازی عامل بنیان. پژوهش های پیشرفت: سیستم ها و راهبردها, 2(4), 11-44.‎
  • مینایی, مژده, وحیدنیا, & محمد حسن. (2022). راهکارهای بازدارنده برای جلوگیری از سیل به کمک سنجش‌ازدور و مدل‌سازی عامل مبنا (مطالعه موردی: شهرستان شوش). مخاطرات محیط طبیعی.‎
  • Дубовський, А. А. (2022). АНАЛІЗ ПРИНЦИПІВ, МЕТОДІВ І ПРОГРАМНИХ ЗАСОБІВ ПРОГНОЗУВАННЯ ВАКАНСІЙ НА РИНКУ ПРАЦІ. Тези доповідей VІ Міжнародної науково-практичної конференції «Інформаційні технології в освіті, науці і техніці»(ІТОНТ-2022),(Черкаси, 23-25 червня 2022 р.)[Електронний ресурс]. Черкаси: ЧДТУ, 2022. 220 с., 107.
  • ИЛЬИНСКИЙ, А. И. Учредители: Богомолов Александр Иванович. ХРОНОЭКОНОМИКА Учредители: Богомолов Александр Иванович, (4), 62-65.
  • مینایی, وحیدنیا, & محمد حسن. (2022). راهکارهای بازدارنده برای جلوگیری از سیل به کمک سنجش‌ازدور و مدل‌سازی عامل مبنا (مطالعه موردی: شهرستان شوش). مخاطرات محیط طبیعی, 1-1.‎
  • اسدی, رحمان, & شاه محمدی درمنی. مدل‌سازی و شبیه‌سازی مراحل مختلف رشد و پاسخ به درمان سرطان سرویکس. مجله انفورماتیک سلامت و زیست پزشکی, 8(2), 140-152.‎
  • Saba, J., Hel-Or, H., & Levy, S. T. תוכרעמ לש םייבושיח םילדומ תיינב ידי לע הדימל םייניבה תביטח ידימלת ברקב עדמב תובכרומ.‎
  • Сарнацький, В. В., & Баклан, І. В. МЕТОДИ ТА ЗАСОБИ МОДЕЛЮВАННЯ РОЗПОВСЮДЖЕННЯ ІНФЕКЦІЙНИХ ЗАХВОРЮВАНЬ.
  • Писковая, Е. А., & Димов, А. В. (2022). ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ ПЕРЕВОЗОЧНОГО ПРОЦЕССА НА ЖЕЛЕЗНОДОРОЖНОМ ТРАНСПОРТЕ. In Повышение управленческого, экономического, социального и инновационно-технического потенциала предприятий, отраслей и народно-хозяйственных комплексов (pp. 212-215).
  • САМОЙЛОВА, К. В., & ЗАМЯТИНА, Е. Б. (2022). Архитектура программной системы для проектирования надежных бизнес-процессов. Труды Института системного программирования РАН, 34(2), 87-76.
  • 박원범, & 이문걸. (2022, June). 대대급 무인기 이동표적 탐색방안. In 2022 년 한국산업경영시스템학회 춘계학술대회 (pp. 575-579).
  • 정혜영, 서보순, 손유진, 김미진, 김병만, & 손일수. (2022). 행위자기반모형 (ABM) 을 활용한 저출생 현상 탐색. 교육혁신연구, 32, 139-168.

2021

  • Abbasi, K. M., Khan, T. A., & ul Haq, I. (2021). Modeling-framework for model-based software engineering of complex Internet of things systems. Mathematical Biosciences and Engineering, 18(6), 9312-9335.
  • Abbott, R., & Lim, J. (2021, July). PyLogo: A Python Reimplementation of (Much of) NetLogo. In SIMULTECH (pp. 199-206).
  • Abd Elhamid, M., Abdelaziz, T., & Bassioni, H. (2021). FACTORS AFFECTING THE THICKNESS OF REPLACEMENT LAYER ON MEDIUM CLAY. ASEAN Engineering Journal, 11(4), 232-245.
  • Abdollahian, M., Chang, Y. L., & Lee, Y. Y. (2021, September). A Complex Adaptive System Approach for Anticipating Technology Diffusion, Income Inequality and Economic Recovery. In International Conference on Computational Science and Its Applications (pp. 251-262). Springer, Cham.
  • Abrami, G., Daré, W. S., Ducrot, R., Salliou, N., & Bommel, P. Participatory modelling. (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems, 189.
  • Abrahamson, D. (2021). Grasp actually: An evolutionist argument for enactivist mathematics education. Human Development. https://doi.org/10.1159/000515680
  • Abrahamson, D., Worsley, M., Pardos, Z., & Ou, L. (2021). Learning analytics of embodied design: Enhancing synergy. International Journal of Child-Computer Interaction, 100409. https://doi.org/10.1016/j.ijcci.2021.100409
  • Abrica, N. L., & Ontiveros, J. A. A. A MODEL FOR REBELLION INFLUENCED BY OPINION AND APPLIED PUBLIC POLICY. Computational Sociology, 201.
  • Abuga, D., & Raghava, N. S. (2021). Real-time Smart Garbage Bin Mechanism for Solid Waste Management in Smart Cities. Sustainable Cities and Society, 103347.
  • Acheampong, R. A., & Asabere, S. B. (2021). Simulating the co-emergence of urban spatial structure and commute patterns in an African metropolis: A geospatial agent-based model. Habitat International, 110, 102343.
  • Agarwal, Ankit. (2021). Agent-based model of broadband adoption in unserved and underserved areas. Masters Theses. 7973.
  • Agha-Hoseinali-Shirazi, M., Bozorg-Haddad, O., Laituri, M., & DeAngelis, D. (2021). Application of Agent Base Modeling in Water Resources Management and Planning. In Essential Tools for Water Resources Analysis, Planning, and Management (pp. 177-216).
  • Agustin, A. D., Ferrer, J. C. M., Bolingot, H. J. M., Celestre, J. D. I., Oppus, C. M., & Monje, J. C. N. (2021, November). Agent-based Modeling of COVID-19 Infection Rate vis-à-vis the Philippine Government Community Quarantine and Face Covering Measures. In 2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) (pp. 1-8). IEEE.
  • Ahimbisibwe, V., Lippe, M., Auch, E., Groeneveld, J., Tumwebaze, S. B., & Berger, U. (2021). Understanding smallholder farmer decision making in forest land restoration using agent-based modeling. Socio-Environmental Systems Modelling, 3, 18036-18036.
  • Ahn, S., & Yun, S. J. (2021). A Study on Residents' Participation in Rural Tourism Project Using an Agent-Based Model-Based on the Theory of Planned Behavior. Journal of Korean Society of Rural Planning, 27(2), 77-89.
  • Ahrweiler, P. (2021). Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019. Springer Nature.
  • Ahumada-Tello, E., & Ramos, K. (2021). Complejidad Social y Educación Superior. Análisis Crítico Basado en Agentes. Revista Ciencias de la Complejidad, 2(Edición Especial), 51-59.
  • Akundi, A., & Smith, E. (2021). Quantitative Characterization of Complex Systems—An Information Theoretic Approach. Applied System Innovation, 4(4), 99.
  • Alajlan, A., Edris, A., Heckendorn, R. B., & Soule, T. (2021). Using Neural Networks and Genetic Algorithms for Predicting Human Movement in Crowds. In Advances in Artificial Intelligence and Applied Cognitive Computing (pp. 353-368). Springer, Cham.
  • Alam, M. D. (2021). Development of a Mass Evacuation Decision Support Tool.
  • Albarrán, J. C., & Ramırez, E. C. Digital Twin in Water Supply Systems to Industry 4.0: The Holonic Production Unit. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA LATIN AMERICA 2021, 42.
  • Albuquerque, E. P. D. (2021). The Creation and Diffusion of Knowledge-an Agent Based Modelling Approach. UCD Centre for Economic Research Working Paper Series; WP2021/13. University College Dublin. School of Economics. 1-22.
  • Alcon, A. G. (2021). Alcon Reinforces Strength of Industry-Leading Ophthalmology Portfolio with Largest Surgical Device Scientific Presence at ASCRS 2021 Folgen.
  • Alexander, S. O. (2021). Evaluation, Communication, and Integration of Climate Information across Scales to Foster Local Decision-Making and Support Community Resilience (Doctoral dissertation, The University of Wisconsin-Madison).
  • Alfaro, J. F., & Miller, S. A. (2021). Analysis of electrification strategies for rural renewable electrification in developing countries using agent-based models. Energy for Sustainable Development, 61, 89-103.
  • Al-Gharaibeh, R. S., & Ali, M. Z. (2021). Knowledge Sharing Framework: a Game-Theoretic Approach. Journal of the Knowledge Economy, 1-35.
  • Alimboyong, C. R. (2021). Modeling virus spread on a network using NetLogo for optimum network management. Indonesian Journal of Electrical Engineering and Computer Science, 23(1), 370-377.
  • Alipour, M., Salim, H., Stewart, R. A., & Sahin, O. (2021). Residential solar photovoltaic adoption behaviour: End-to-end review of theories, methods and approaches. Renewable Energy.
  • Allioui, H., Sadgal, M., & Elfazziki, A. (2021). Optimized control for medical image segmentation: improved multi-agent systems agreements using Particle Swarm Optimization. Journal of Ambient Intelligence and Humanized Computing, 1-19.
  • Allison, A. E., Dickson, M. E., Fisher, K. T., & Thrush, S. F. Communicating drivers of environmental change through transdisciplinary human‐environment modelling. Earth's Future, e2020EF001918.
  • Al-Najjar, A. A. M. (2021). Optimizing MANETs Network Lifetime Using a Proactive Clustering Algorithm. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(2), 3280-3292.
  • Alsharhan, A. M. (2021). Survey of Agent-Based Simulations for Modelling COVID-19 Pandemic. Advances in Science, Technology and Engineering Systems Journal, 6(2), 439-447.
  • Altun, K., ALTUNTAŞ, S., & DERELİ, T. An interaction-oriented multi-agent SIR model to assess the spread of SARS-CoV-2. Hacettepe Journal of Mathematics and Statistics, 1-12.
  • Álvarez-Pomar, L., & Rojas-Galeano, S. (2021). Impact of Personal Protection Habits on the Spread of Pandemics: Insights from an Agent-Based Model. The Scientific World Journal, 2021.
  • Alzoor, F. S., Ezzeldin, M., Mohamed, M., & El-Dakhakhni, W. (2021). Prioritizing Bridge Rehabilitation Plans through Systemic Risk-Guided Classifications. Journal of Bridge Engineering, 26(7), 04021038.
  • Ambrosius, F. H., Kramer, M. R., Spiegel, A., Bokkers, E. A., Bock, B. B., & Hofstede, G. J. (2022). Diffusion of organic farming among Dutch pig farmers: An agent-based model. Agricultural Systems, 197, 103336.
  • Ambrosius, F. H., Kramer, M. R., Spiegel, A., Bokkers, E. A., Bock, B. B., & Hofstede, G. J. UNDERSTANDING DIFFUSION OF ORGANIC FARMING AMONG DUTCH PIG FARMERS: AN AGENT-BASED MODEL. Transition through markets, 93.
  • Amparore, E. G., Beccuti, M., Castagno, P., Franceschinis, G., Pennisi, M., & Pernice, S. (2021, December). Multiformalism modeling and simulation of immune system mechanisms. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 3259-3266). IEEE.
  • An, G., & Cockrell, R. C. (2021). Agent-Based Modeling of Systemic Inflammation: A Pathway Toward Controlling Sepsis. In Sepsis (pp. 231-257). Humana, New York, NY.
  • An, L., Grimm, V., Sullivan, A., TurnerII, B. L., Malleson, N., Heppenstall, A., ... & Tang, W. (2021). Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457, 109685.
  • An, S., Broniec, W., Rugaber, S., Weigel, E., Hammock, J., & Goel, A. (2021). Recognizing Novice Learner’s Modeling Behaviors. In Procs. 19th International Conference on Intelligent Tutoring Systems.
  • An, X., Qi, L., Zhang, J., & Jiang, X. (2021). Research on dual innovation incentive mechanism in terms of organizations’ differential knowledge absorptive capacity. Plos one, 16(8), e0256751.
  • Andrade, J. P. B., Maia, J. E. B., & de Campos, G. A. L. (2021). Centralized Algorithms Based on Clustering with Self-tuning of Parameters for Cooperative Target Observation. Revista de Informática Teórica e Aplicada, 28(2), 39-49.
  • Andrae, S., & Pobuda, P. (2021). Welche Modellierungswerkzeuge stehen zur Verfügung?. In Agentenbasierte Modellierung (pp. 37-41). Springer Gabler, Wiesbaden.
  • Anebagilu, P. K., Dietrich, J., Prado-Stuardo, L., Morales, B., Winter, E., & Arumi, J. L. (2021). Application of the theory of planned behavior with agent-based modeling for sustainable management of vegetative filter strips. Journal of Environmental Management, 284, 112014.
  • Angel, M. C. M., Humberto, D. B., & Alfredo, T. M. (2021). Optimization of Vehicle Flow Times in a Single Crossing System Through the Development of a Multi-Agent Platform. Journal homepage: http://iieta. org/journals/isi, 26(4), 387-392.
  • Anokhin, A., Burov, S., Parygin, D., Rent, V., Sadovnikova, N., & Finogeev, A. (2021). Development of Scenarios for Modeling the Behavior of People in an Urban Environment. In Society 5.0: Cyberspace for Advanced Human-Centered Society (pp. 103-114). Springer, Cham.
  • Antczak, T., Skorupa, B., Szurlej, M., Weron, R., & Zabawa, J. (2021). Simulation modeling of epidemic risk in supermarkets: Investigating the impact of social distancing and checkout zone design (No. WORMS/21/05). Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
  • Anvari, S., Nambiar, S., Pang, J., & Maftoon, N. (2021). Computational Models and Simulations of Cancer Metastasis. Archives of Computational Methods in Engineering, 1-23.
  • Anzola, D. (2021). Capturing the representational and the experimental in the modelling of artificial societies. European Journal for Philosophy of Science, 11(3), 1-29.
  • Aqib, M., & Ukil, A. (2020, November). Modelling of Electric Vehicle Charging and Discharging Profile to Mimic Real life Scenario at Charging Stations. In 2020 IEEE REGION 10 CONFERENCE (TENCON) (pp. 501-505). IEEE.
  • Arasteh, M. A., & Farjami, Y. (2021). New Hydro-economic System Dynamics and Agent-based Modeling for Sustainable Urban Groundwater Management: A Case Study of Dehno, Yazd Province, Iran. Sustainable Cities and Society, 103078.
  • Arico, F., Annatelli, M., & Trapasso, G. (2021). Turning mustard gas chemistry into green chemistry: a new tool for pharmaceutical synthesis. In 6th Green & Sustainable Chemistry Conference. Elsevier.
  • Ariosa Hernández, R. (2021). N2P: Netlogo to Pandora, noves funcionalitats i mòdul de visualització en entorns ABM (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • Ascenzi, I. (2021). Forest Credits to Foster Reforestation in the Brazilian Atlantic Forest (Master's thesis).
  • Asiddao, M. D., & Bongolan, V. P. (2021). Agent-Based Fire-Spreading Model in a Dense Urban Community. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 46, 35-40.
  • Aslan, U. Wu, S. P., Horn, M., & Wilensky, U. (2021). Connecting "the chemistry triplet" through co-designing computational models with teachers: A case study on calorimetry . Paper presented at the 2021 Annual Meeting of the American Education Research Association (AERA).
  • Astudillo, Y. A. P. A Hybrid Control Architecture for an Automated Storage and Retrieval System. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA LATIN AMERICA 2021, 30.
  • Av-Shalom, N. A. Y., Duncan, R. G., & Chinn, C. A. (2021). Students’ Conceptualizations of the Role of Evidence in Modeling. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Azadi, F., Mitrovic, N., & Stevanovic, A. (2021). Impact of Shared Lanes on Performance of the Combined Flexible Lane Assignment and Reservation-based Intersection Control. Transportation Research Record, 03611981211064274.
  • Azadi, M., Rahman, A., & ShahMohamadi, F. (2021). Modeling and simulation of ovarian cancer and tumor growth and spread in different stages of ovarian cancer according to the TNM system. Journal of Health and Biomedical Informatics, 8(1), 42-54.
  • Azizi, A., Mubayi, A., & Mubayi, A. (2021). Social Ecological Contexts and Alcohol Drinking Dynamics: An Application of the Survey Data-Driven Agent-Based Model for University Students. Journal of the Indian Institute of Science, 1-21.
  • Aziz, Z. A., Abdulqader, D. N., Sallow, A. B., & Omer, H. K. (2021). Python Parallel Processing and Multiprocessing: A Rivew. Academic Journal of Nawroz University, 10(3), 345-354.
  • Bagnoli, F., de Bonfioli Cavalcabo, G., Casu, B., & Guazzini, A. (2021). Community Formation as a Byproduct of a Recommendation System: A Simulation Model for Bubble Formation in Social Media. Future Internet, 13(11), 296.
  • Bandemer, L., & McCarthy, K. S. (2021). Supporting Comprehension in Computer-Based Science Simulations (No. 6188). EasyChair.
  • Barceló, J. G. A., & Garcés, R. A. A. (2021). Inclusión de la propensión al autoempleo en el proceso de emparejamiento del mercado laboral. Problemas del Desarrollo. Revista Latinoamericana de Economía, 52(207).
  • Barnes, B., Dunn, S., Pearson, C., & Wilkinson, S. (2021). Improving Human Behaviour in Macroscale City Evacuation Agent-Based Simulation. International Journal of Disaster Risk Reduction, 102289.
  • Barop, J. (2021). GENERALISING HOTELLING’S LAW: Economic and Philosophical Findings from Agent-Based Modelling. Rerum Causae, 12(1).
  • Barrón-Estrada, M. L., Zatarain-Cabada, R., Romero-Polo, J. A., & Monroy, J. N. (2021). Patrony: A mobile application for pattern recognition learning. Education and Information Technologies, 1-24.
  • Barton, A., Volna, E., Kotyrba, M., & Jarusek, R. (2021). Proposal of a Control Algorithm for Multiagent Cooperation Using Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems.
  • Bastani, M., & Jahan, A. (2021). Integration of Taguchi-Simulation Method for Improving Banking Services. Sustainable Operations and Computers.
  • Battaglia, O. R., Di Paola, B., & Fazio, C. (2021). Exploring the Coherence of Student Reasoning when Responding to Questionnaires on Thermally Activated Phenomena. Eurasia Journal of Mathematics, Science and Technology Education, 17(7), em1977.
  • Baulenas, E., Baiges, T., Cervera, T., & Pahl-Wostl, C. (2021). How do structural and agent-based factors influence the effectiveness of incentive policies? A spatially explicit agent-based model to optimize woodland-for-water PES policy design at the local level. Ecology and Society, 26(2).
  • Bazzanella, E., & Santos, F. (2021). Does a Q-Learning NetLogo Extension Simplify the Development of Agent-based Simulations?. Proceedings of the 15thWorkshop-School on Agents, Environments, and Applications (WESAAC), to appear.
  • Beccuti, M., Castagno, P., Franceschinis, G., Pennisi, M., & Pernice, S. (2021). A Petri Net Formalism to Study Systems at Different Scales Exploiting Agent-Based and Stochastic Simulations. In Performance Engineering and Stochastic Modeling (pp. 22-43). Springer, Cham.
  • Belavadi, P., Burbach, L., Ziefle, M., & Valdez, A. C. (2021, July). Finding a Structure: Evaluating Different Modelling Languages Regarding Their Suitability of Designing Agent-Based Models. In International Conference on Human-Computer Interaction (pp. 201-219). Springer, Cham.
  • Belda, A., Giancola, E., Williams, K., Dabirian, S., Jradi, M., Volpe, R., ... & Eicker, U. (2022). Reviewing Challenges and Limitations of Energy Modelling Software in the Assessment of PEDs Using Case Studies. In Sustainability in Energy and Buildings 2021 (pp. 465-477). Springer, Singapore.
  • Beltrán Pérez, G., & Rodríguez González, D. G. An agent-based approach for tourist planning.
  • Berger, C., & Mahdavi, A. (2021, July). Approaching the human dimension of building performance via agent-based modeling. In ECPPM 2021-eWork and eBusiness in Architecture, Engineering and Construction: Proceedings of the 13th European Conference on Product & Process Modelling (ECPPM 2021), 15-17 September 2021, Moscow, Russia (p. 171). CRC Press.
  • Bergholm, J. (2021). The Data Retention Saga Continued–from Tele2 Sverige to Privacy International and La Quadrature du Net. JFT, 2021(2), 111-139.
  • Bernardin, A., Martínez, A. J., & Perez-Acle, T. (2021). On the effectiveness of communication strategies as non-pharmaceutical interventions to tackle epidemics. PloS one, 16(10), e0257995.
  • Bethencourt, J. A. B., Zayas, R. H., & Escoda, M. Á. (2021). Simulación estocástica de un brote de enfermedad respiratoria aviar en Camagüey. Revista de Producción Animal, 33(2).
  • Bezzaoucha, F. S., Sahnoun, M. H., & Benslimane, S. M. (2021, February). Multi-agent modeling and simulation of wind turbine behavior with failure propagation consideration. In 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH) (pp. 97-102). IEEE.
  • Bezzout, H., & Faylali, H. E. (2022). EmPRM: A Novel Multi-agent Model for Modeling and Simulating of Electromagnetic Waves Using Netlogo Platform. In Advances on Smart and Soft Computing (pp. 517-526). Springer, Singapore.
  • Bhunia, G. S., & Shit, P. K. (2021). GeoComputation and Spatial Modelling for Decision-Making. In GeoComputation and Public Health (pp. 221-273). Springer, Cham.
  • Biggs, R., de Vos, A., Preiser, R., Clements, H., Maciejewski, K., & Schlüter, M. (Eds.). (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems. Routledge.
  • Bijak, J., Hinsch, M., Nurse, S., Prike, T., & Reinhardt, O. (2022). Bayesian Model-Based Approach: Impact on Science and Policy. In Towards Bayesian Model-Based Demography (pp. 155-174). Springer, Cham.
  • Bijandi, M., Karimi, M., van der Knaap, W., & Bansouleh, B. F. (2021). A novel approach for multi-stakeholder agricultural land reallocation using agent-based modeling: A case study in Iran. Landscape and Urban Planning, 215, 104231.
  • Bioco, J., Cánovas, F., Prata, P., & Fazendeiro, P. (2021). SDSim: A generalized user friendly web ABM system to simulate spatiotemporal distribution of species under environmental scenarios. Environmental Modelling & Software, 105234.
  • Bissembayeva, G. (2021). Simulation and Modeling of Microorganisms in Biofilm. (Thesis).
  • Blanchard, J. R., Santos, R. O., & Rehage, J. S. (2021). Sociability interacts with temporal environmental variation to spatially structure metapopulations: A fish dispersal simulation in an ephemeral landscape. Ecological Modelling, 443, 109458.
  • Bonakdar, S. B., & Roos, M. Dissimilarity Effects on House Prices: What Is the Value of Similar Neighbours?.
  • Bonghanoy, C. (2021). A Netlogo Simulation of Behavior-Based and Random Walk (BRW) Method to Solve Area-Coverage Problem.
  • Bonin, O. (2021). 15. Thom’s catastrophe theory and Turing’s morphogenesis for urban growth modelling. Handbook on Entropy, Complexity and Spatial Dynamics: A Rebirth of Theory?, 246.
  • Bosse, S. (2021). Large-scale agent-based simulation and crowd sensing with mobile agents. Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods.
  • Bourceret, A., Amblard, L., & Mathias, J. D. (2021). Governance in social-ecological agent-based models: a review. Ecology and Society, 26(2).
  • Botetano, C., & Abrahamson, D. (2021). The Botetano arithmetic method: Introduction and early evidence. International Journal of Mathematical Education in Science and Technology, 1-19. https://doi.org/10.1080/0020739X.2020.1867916
  • Bozuyla, M., & Tola, A. T. (2021). Designing a Novel Transportation System Using Microscopic Models and Multi-Agent Approach. Automatic Control and Computer Sciences, 55(2), 125-136.
  • Brady, C. (2021). Patches as an expressive medium for exploratory multi‐agent modelling. British Journal of Educational Technology, 52(3), 1024-1042.
  • Breitwieser, L., Hesam, A., de Montigny, J., Vavourakis, V., Iosif, A., Jennings, J., ... & Bauer, R. (2021). BioDynaMo: a modular platform for high-performance agent-based simulation. Bioinformatics.
  • Brinkmann, K., Kübler, D., Liehr, S., & Buerkert, A. (2021). Agent-based modelling of the social-ecological nature of poverty traps in southwestern Madagascar. Agricultural Systems, 190, 103125.
  • Brittin, J., Araz, O. M., Ramirez-Nafarrate, A., & Huang, T. T. K. (2021). An Agent-Based Simulation Model for Testing Novel Obesity Interventions in School Environment Design. IEEE Transactions on Engineering Management.
  • Brouwer, J. (2021). Safety implications of the introduction of Autonomous Vehicles on rural roads: An agent-based modeling approach.
  • Brown, C. (2021). Quantitative modelling and computer simulation. The Routledge Handbook of Landscape Ecology.
  • Brown, S., Ferreira, C., Houck, M., & Liner, B. (2021). Conceptual Ex‐Ante Simulation for Green Stormwater Infrastructure Adoption on Private Property Using Agent‐Based Modeling. Water Environment Research.
  • Buchmann, T., Wolf, P., & Fidaschek, S. (2021). Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach. Energies, 14(3), 656.
  • Buhat, C. A. H., Lutero, D. S., Olave, Y. H., Torres, M. C., & Rabajante, J. F. (2021). Community Transmission of Respiratory Infectious Diseases using Agent-based and Compartmental Models. Mindanao Journal of Science and Technology, 19(2).
  • Burg, V., Troitzsch, K. G., Akyol, D., Baier, U., Hellweg, S., & Thees, O. (2021). Farmer's willingness to adopt private and collective biogas facilities: An agent-based modeling approach. Resources, Conservation and Recycling, 167, 105400.
  • Burova, A. A., Burov, S. S., Parygin, D. S., Finogeev, A. G., & Smirnova, T. V. (2021). Administration panel of the multi-agent modeling platform with the ability to generate graphical reports. International Journal of Open Information Technologies, 9(12), 4-14.
  • Burrows, A. C., Borowczak, M., & Mugayitoglu, B. (2022). Computer Science beyond Coding: Partnering to Create Teacher Cybersecurity Microcredentials. Education Sciences, 12(1), 4.
  • Burrows, A. C., Borowczak, M., Myers, A., Schwortz, A. C., & McKim, C. (2021). Integrated STEM for Teacher Professional Learning and Development:“I Need Time for Practice”. Educ. Sci. 2021, 11, 21.
  • Burrows, A. C., Swarts, G. P., Hutchison, L., Katzmann, J. M., Thompson, R., Freeman, L., ... & Reynolds, T. (2021). Finding Spaces: Teacher Education Technology Competencies (TETCs). Education Sciences, 11(11), 733.
  • Cadavid, L., Díez-Echavarría, L. F., & Valencia-Arias, A. (2021). Does heterogeneity operationalization matter to model the diffusion phenomena?. IEEE Latin America Transactions, 100(XXX).
  • Calderón, A., & Silva, V. (2021). Exposure forecasting for seismic risk estimation: Application to Costa Rica. Earthquake Spectra, 8755293021989333.
  • Calderoni, F., Campedelli, G. M., Szekely, A., Paolucci, M., & Andrighetto, G. (2021). Recruitment into Organized Crime: An Agent-Based Approach Testing the Impact of Different Policies. Journal of Quantitative Criminology, 1-41.
  • Camara, D., Kotzinos, D., Rousseau, P.(2021) Weak signal detection and identification in large data sets: a review of methods and applications. IEEE TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING (preprint.)
  • Campennì, M., Cronk, L., & Aktipis, A. (2021). Need-Based Transfers Enhance Resilience to Shocks: An Agent-Based Model of a Maasai Risk-Pooling System. Human Ecology, 1-14.
  • Canals, A. (2021). To hoard or to share? Strategic management of knowledge and ICTs in complex economic systems. Intangible Capital, 17(2), 148-172.
  • Cao, Y., Li, F., Xi, X., van Bilsen, D. J. C., & Xu, L. (2021). Urban livability: Agent-based simulation, assessment, and interpretation for the case of Futian District, Shenzhen. Journal of Cleaner Production, 128662.
  • Caplan, B., Covitt, B., Love, G., Berkowitz, A. R., Gunckel, K. L., McClure, C., & Moore, J. C. (2021). Using computational thinking and modeling to build water and watershed literacy. Connected Science Learning, 3(2).
  • Cardoso, R. C., & Ferrando, A. (2021). A Review of Agent-Based Programming for Multi-Agent Systems. Computers, 10(2), 16.
  • Cardoso, R. C., Ferrando, A., Briola, D., Menghi, C., & Ahlbrecht, T. (2021). Agents and Robots for Reliable Engineered Autonomy: A Perspective from the Organisers of AREA 2020. Journal of Sensor and Actuator Networks, 10(2), 33.
  • Carrella, E. (2021). No Free Lunch when Estimating Simulation Parameters. Journal of Artificial Societies and Social Simulation, 24(2).
  • Carvajal León, B. F. (2022). Modelización basada en el individuo, de un reactor CSTR con recirculación de lodos activados (Bachelor's thesis, Quito: UCE).
  • Castillo Osorio, E. E., Seo, M. S., & Yoo, H. H. (2021). Analysis of suitable evacuation routes through multi-agent system simulation within buildings. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 39(5), 265-278.
  • Ceja, A., Kane, S., & Way, M. (2021). The PEACH Model: Physiology, Exoclimatology, and Astroecology for Characterizing Habitability. Bulletin of the American Astronomical Society, 53(3), 1217.
  • CENANİ, Ş. Emergence and complexity in agent-based modeling: Review of state-of-the-art research. Journal of Computational Design, 2(2), 1-24.
  • Chabbar, S., Benmir, M., Karkri, J. E., Bensaid, K., Aboulaich, R., & Nejjari, C. (2021). Modeling and simulation of the evolution of the corona virus pandemic in a context of Migration. Journal of Theoretical and Applied Information Technology, 4363-4374.
  • Chai, S. S., & Goh, K. L. (2021). Daily Rainfall Forecasting Using Meteorology Data with Long Short-Term Memory (LSTM) Network. Journal of Optimization in Industrial Engineering.
  • Chanda, S. S. (2021). Mandating Code Disclosure is Unnecessary--Strict Model Verification Does Not Require Accessing Original Computer Code. arXiv preprint arXiv:2105.05170.
  • Chathika, G., Rand, W., & Garibay, I. (2021). Inferring mechanisms of response prioritization on social media under information overload. Scientific Reports (Nature Publisher Group), 11(1).
  • Chaves, C. J. N., Leal, B. S. S., Rossatto, D. R., Berger, U., & Palma-Silva, C. (2021). Deforestation is the turning point for the spreading of a weedy epiphyte: an IBM approach. Scientific Reports, 11(1), 1-14.
  • Chen, B., Xu, B., & Gong, P. (2021). Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities. Big Earth Data, 1-32.
  • Chen, F., Liu, J., & Chen, J. (2021). Earthquake Disaster Rescue Model Based on Complex Adaptive System Theory. Complexity, 2021.
  • Chen, J., Gong, B., Wang, Y., & Zhang, Y. (2021). Construction of Internet of things trusted group based on multidimensional attribute trust model. International Journal of Distributed Sensor Networks, 17(1), 1550147721989888.
  • Chen, J., Shi, T., & Li, N. (2021). Pedestrian evacuation simulation in indoor emergency situations: Approaches, models and tools. Safety Science, 142, 105378.
  • Chen, S. H. (2021). Humanity in the Era of Autonomous Human–machine Teams. In Systems Engineering and Artificial Intelligence (pp. 309-331). Springer, Cham.
  • Cheng, C., Luo, Y., Yu, C. B., & Ding, W. P. (2021). Social bots and mass media manipulate public opinion through dual opinion climate. Chinese Physics B.
  • Cheng, L., Guo, H., & Lin, H. (2021). Evolutionary model of coal mine safety system based on multi-agent modeling. Process Safety and Environmental Protection.
  • Cheremisina, E. N., Tokareva, N. A., Kirpicheva, E. Y., Kreider, O. A., Milovidova, A. A., & Potemkina, S. V. (2021). THE CONCEPT OF TRAINING IT PROFESSIONALS IN THE CROSS-CUTTING DIGITAL TECHNOLOGIES.
  • Chesney, T. (2021). Designing an Agent Model. Agent-Based Modelling of Worker Exploitation, 117-122.
  • Chetcuti, J., Kunin, W. E., & Bullock, J. M. (2021). Matrix composition mediates effects of habitat fragmentation: a modelling study. Landscape Ecology, 1-16.
  • Chinesta Fortes, I. (2021). Wealth Distribution and Inheritance: A Agent-Based simulation analysis.
  • Chiu, M. M., & Reimann, P. (2021). Statistical and stochastic analysis of sequence data. In International Handbook of Computer-Supported Collaborative Learning (pp. 533-550). Springer, Cham.
  • Cho, S. Y., Lim, M. J., & Im, T. (2021). The Effects of Project Based Learning on Learners' Creativity and Problem Solving. Journal of Practical Engineering Education, 13(1), 213-219.
  • Choi, T., & Park, S. (2021). Theory building via agent-based modeling in public administration research: vindications and limitations. International Journal of Public Sector Management.
  • Chukundah, R. E. (2021). Reforesting the Atlantic Forest, through Forest Credits, PES or Carbon Credits (Bachelor's thesis).
  • Chunxiao, Z., & Junjie, G. (2021). Autonomy-oriented proximity mobile social network modeling in smart city for emergency rescue. International Journal of Distributed Sensor Networks, 17(12), 15501477211061252.
  • Chun-bei, X. I. A. O., Jun, M. A., & Chen, Y. A. N. G. (2021). Simulation study on fire spread of traditional building community in Beijing Nanluoguxiang region. Fire Science and Technology, 40(6), 865.
  • Civico, M. (2021). Language policy and planning: a discussion on the complexity of language matters and the role of computational methods. SN Social Sciences, 1(8), 1-22.
  • Cimino, M. G., Minici, D., Monaco, M., Petrocchi, S., & Vaglini, G. (2021). A hyper-heuristic methodology for coordinating swarms of robots in target search. Computers & Electrical Engineering, 95, 107420.
  • Coelho, C. G. C., & Ralha, C. G. (2021). MASE-EGTI: An agent-based simulator for environmental land change. Environmental Modelling & Software, 105252.
  • Collard, J. D., Stattner, E., & Gergos, P. (2021). The “ReadyPark” Collaborative Parking Search Strategy. Smart Cities, 4(3), 1130-1145.
  • Collard, P. (2021). The “flat peer learning” agent-based model. Journal of Computational Social Science, 1-27.
  • Condie, S. A., Anthony, K. R., Babcock, R. C., Baird, M. E., Beeden, R., Fletcher, C. S., ... & Westcott, D. A. (2021). Large-scale interventions may delay decline of the Great Barrier Reef. Royal Society Open Science, 8(4), 201296.
  • Conroy-Beam, D. (2021). Couple Simulation: A Novel Approach for Evaluating Models of Human Mate Choice. Personality and Social Psychology Review, 1088868320971258.
  • Crevier, L. P., Salkeld, J. H., Marley, J., & Parrott, L. (2021). Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities. Ecological Modelling, 446, 109505.
  • Cruz, D. A., & Kemp, M. (2021). Hybrid computational modeling methods for systems biology. Progress in Biomedical Engineering.
  • Cruz Ardila, J. C., Trujillo Perdomo, J. F., Zambrano Vidal, L. F., Panesso Patiño, V., Arévalo Soto, A., & Girón Restrepo, G. A. (2021). Boletín de Investigaciones agosto de 2021.
  • Cubeiro, M. T. (2021). Ignorancia y complejidad: la sociología de la enfermedad mental. Acciones e Investigaciones Sociales, (42).
  • Cunha, B., Brito, C., Araújo, G., Sousa, R., Soares, A., & Silva, F. A (2021). Smart traffic control in vehicle ad-hoc networks: a Systematic Literature Review. Universidade Federal do Piauí.
  • Currat, M., Quilodrán, C. S., & Excoffier, L. (2021). Simulations of Human Dispersal and Genetic Diversity. In Evolution of the Human Genome II (pp. 231-256). Springer, Tokyo.
  • Currie, T., Campenni, M., Flitton, A., Njagi, T., Ontiri, E., Perret, C., & Walker, L. (2021). Code supporting The Cultural Evolution & Ecology of Institutions.
  • Currie, T. E., Campenni, M., Flitton, A., Njagi, T., Ontiri, E., Perret, C., & Walker, L. (2021). The cultural evolution and ecology of institutions. Philosophical Transactions of the Royal Society B, 376(1828), 20200047.
  • da Silva, A. C. G., de Lima, C. L., da Silva, C. C., Moreno, G. M. M., Silva, E. L., Marques, G. S., ... & dos Santos, W. P. (2022). Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis, 333-357.
  • da Silva, A. J. P., & Arroio, A. (2021). Tempo e tecnologia no processo de visualização em Química: um estudo exploratório sobre as práticas de professores em formação inicial. Revista de Investigação Tecnológica em Educação em Ciências e Matemática, 1, 80-99.
  • Dabholkar, S., Horn, M., & Wilensky, U. (2021). A technology-mediated co-design approach for integrating Computational Thinking in a science classroom. Paper presented in the 2021 Annual Meeting of the American Education Research Association (AERA).
  • Dabholkar, S., (2021). Designing computational models as Emergent Systems Microworlds for learning biomaking digitally. In Walker, J. and Strawhacker, A. (Symposium chairs), The Biomaker Ecosystem: Technologies, Spaces and Curriculum for K-12 Making with Biology. Presented at The 2021 Annual Meeting of American Education Research Association (AERA).
  • Dabholkar, S., Peel, A., Hao, D., Kelter, J., Horn, M., & Wilensky, U. (2021). Analysis of Co-designed Biology Units Integrated with Computational Thinking Activities. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 665-668). Bochum, Germany: International Society of the Learning Sciences.
  • Dabholkar, S., Tran, S., Horn, M. S., & Wilensky, U. (2021). Students' Attitudinal Change After Participating in a CT integrated Biology Unit . In Dabholkar S. (Symposium organizer), Integrating Computational Thinking in Science Curricula: Professional Development and Student Assessment. Presented at the 2021 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Daems, D. (2021). Social Complexity and Complex Systems in Archaeology. Routledge.
  • Das, D. (2021). Agent Based Virus Model using NetLogo: Infection Propagation, Precaution, Recovery, Multi-site Mobility and (Un) Lockdown. arXiv preprint arXiv:2102.00844.
  • Datseris, G., Vahdati, A. R., & DuBois, T. C. (2021). Agents. jl: A performant and feature-full agent based modelling software of minimal code complexity. arXiv preprint arXiv:2101.10072.
  • David, N. G. (2021). Reframing Educational Leadership Research in the Twenty-First Century. In Concept and Design Developments in School Improvement Research (pp. 107-135). Springer, Cham.
  • Day, T. E., Dong, Y., & Gopakumar, B. (2021). How Many of Those Things Do We Really Need? Discrete Event Simulation. In Comprehensive Healthcare Simulation: Improving Healthcare Systems (pp. 187-195). Springer, Cham.
  • DeAngelis, D. L., Franco, D., Hastings, A., Hilker, F. M., Lenhart, S., Lutscher, F., ... & Tyson, R. C. (2021). Towards Building a Sustainable Future: Positioning Ecological Modelling for Impact in Ecosystems Management. Bulletin of Mathematical Biology, 83(10), 1-28.
  • Delcea, C., Cotfas, L. A., Milne, R. J., Xie, N., & Mierzwiak, R. (2021). Grey clustering of the variations in the back-to-front airplane boarding method considering COVID-19 flying restrictions. Grey Systems: Theory and Application.
  • Deng, S., & Zhang, J. (2021). Modernization Versus Dependency Approaches to Sustainable Development--Based on the UN Report 2019. In E3S Web of Conferences (Vol. 275, p. 02029). EDP Sciences.
  • Derkach, T. M. (2021, March). The origin of misconceptions in inorganic chemistry and their correction by computer modelling. In Journal of Physics: Conference Series (Vol. 1840, No. 1, p. 012012). IOP Publishing.
  • de Castro, M. G. A., & García-Peñalvo, F. J. ICT methodologies for teacher professional development in Erasmus+ projects related to eLearning. In 2021 XI International Conference on Virtual Campus (JICV) (pp. 1-6). IEEE.
  • de Jong, K., Panja, D., van Kreveld, M., & Karssenberg, D. (2021). An environmental modelling framework based on asynchronous many-tasks: scalability and usability. Environmental Modelling & Software, 104998.
  • de Kemp, E. A. (2021). Spatial Agents for Geological Surface Modelling. Geoscientific Model Development Discussions, 1-32.
  • de Oliveira, G. D., Porto, P. P. G., Alves, C. D. M. A., & Ralha, C. G. (2021). An Agent-Based Model for Simulating Irrigated Agriculture in the Samambaia Basin in Goiás. Revista de Informática Teórica e Aplicada, 28(2), 107-123.
  • de Vos, A., Maciejewski, K., Bodin, Ö., Norström, A., Schlüter, M., & Tengö, M. The practice and design of social-ecological systems research. (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems, 47.
  • de Quadros, C. E. P., Adamatti, D. F., & de Lima Bicho, A. (2021, October). BioTraffic: a bio-inspired behavioral model to vehicle traffic simulation. In 2021 20th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames) (pp. 29-38). IEEE.
  • de Souza, V. M., Bloemhof, J., & Borsato, M. (2021). Assessing the eco-effectiveness of a solid waste management plan using agent-based modelling. Waste Management, 125, 235-248.
  • de Souza Almeida, F. M., Gomes, A. P., & de Freitas, A. F. (2021). Social networks and efficiency in dairy farming: the case of the Program for the Development of Dairy Farming in Minas Gerais, Brazil. Livestock Science, 104401.
  • de Oliveira Simoyama, F., Sarti, F. M., & Battisti, M. C. G. (2021). Effects of disclosing inspection scores of health facilities. Socio-Economic Planning Sciences, 101183.
  • de Velazco, F. F., Lara, E. C., & Luna, S. R. (2021). Proposal of a Model from the Perspective of Parsons Functional-Structural Theory. Journal of Systemics, Cybernetics and Informatics, 19(8), 182-197.
  • de Wildt, T. E., Boijmans, A. R., Chappin, E. J., & Herder, P. M. (2021). An ex ante assessment of value conflicts and social acceptance of sustainable heating systems: An agent-based modelling approach. Energy Policy, 153, 112265.
  • Dhou, K., & Cruzen, C. (2021, June). An Innovative Employment of the NetLogo AIDS Model in Developing a New Chain Code for Compression. In International Conference on Computational Science (pp. 17-25). Springer, Cham.
  • Díaz-de la Fuente, S., de Armiño Pérez, C. A., Delgado, R. A., Villahoz, J. J. L., Cosío, Á. H., del Campo, M. Á. M., & del Olmo Martínez, R. PROTOCOLO–Evaluar la competencia transversal trabajo en equipo en estudiantes de grado y máster mediante la entrevista por competencias. In Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. PressBooks.
  • Díaz Monsalvea, J., Enríquez Corredor, I., Pinto Moreno, Á. M., & Sánchez Santamaría, J. Evaluación de una emulación de un sistema ASRS acoplado a un sistema de compras.
  • Dignum, F. (2021). The Real Impact of Social Simulations During the COVID-19 Crisis. In Social Simulation for a Crisis (pp. 319-329). Springer, Cham.
  • Dimka, J., & Sattenspiel, L. (2021). “We didn't get much schooling because we were fishing all the time”: Potential impacts of irregular school attendance on the spread of epidemics. American Journal of Human Biology, e23578.
  • Dobrota, M., Zornić, N., & Marković, A. (2021). FDI Time Series Forecasts: Evidence from Emerging Markets. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies.
  • Doeweler, F. (2021). Causes of Recruitment Limitation at Abrupt Alpine Treelines (Doctoral dissertation, Auckland University of Technology).
  • Dogaroglu, B., Caliskanelli, S.P., Tanyel, S. (2021). Comparison of Intelligent Parking Guidance System and Conventional System with Regard to Capacity Utilisation. Sustainable Cities and Society, 103152.
  • Donaldson, S. (2021). Flocc: From Agent-Based Models to Interactive Simulations on the Web. Northeast Journal of Complex Systems (NEJCS), 3(1), 6.
  • Dong, S. (2021). A Class of Public Opinion Dissemination Model considering the Information Screening Mechanism. Security and Communication Networks, 2021.
  • Dong, Z., Liu, H., & Zheng, X. (2021). The influence of teacher-student proximity, teacher feedback, and near-seated peer groups on classroom engagement: An agent-based modeling approach. Plos one, 16(1), e0244935.
  • Döpper, T., Milakovic, D., Scheel, O., Große-Wöhrmann, B., Oexle, J., Slotosch, S., ... & Widmaier, L. (2021). Expanding HLRS Academic HPC Simulation Training Programs to More Target Groups. Graphics: Steven Behun, Heather Marvin, 12(3), 13.
  • Doussin, B., Adam, C., & Georges, D. (2021). Multi-scale simulation of COVID-19 epidemics. arXiv preprint arXiv:2112.01167.
  • Dovrat, D., Tripathy, T., & Bruckstein, A. M. (2021). On Tracking and Capture in Proportional-Control Bearing-Only Unicycle Pursuit. IEEE Control Systems Letters.
  • Ducke, B., & Suchowska, P. (2021). Exploratory Network Reconstruction with Sparse Archaeological Data and XTENT. Journal of Archaeological Method and Theory, 1-32.
  • Duijsings, R. F. Y. (2021). Bargaining Model: Enhancing the Wealth and Survival of the Poor by Finding a Better Long-term Strategy (Bachelor's thesis).
  • Eberbach, C., Hmelo‐Silver, C. E., Jordan, R., Taylor, J., & Hunter, R. (2021). Multidimensional trajectories for understanding ecosystems. Science Education.
  • Edrisi, A., Lahoorpoor, B., & Lovreglio, R. (2021). Simulating Metro Station Evacuation using Three Agent-based Exit Choice Models. Case Studies on Transport Policy.
  • Effati, S., & Tavakoli, E. (2021). The Effect of Social Distancing and Personal Protective Equipment on the Outbreak of SARS-COVID-2: An Agent-Based Modeling Approach. (pre-print).
  • EFSA Scientific Committee, More, S., Bampidis, V., Benford, D., Bragard, C., Halldorsson, T., ... & Rortais, A. (2021). A systems‐based approach to the environmental risk assessment of multiple stressors in honey bees. EFSA Journal, 19(5), e06607.
  • Eismann, K. (2021). Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter. Journal of Business Economics, 1-31.
  • Eitzel, M. V., Solera, J., Hove, E. M., Wilson, K. B., Ndlovu, A. M., Ndlovu, D., ... & Veski, A. (2021). Assessing the Potential of Participatory Modeling for Decolonial Restoration of an Agro-Pastoral System in Rural Zimbabwe. Citizen Science: Theory and Practice, 6(1).
  • El Fakir, A., Fairchild, R., Tkiouat, M., & Taamouti, A. (2021). A bargaining model for profit and loss sharing entrepreneurial financing: A game theoretic model using agent based simulation. International journal of finance and economics.
  • El Mouhib, M., Azghiou, K., & Tahani, A. (2021, April). Analysis of the Impact of Traffic Density on the Compromised CAV Rate: a Multi-Agent Modeling Approach. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-6). IEEE.
  • Ellison, A. M., & Gotelli, N. J. (2021). Ants (Hymenoptera: Formicidae) and humans: from inspiration and metaphor to 21 st-century symbiont. Myrmecological News, 31.
  • Elmenreich, W., Schnabl, A., & Schranz, M. (2021). An artificial hormone-based algorithm for production scheduling from the bottom-up.
  • Embodying, P. (2021). The Pack. Design Make Play for Equity, Inclusion, and Agency: The Evolving Landscape of Creative STEM Learning, 208.
  • Encinas, D., Jara, J., Bond, R., Rosatto, D., Maccallini, L., Gomez, M., ... & Morales, M. (2021). Técnicas de modelado y simulación para arquitecturas HPC y salud. In XXIII Workshop de Investigadores en Ciencias de la Computación (WICC 2021, Chilecito, La Rioja).
  • Enderle, P., King, N., & Margulieux, L. (2021). What’s in a Wave?. The Science Teacher, 88(4).
  • Ernst, A., & Simon, K. H. (2021). Soziale Simulation als Werkzeug der transformativen Forschung. GAIA-Ecological Perspectives for Science and Society, 30(2), 134-136.
  • Eshanthini, P., Nandhakumar, S., & Bandari, R. Ground Water Modelling of Poondi Micro-Watershed, Thiruvallur, Tamil Nadu. Advances in Construction Management: Select Proceedings of ACMM 2021, 233.
  • Ewert, U. C. (2021). Agentenbasierte Modellierung in der Mediävistik, oder: Wie der Netzwerkhandel der Hansekaufleute entstanden sein könnte. Mannheim Working Papers in Premodern Economic History, 2(2), 1-27.
  • Faber, R. (2021). Accessible Data Mining for Agent-Based Simulation Models. (Master’s thesis).
  • Fahad, M., Sontheimer, K., Jimenez-Romero, C., Chavez, R. I., Diaz, S., Klijn, W., & Morrison, A. Multi-scale brain co-simulation in the Human Brain Project: EBRAINS tools for in-transit simulation and analysis.
  • Fain, J. (2021). Should retail stores locate close to a rival?. Journal of Economic Interaction and Coordination, 1-34.
  • Falcone, R., & Sapienza, A. (2021). An agent-based model to assess citizens’ acceptance of COVID-19 restrictions. Journal of Simulation, 1-15.
  • Fan, Z., Ding, N., Zhu, X., & Zhu, Y. (2021). Suicide Bombing Attack Modelling and Simulation: A Case Study of Golden Water Bridge Attack. Journal of Safety Science and Resilience.
  • Farhadi, B., Rahmani, A. M., Asghari, P., & Hosseinzadeh, M. (2021). Friendship Selection and Management in Social Internet of Things: A systematic review. Computer Networks, 108568.
  • Farhadicheshmehmorvari, A. (2021). An Agent-Based Financial Network Modeling Based on Systematic Trust (Doctoral dissertation).
  • Faweya, O., Desai, P. S., & Higgs III, C. F. (2021). Towards an agent-based model to simulate osseointegration in powder-bed 3D printed implant-like structures. Journal of the Mechanical Behavior of Biomedical Materials, 104915.
  • Fazio, M., Pluchino, A., Inturri, G., Pira, M. L., Giuffrida, N., & Ignaccolo, M. (2021). Exploring the impact of mobility restrictions on the COVID-19 spreading through an agent-based approach. arXiv preprint arXiv:2102.08226.
  • Feinberg, A., Hooijschuur, E., Rogge, N., Ghorbani, A., & Herder, P. (2021). Sustaining collective action in urban community gardens. Journal of Artificial Societies and Social Simulation, 24(3).
  • Ferguson, M., Arangala, C., Yokley, K., & Rave, M. (2021). Agent Based Simulation of Dengue with Wolbachia Intervention. Minnesota Journal of Undergraduate Mathematics, 6(1).
  • Ferrare, F. D., Baum, D. M., de Almeida Júnior, J. R., Júnior, J. B. C., & Cugnasca, P. S. (2021, October). Scenarios for the Use of eVTOLs Using Multiagent SystemsWith Netlogo: Comparison Of Parameters And The Impact On UAM. In 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) (pp. 1-7). IEEE.
  • Ferrare, F. D., Baum, D. M., de Almeida Júnior, J. R., Junior, J. B. C., & Cugnasca, P. S. (2021). Urban Air Mobility (UAM): A Model Proposal based on Agents using Netlogo. In SIMULTECH (pp. 352-359).
  • Ferraro, K. M., Schmitz, O. J., & McCary, M. A. (2021). Effects of ungulate density and sociality on landscape heterogeneity: a mechanistic modeling approach. Ecography.
  • Fitzpatrick, B. G., Federico, P., Kanarek, A., & Lenhart, S. Control of a consumer‐resource agent‐based model using partial differential equation approximation. Optimal Control Applications and Methods.
  • Flache, A., & de Matos Fernandes, C. A. (2021). 24. Agent-based computational models1. Research Handbook on Analytical Sociology, 453.
  • Florindo, A. A., Teixeira, I. P., Barrozo, L. V., Sarti, F. M., Fisberg, R. M., Andrade, D. R., & Garcia, L. M. T. (2021). Study protocol: health survey of Sao Paulo: ISA-Physical Activity and Environment. BMC Public Health, 21(1), 1-10.
  • Folke, T., & Kennedy, W. G. (2021). Agent-Based Modelling: A Bridge Between Psychology and Macro-social Science. In M. MacLachlan & J. McVeigh (Eds.), Macropsychology: A Population Science for Sustainable Development Goals, 189.
  • Foramitti, J. (2021). AgentPy: A package for agent-based modeling in Python. Journal of Open Source Software, 6(62), 3065.
  • Fouladvand, J., Rojas, M. A., Hoppe, T., & Ghorbani, A. (2021). Simulating thermal energy community formation: Institutional enablers outplaying technological choice. Applied Energy, 117897.
  • Fox, W. P. (2021). Mathematical Modeling in the Age of a Pandemic.
  • Francos, R. M., & Bruckstein, A. M. (2021). Pincer-based vs. Same-direction Search Strategies After Smart Evaders by Swarms of Agents. arXiv preprint arXiv:2104.06940.
  • Fregoso, J. H. C. (2021). Breves consideraciones sobre la naturaleza compleja de la ciencia económica. Expresión Económica. Revista de análisis, (46), 9-19.
  • Friedman, D. A., Tschantz, A., Ramstead, M. J. D., Friston, K., & Constant, A. (2021). Active Inferants: An Active Inference Framework for Ant Colony Behavior. Front. Behav. Neurosci, 15, 647732.
  • Friesen, M. R., & McLeod, R. D. (2021). Towards Equitable Hiring Practices for Engineering Education Institutions: An Individual-Based Simulation Model. In Advances in Software Engineering, Education, and e-Learning (pp. 265-276). Springer, Cham.
  • Fuchs, A., Pichler-Koban, C., Pitman, A., Elmenreich, W., & Jungmeier, M. (2021). Games and Gamification—New Instruments for Communicating Sustainability. The Sustainability Communication Reader: A Reflective Compendium, 221-243.
  • Fuchs, S., Rietsche, R., Aier, S., & Rivera, M. (2021). Is more always better? Simulating Feedback Exchange in Organizations.
  • Fu, Z., Dong, P., Li, S., Ju, Y., & Liu, H. (2021). How blockchain renovate the electric vehicle charging services in the urban area? A case study of Shanghai, China. Journal of Cleaner Production, 128172.
  • Gajary, L. C. (2021). Pathways for Theory Development: A Logic and a Methodology for Public and Nonprofit Strategic Planning (Doctoral dissertation, The Ohio State University).
  • Galán, S. F. (2021). Extending cellular evolutionary algorithms with message passing. Soft Computing, 1-12.
  • Galán, S. F. (2021). Comparative Evaluation of the Fast Marching Method and the Fast Evacuation Method for Heterogeneous Media. Applied Artificial Intelligence, 1-25.
  • Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., ... & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, 101741.
  • Gallagher, C. A., Chudzinska, M., Larsen‐Gray, A., Pollock, C. J., Sells, S. N., White, P. J., & Berger, U. (2021). From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models. Biological Reviews.
  • GAO, DEHUA. "Agent-Based Dynamics Modelling in Routine." Cambridge Handbook of Routine Dynamics (2021): 159.
  • García, R. M., de la Iglesia, D. H., de Paz, J. F., Leithardt, V. R., & Villarrubia, G. (2021, February). Urban Search and Rescue with Anti-pheromone Robot Swarm architecture. In 2021 Telecoms Conference (ConfTELE) (pp. 1-6). IEEE.
  • García-Díaz, C. (2021). Agent-Based Organizational Ecologies: A Generative Approach to Market Evolution. In Pathways Between Social Science and Computational Social Science (pp. 179-196). Springer, Cham.
  • Garg, V. (2021). Cooperative Multi-robot Target Searching and Tracking Using Velocity Inspired Robotic Fruit Fly Algorithm. SN Computer Science, 2(6), 1-12.
  • Garrido, D., Jacob, J., Silva, D. C., & Rossetti, R. J. (2021). Pedestrian Simulation In SUMO Through Externally Modelled Agents. In ECMS (pp. 111-118).
  • Gasparini, F., Giltri, M., & Bandini, S. (2021). Safety perception and pedestrian dynamics: Experimental results towards affective agents modeling. AI Communications, (Preprint), 1-15.
  • Gay, P. E., Trumper, E., Lecoq, M., & Piou, C. (2021). Importance of human capital, field knowledge and experience to improve pest locust management. Pest Management Science.
  • Gegear, R. J., Heath, K. N., & Ryder, E. F. (2021). Modeling scale up of anthropogenic impacts from individual pollinator behavior to pollination systems. Conservation Biology.
  • Gerbrands, P., & Unger, B. (2021). Policy Reform Effects in the Tax Ecosystem. Combating Fiscal Fraud and Empowering Regulators: Bringing Tax Money Back Into the COFFERS, 272.
  • Gerdes, L., Scholz-Wäckerle, M., & Schröter, J. (2021). Computerspiele und ökonomische Modellformen Auf dem Weg zu transformationskritischen Medien. Zeitschrift für Medienwissenschaft, 13(2), 35-44.
  • Gervasi, V., & Guberti, V. (2021). African swine fever endemic persistence in wild boar populations: Key mechanisms explored through modelling. Transboundary and Emerging Diseases.
  • Ghaitaranpour, A., Mohebbi, M., & Koocheki, A. (2021). An innovative model for describing oil penetration into the doughnut crust during hot air frying. Food Research International, 110458.
  • Ghorbani, A., de Bruin, B., & Kreulen, K. (2021). Studying the Influence of Culture on the Effective Management of the COVID-19 Crisis. In Social Simulation for a Crisis (pp. 189-230). Springer, Cham.
  • Ghorbani, A., Ho, P., & Bravo, G. (2021). Institutional form versus function in a common property context: The credibility thesis tested through an agent-based model. Land Use Policy, 102, 105237.
  • Ghoreishi, M., Razavi, S., & Elshorbagy, A. (2021). Understanding Human Adaptation to Drought: Agent-Based Agricultural Water Demand Modeling in the Bow River Basin, Canada. Hydrological Sciences Journal.
  • Giabbanelli, P. J., & Jackson, P. J. (2021, June). How Do Teams of Novice Modelers Choose an Approach? An Iterated, Repeated Experiment in a First-Year Modeling Course. In International Conference on Computational Science (pp. 661-674). Springer, Cham.
  • Giabbanelli, P. J., Tison, B., & Keith, J. (2021). The application of modelling and simulation to public health: Assessing the quality of Agent-Based Models for obesity. Simulation Modelling Practice and Theory, 102268.
  • Gibson, M., Slade, R., Pereira, J. P., & Rogelj, J. (2021). Comparing Mechanisms of Food Choice in an Agent-Based Model of Milk Consumption and Substitution in the UK. Journal of Artificial Societies and Social Simulation, 24(3).
  • Giordano, N., Rosati, S., Valeri, F., Borchiellini, A., & Balestra, G. (2021). Simulation of the Impact on the Workload of the Enlargement of the Clinical Staff of a Specialistic Reference Center. Studies in health technology and informatics, 281, 605-609.
  • Glake, D., Panse, F., Ritter, N., Clemen, T., & Lenfers, U. (2021). Data Management in Multi-Agent Simulation Systems. BTW 2021.
  • Gokhale, V. A. (2021). 7 Positioning Resilience in. Reliability-Based Analysis and Design of Structures and Infrastructure, 89.
  • Goldstein, E., Erinjery, J. J., Martin, G., Kasturiratne, A., Ediriweera, D. S., de Silva, H. J., ... & Iwamura, T. (2021). Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes. PLOS Neglected Tropical Diseases, 15(1), e0009047.
  • Gómez González, L. (2021). Medidas para la prevención del COVID-19 en los procesos de la aviación comercial de pasajeros (Bachelor's thesis, Universitat Politècnica de Catalunya).
  • González-Mon, B., Lindkvist, E., Bodin, Ö., Zepeda-Domínguez, J. A., & Schlüter, M. (2021). Fish provision in a changing environment: The buffering effect of regional trade networks. Plos one, 16(12), e0261514.
  • Griesemer, M., & Sindi, S. S. (2022). Rules of Engagement: A Guide to Developing Agent-Based Models. In Microbial Systems Biology (pp. 367-380). Humana, New York, NY.
  • Guevara-Rivera, E., Osorno-Hinojosa, R., Zaldivar-Carrillo, V., & Perez-Ortiz, H. (2021). Dynamic simulation methodology for implementing circular economy: A new case study. Journal of Industrial Engineering and Management, 14(4), 850-862.
  • Gül, N., Hasgül, Z., & Aytöre, C. Agent-Based Simulation Modeling For Covid-19 Vaccination Policies: Single-Dose And DoubleDose Applications.
  • Gumzej, R. (2021). Use Case: E-Marketplace Regulation. In Intelligent Logistics Systems for Smart Cities and Communities (pp. 149-162). Springer, Cham.
  • Gunaratne, C., & Garibay, I. (2021). NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces. SoftwareX, 16, 100801.
  • Guo, L., Li, Y., & Sheng, D. (2021). Modeling and Simulating Online Panic in an Epidemic Complexity System: An Agent-Based Approach. Complexity, 2021.
  • Grajdura, S. A., Borjigin, S. G., & Niemeier, D. A. (2020, November). Agent-based wildfire evacuation with spatial simulation: a case study. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (pp. 56-59).
  • Gravel-Miguel, C., Murray, J. K., Schoville, B. J., Wren, C. D., & Marean, C. W. (2021). Exploring variability in lithic armature discard in the archaeological record. Journal of Human Evolution, 155, 102981.
  • Grillo, C. A., Holford, M., & Walter, N. G. (2021). From Flatland to Jupiter: Searching for Rules of Interaction Across Biological Scales. Integrative and Comparative Biology.
  • Grizioti, M., & Kynigos, C. (2021, June). Children as players, modders, and creators of simulation games: A design for making sense of complex real-world problems: Children as players, modders and creators of simulation games. In Interaction Design and Children (pp. 363-374).
  • Gunaratne, C., & Garibay, I. (2021). NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces. SoftwareX, 16, 100801.
  • Gürsoy, F., & Badur, B. (2021). An Agent-Based Modelling Approach to Brain Drain. arXiv preprint arXiv:2103.03234.
  • Habib, L., Pacaux-Lemoine, M. P., Berdal, Q., & Trentesaux, D. (2021). From Human-Human to Human-Machine Cooperation in Manufacturing 4.0. Processes, 9(11), 1910.
  • Haddad, T. A., Hedjazi, D., & Aouag, S. (2021). An IoT-Based Adaptive Traffic Light Control Algorithm for Isolated Intersection. In Advances in Computing Systems and Applications: Proceedings of the 4th Conference on Computing Systems and Applications (pp. 107-117). Springer International Publishing.
  • Hakim, G., & Braun, R. (2021, December). Wireless Sensor Network Routing for Energy Efficiency. In International Conference On Systems Engineering (pp. 329-343). Springer, Cham.
  • Hammock, J., & Goel, A. (2021). Recognizing Novice Learner’s Modeling Behaviors. In Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (p. 189). Springer Nature.
  • Han, X. (2020, December). Influence of exits and evacuees on evacuation efficiency. In IOP Conference Series: Earth and Environmental Science (Vol. 608, No. 1, p. 012031). IOP Publishing.
  • Hanneman, R. A Computational Model of Worker Protest. Journal of Artificial Societies and Social Simulation, 14(3), 1.
  • Hansen, H. H., Korevaar, G., & Lukszo, Z. (2021). The effect of group decisions in heat transitions: An agent-based approach. Energy Policy, 156, 112306.
  • Haghpanah, F., Ghobadi, K., & Schafer, B. W. (2021). Multi-hazard hospital evacuation planning during disease outbreaks using agent-based modeling. International Journal of Disaster Risk Reduction, 102632.
  • Haghpanah, F., Schafer, B. W., & Castro, S. (2021). Application of Bug Navigation Algorithms for Large-Scale Agent-Based Evacuation Modeling to Support Decision Making. Fire Safety Journal, 103322.
  • Haque, G. (2021). Expanding an Agent Based Model to Simulate SARS-CoV-2 Spread in Places of Worship.
  • Harel, D., & Marron, A. (2021, October). Introducing Dynamical Systems andChaos Early in Computer Science andSoftware Engineering Education Can Help Advance Theory and Practice ofSoftware Development and Computing. In International Symposium on Leveraging Applications of Formal Methods (pp. 322-334). Springer, Cham.
  • Harwick, C. (2021). Helipad: A Framework for Agent-Based Modeling in Python. Available at SSRN.
  • Hassannezhad, M., Gogarty, M., O’Connor, C. H., Cox, J., Meier, P. S., & Purshouse, R. C. (2021). A Cybernetic Participatory Approach for Whole-Systems Modelling and Analysis, with Application to Inclusive Economies.
  • Hasanpour, S., & Rassafi, A. A. (2020). Pedestrian Movement Simulation in Evacuation Process from a Dynamic Environment using Agent-Based Modeling. Quarterly Journal of Transportation Engineering, 12(2), 357-376.
  • Hassanpour, S., & Rassafi, A. A. (2021). Agent-Based Simulation for Pedestrian Evacuation Behaviour Using the Affordance Concept. KSCE Journal of Civil Engineering, 1-13.
  • Hassanpour, S., Rassafi, A. A., Gonzalez, V., & Liu, J. (2021). A hierarchical agent-based approach to simulate a dynamic decision-making process of evacuees using reinforcement learning. Journal of Choice Modelling, 100288.
  • Hbaieb, A., Ayed, S., & Chaari, L. (2021). A survey of trust management in the Internet of Vehicles. Computer Networks, 108558.
  • He, C., Jia, G., McCabe, B., Chen, Y., Zhang, P., & Sun, J. (2021). Psychological decision-making process of construction worker safety behavior: an agent-based simulation approach. International journal of occupational safety and ergonomics, (just-accepted), 1-27.
  • He, Z., Huang, D., & Fang, J. (2021). Social Stability Risk Diffusion of Large Complex Engineering Projects Based on an Improved SIR Model: A Simulation Research on Complex Networks. Complexity, 2021.
  • Hendriks, F., Distel, B., Engelke, K. M., Westmattelmann, D., & Wintterlin, F. (2021). Methodological and Practical Challenges of Interdisciplinary Trust Research. In Trust and Communication (pp. 29-57). Springer, Cham.
  • Hentati, A. I., & Fourati, L. C. A Convoy of Ground Mobile Vehicles Protection using Cooperative UAVs-based System. In 2021 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.
  • Hernández, P., Pena, C., Ramos, A., & Gómez-Cadenas, J. J. (2021). A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times. Plos one, 16(2), e0244107.
  • Herrera, D. Y. M., Barrientos, A. H., & Escobar, O. Z. (2021). A review of agent-based modeling for simulation of agricultural systems. DYNA, 88(217), 103-110.
  • Hervey, S. D., Rutledge, L. Y., Patterson, B. R., Romanski, M. C., Vucetich, J. A., Belant, J. L. & Brzeski, K. E. (2021). A first genetic assessment of the newly introduced Isle Royale gray wolves (Canis lupus). Conservation Genetics, 1-14.
  • Hižak, J. (2021). Iterated Prisoner's Dilemma among mobile agents performing 2D random walk. Croatian Operational Research Review, 12(2), 161-174.
  • Hjorth, A. (2021, August). NaturalLanguageProcesing4All: -A Constructionist NLP tool for Scaffolding Students’ Exploration of Text. In Proceedings of the 17th ACM Conference on International Computing Education Research (pp. 347-354).
  • Hjorth, A., Hansen, I. B., & Sherson, J. (2021). OptimizerSpace: A CSCL Tool for Search and Optimization. In Proceedings of the 14th International Conference on Computer-Supported Collaborative Learning-CSCL 2021. International Society of the Learning Sciences.
  • Hoffenson, S., & Fay, B. (2021). Teaching Market-Driven Engineering Design with an Agent-Based Simulation Tool. Advances in Engineering Education, 9(2), n2.
  • Høholt, M., Graungaard, D., Bouvin, N. O., Petersen, M. G., & Eriksson, E. (2021). Towards a model of progression in computational empowerment in education. International Journal of Child-Computer Interaction, 100302.
  • Hölzchen, E., Hertler, C., Mateos, A., Rodríguez, J., Berndt, J. O., & Timm, I. J. (2021). Discovering the opposite shore: How did hominins cross sea straits?. PloS one, 16(6), e0252885.
  • Hong, J., & Chun, J. (2021). Analysis of Designer Brands Aiming for the Value of Slow Fashion-Focused on John Alexander Skelton and Geoffrey B. Small. Journal of the Korean Society of Clothing and Textiles, 45(1), 136-154.
  • Hosseini, S., & Zandvakili, A. (2021). The SEIRS-C Model of Information diffusion Based on Rumor spreading with Fuzzy Logic in Social Networks. International Journal of Computer Mathematics, (just-accepted), 1-30.
  • Huang, J., & Nouri, B. (2021). An Agent-Based Model to Evaluate the Effect of Socioeconomic Status and Demographic Factors on COVID-19 Prevalence and Mortality.
  • Huang, S., Potter, A., Eyers, D., & Li, Q. (2021). The influence of online review adoption on the profitability of capacitated supply chains. Omega, 102501.
  • Huang, W., Yuan, B., Wang, S., & Zhang, X. (2020, December). Research on Simulation of Network Attack and Defense situation based on Evolutionary Game. In 2020 The 9th International Conference on Networks, Communication and Computing (pp. 96-103).
  • Huang, Y., Karabiyik, T., Madamanchi, A., & Magana, A. J. (2021). An Agent-Based Modeling Approach for Informing the U.S. Plastic Waste Management Process. SIMUL 2021 : The Thirteenth International Conference on Advances in System Simulation, 65–71.
  • Hudec, O., Gazda, V., Zoričák, M., & Horváth, D. (2021). 20. Industrial districts as the outcome of self-organisation in time and space. Handbook on Entropy, Complexity and Spatial Dynamics: A Rebirth of Theory?, 342.
  • Hunter, E., & Kelleher, J. D. (2021). Adapting an Agent-Based Model of Infectious Disease Spread in an Irish County to COVID-19. Systems, 9(2), 41.
  • Husarek, D., Paulus, S., Metzger, M., Salapic, V., & Niessen, S. (2021). Modeling Charging Infrastructure Requirements to Achieve a Holistic E-Mobility Integration in Regional Energy Systems.
  • Husnain M, & Shafi, N. (2021) An Extension to Wolf Sheep Predation (Docked Hybrid) Agent Based Model in NetLogo. Journal of Software Engineering and Intelligent Systems 6(1).
  • Hwang, Y., & Heo, G. (2021). Development of a Radiological Emergency Evacuation Model Using Agent-Based Modeling. Nuclear Engineering and Technology.
  • Ibbotson, P., Jimenez-Romero, C., & Page, K. M. (2021). Dying to cooperate: the role of environmental harshness in human collaboration. Behavioral Ecology.
  • Ibrahim, M., Hashmi, U. S., Nabeel, M., Imran, A., & Ekin, S. (2021). Embracing Complexity: Agent-based Modeling for HetNets Design and Optimization via Concurrent Reinforcement Learning Algorithms. IEEE Transactions on Network and Service Management.
  • Imirzian, N., & Hughes, D. P. (2021). An agent-based model shows zombie ants exhibit search behavior. Journal of Theoretical Biology, 110789.
  • Innes-Gold, A., Pavlowich, T., Heinichen, M., McManus, M. C., McNamee, J., Collie, J., & Humphries, A. (2021). Exploring social-ecological trade-offs in fisheries using a coupled food web and human behavior model. Ecology and Society, 26(2).
  • Innocenti, E., Detotto, C., Idda, C., Parker, D. C., & Prunetti, D. (2020). An iterative process to construct an interdisciplinary ABM using MR POTATOHEAD: An application to Housing Market Models in touristic areas. Ecological Complexity, 44, 100882.
  • Inturri, G., Giuffrida, N., Ignaccolo, M., Le Pira, M., Pluchino, A., Rapisarda, A., & D'Angelo, R. (2021). Taxi vs. demand responsive shared transport systems: an agent-based simulation approach. Transport Policy.
  • Ionescu, Ș., Nica, I., & Chiriță, N. (2021). Cybernetics approach using agent-based modeling in the process of evacuating educational institutions in case of disasters. Sustainability, 13(18), 10277.
  • Jacobson, M. & Wilensky, U. (2021). Complex systems and the learning sciences: Educational, theoretical, and methodological implications. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (3rd Edition). Cambridge, UK: Cambridge University Press.
  • Jager, W. (2021). Using Agent Based Modelling to Explore Behavioural Dynamics Affecting our Climate. Current Opinion in Psychology.
  • Jahanbani, M., Vahidnia, M. H., & Aspanani, M. Planning to explore lime minerals using spectral angle mapper (SAM) processing and agent-based modeling (ABM). Journal of Geomatics Science and Technology, 0-0.
  • Jani, A. (2021). An agent-based model of repeated decision making under risk: modeling the role of alternate reference points and risk behavior on long-run outcomes. Journal of Business Economics, 1-27.
  • Jansens, R., Kingston, M., Morrison, B., Dubey, M., & Guerin, S. (2021). SIN (language).
  • Janssen, S. D., Viaene, K. P., Van Sprang, P., & De Schamphelaere, K. A. (2021). Integrating Bioavailability of Metals in Fish Population Models. Environmental Toxicology and Chemistry.
  • Jaramillo, D., Anderson, A., & Edington, C. (2021). Epidemiology model of Covid-19.
  • Jensen, M., Dignum, F., Vanhée, L., Păstrăv, C., & Verhagen, H. (2021). Agile Social Simulations for Resilience. In Social Simulation for a Crisis (pp. 379-408). Springer, Cham.
  • Jensen, M., Lorig, F., Vanhée, L., & Dignum, F. (2021). Deployment and Effects of an App for Tracking and Tracing Contacts During the COVID-19 Crisis. In Social Simulation for a Crisis (pp. 167-188). Springer, Cham.
  • Jensen, M., Vanhée, L., & Kammler, C. (2021). Social Simulations for Crises: From Theories to Implementation. In Social Simulation for a Crisis (pp. 39-84). Springer, Cham.
  • Jiang, H., Chen, C., Zhao, S., & Wu, Y. (2020). Evolution of a technology standard alliance based on an echo model developed through complex adaptive system theory. Complexity, 2020.
  • Jiang, L. (2021). Racial and Ethnic Disparities in Breastfeeding Practices and the Impact of Interventions in a Low-Income Population in Los Angeles County (Doctoral dissertation, UCLA).
  • Jiménez, A. F., Cárdenas, P. F., & Jiménez, F. (2022). Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales. Computers and Electronics in Agriculture, 192, 106635.
  • Jin, K. S., Lee, S. M., & Kim, Y. C. (2021). Adaptive and optimized agent placement scheme for parallel agent‐based simulation. ETRI Journal.
  • Jing, T., Meng, Q. H., & Ishida, H. (2021). Recent Progress and Trend of Robot Odor S.
  • Jiping, C., Lijie, C., Gang, D., & Bo, R. (2021). Simulation on Aviation Maintenance Support System Based on Goal-driven. Journal of System Simulation, 33(9), 2157.
  • Jo, C., Kim, D. H., & Lee, J. W. (2021). Sustainability of religious communities. Plos one, 16(5), e0250718.
  • Jungck, J. (2021). BIOLOGICAL MODELS FOR FINITE MATHEMATICS. PRIMUS, 1-43.
  • Kanters, H., Brughmans, T., & Romanowska, I. (2021). Sensitivity analysis in archaeological simulation: An application to the MERCURY model. Journal of Archaeological Science: Reports, 38, 102974.
  • Karchevskyi, M., & Karchevskaya, H. (2021, March). Agent-Based Modeling as a Method of Crime Research. In International Conference on Economics, Law and Education Research (ELER 2021) (pp. 117-121). Atlantis Press.
  • Kart, Ö., Genç, O. Ç., & Başçiftçi, F. (2021). Speed Compatible Green Wave Coridor with Internet of Objects (No. 6618). EasyChair.
  • Karyawati, A. E. (2021). Scenario modelling as planning evidence to improve access to emergency obstetric care in eastern Indonesia. In The Modal Future: A Theory of Future-Directed Thought and Talk (pp. 278-290). Cambridge: Cambridge University Press.
  • Kaspersen, M. H., Graungaard, D., Bouvin, N. O., Petersen, M. G., & Eriksson, E. (2021). Towards a model of progression in computational empowerment in education. International Journal of Child - Computer Interaction, 29, 100302.
  • Katan, J., & Perez, L. (2021). ABWiSE v1. 0: Toward and Agent-Based Approach to Simulating Wildfire Spread. Natural Hazards and Earth System Sciences Discussions, 1-27.
  • Katerndahl, D., Burge, S. K., & del Pilar Montanez Villacampa, M. (2021). Modeling Daily Partner Violence and Substance Use Based upon Couple’s Reporting. Journal of Interpersonal Violence, 08862605211050108.
  • Kato, D., Yada, S., & Kurahashi, S. (2021). Analysis of Factory Automated Guided Vehicles Systems Using Contract Net Protocol. In Agents and Multi-Agent Systems: Technologies and Applications 2021 (pp. 511-519). Springer, Singapore.
  • Kato, J. S., & Sbicca, A. (2021). Bounded Rationality, Group Formation and the Emergence of Trust: An Agent-Based Economic Model. Computational Economics, 1-29.
  • Kaufman, M., & Yuthas, K. (2021) Learning Analytics and Technology Through TeachingLearning Analytics through Teaching. Journal of Emerging Technologies in Accounting.
  • Kaur, S. (2021). A Framework to Study the Impact of Interventions on Social Isolation During Pandemics Using Multi-Agent Simulation (Doctoral dissertation, University of Windsor (Canada)).
  • Kautz, F., & Mallick, R. B. (2021). Simulation-Based Stochastic Method to Model Microcrack Coalescence in Asphalt Pavements: Concept Paper. Journal of Transportation Engineering, Part B: Pavements, 147(4), 06021003.
  • Ke, L., Sadler, T. D., Zangori, L., & Friedrichsen, P. J. (2021). Developing and Using Multiple Models to Promote Scientific Literacy in the Context of Socio-Scientific Issues. Science & Education, 1-19.
  • Kelter, J., Bugler, A., & Wilensky, U. (2021). Agent-Based Models of Quadratic Voting. In Z. Yang, & E. von Briesen (Eds.), Proceedings of the 2020 Conference of The Computational Social Science Society of the Americas (pp. 131-142). (Springer Proceedings in Complexity). Springer Science and Business Media B.V. https://doi.org/10.1007/978-3-030-83418-0_8.
  • Kelter, J. Z., Emery, J. D., & Wilensky, U. (2021). A Multi-level Diffusion Unit: Connecting Submicro- and Macro-levels with Computational, Graphical, and Mathematical Representations. 2021 ASEE Virtual Annual Conference.
  • Kelter, J., Peel, A., Bain, C., Anton, G., Dabholkar, S., Horn, M. S., & Wilensky, U. (2021). Constructionist co‐design: A dual approach to curriculum and professional development. British Journal of Educational Technology, 1043–1059. https://doi.org/10.1111/bjet.13084.
  • Khaddage, F., & Lattemann, C. (2021, July). Artificial Intelligence and Cloud-based Technologies to Empower Learning “Active Experiments via NetLogo”. In EdMedia+ Innovate Learning (pp. 15-21). Association for the Advancement of Computing in Education (AACE).
  • Khalil, H., & Wainer, G. CD2: An Automation Tool for Cell-DEVS CO2 Diffusion Models. Proc. SimAud2021.
  • Khan, I., & Cañamero, L. (2021, July). Adaptation-By-Proxy: Contagion Effect of Social Buffering in an Artificial Society. In ALIFE 2021: The 2021 Conference on Artificial Life. MIT Press.
  • Kianpour, M. (2021). Heterogeneous Preferences and Patterns of Contribution in Cybersecurity as a Public Good.
  • Kilkis, S., Prakasha, P. S., Naeem, N., & Nagel, B. (2021). A Python Modelling and Simulation Toolkit for Rapid Development of System of Systems Inverse Design (SoSID) Case Studies. In AIAA Aviation 2021 Forum (p. 3000).
  • Kilty, T. J., & Burrows, A. C. (2021). Secondary Science Preservice Teachers: Technology Integration in Methods and Residency. Journal of Science Teacher Education, 1-23.
  • Kim, S., Feng, B., Smith, K., Masoud, S., Zheng, Z., Szabo, C., & Loper, M. (2021). Toward Better Management of Potentially Hostile Crowds. In Proceedings of the 2021 Winter Simulation Conference (WSC).
  • Kim, W. (2021). Does START-UP NY Promote Firm Formation?. Nakhara: Journal of Environmental Design and Planning, 20, 105-105.
  • Kizhakkedath, A., & Tai, K. (2021). Vulnerability Analysis of Critical Infrastructure Network. International Journal of Critical Infrastructure Protection, 100472.
  • Klein, D., & Marx, J. Die epistemische Qualität demokratischer Entscheidungsverfahren. Interaktionseffekte zwischen eigennützigen, individuellen Überzeugungen und der epistemischen Qualität kollektiver Entscheidungen. Demokratie und Wahrheit.
  • Klopfer, E. (2021). The Complex Evolution of Technologies and Pedagogies for Learning about Complex Systems.
  • Knowe, M., & Gresalfi, M. (2021). Bridging the Divide: Exploring Affordances for Interdisciplinary Learning. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Kooshknow, A. R. M. M., Herber, R., & Ruzzenenti, F. (2021). Is electricity storage systems in the Netherlands indispensable or doable? Testing electricity storage business models with exploratory agent-based modeling. arXiv preprint arXiv:2112.11035.
  • Koppelaar, R., Marvuglia, A., & Rugani, B. (2021). Water Runoff and Catchment Improvement by Nature-Based Solution (NBS) Promotion in Private Household Gardens: An Agent-Based Model. In Rethinking Sustainability Towards a Regenerative Economy (pp. 91-114). Springer, Cham.
  • Koralewski, T. E., Wang, H. H., Grant, W. E., Brewer, M. J., Elliott, N. C., & Westbrook, J. K. (2021). Modeling the dispersal of wind-borne pests: Sensitivity of infestation forecasts to uncertainty in parameterization of long-distance airborne dispersal. Agricultural and Forest Meteorology, 301, 108357.
  • Kořínek, M., Tázlar, O., & Štekerová, K. (2021). Digital Twin Models: BIM Meets NetLogo.
  • Koshy-Chenthittayil, S., Archambault, L., Senthilkumar, D., Laubenbacher, R., Mendes, P., & Dongari-Bagtzoglou, A. (2021). Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review. Microorganisms, 9(2), 417.
  • Kostiou, V., Hall, M. W., Jones, P. H., & Hall, B. A. (2021). Simulations reveal that different responses to cell crowding determine the expansion of p53 and Notch mutant clones in squamous epithelia. Journal of the Royal Society Interface, 18(183), 20210607.
  • Kowarsch, D., & Yang, Z. (2021, July). The Impact of Housing Programs on Unsheltered Homeless Population: An Agent-Based Approach. In International Conference on Applied Human Factors and Ergonomics (pp. 84-92). Springer, Cham.
  • Král, B. J. Model komplexního systému: simulace šíření kůrovce.
  • Kurniawan, A. C., & Arvitrida, N. I. (2021). An agent-based simulation for a trade-off between frequency and depth in retail price promotion strategy. Management & Marketing, 16(1), 1-12.
  • Kussmaul, C., & Pirmann, T. (2021). Guided Inquiry Learning with Technology: Investigations to Support Social Constructivism. In CSEDU (1) (pp. 483-490).
  • Kwon, H. (2021). Refining Behavioural Theories and Rules in Agent-Based Models to Enhance Dynamic Simulation of Urban Change (Doctoral dissertation, University of Cambridge).
  • LaBarbera, K., & Scullen, J. C. (2021). Using individual capture data to reveal large-scale patterns of social association in birds. Journal of Ornithology, 1-17.
  • Lade, S. J., Anderies, J. M., Currie, P., & Rocha, J. C. Dynamical systems modelling. (2021). The Routledge Handbook of Research Methods for Social-Ecological Systems, 359.
  • Laguna-Sánchez, G. A., & López-Sauceda, J. (2021). Modelo heurístico, para la dinámica de propagación de una enfermedad infecciosa. Contactos, Revista de Educación en Ciencias e Ingeniería, (119), 45-55.
  • Lal, C., & Marijan, D. (2021). Blockchain Testing: Challenges, Techniques, and Research Directions. arXiv preprint arXiv:2103.10074.
  • Lane, J. E., McCaffree, K., & Shults, F. L. (2021). Is radicalization reinforced by social media censorship?. arXiv preprint arXiv:2103.12842.
  • Lange, K. P., Korevaar, G., Oskam, I. F., Nikolic, I., & Herder, P. M. (2021). Agent-based Modelling and Simulation for Circular Business Model Experimentation. Resources, Conservation & Recycling Advances, 200055.
  • Langellier, B. A., Stankov, I., Hammond, R. A., Bilal, U., Auchincloss, A. H., Barrientos, T., ... & Roux, A. V. D. Potential impacts of policies to reduce purchasing of ultra-processed foods in Mexico at different stages of the social transition: an agent-based modeling approach. Public Health Nutrition, 1-24.
  • Larrain, N., & Groene, O. (2021). Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection. PloS one, 16(7), e0254334.
  • Latif, R., Ahmed, M. U., Tahir, S., Latif, S., Iqbal, W., & Ahmad, A. (2021). A novel trust management model for edge computing. Complex & Intelligent Systems, 1-17.
  • Le, N. T. T., Nguyen, P. A. H. C., & Hanachi, C. (2021, September). Agent-Based Modeling and Simulation of Citizens Sheltering During a Tsunami: Application to Da Nang City in Vietnam. In International Conference on Computational Collective Intelligence (pp. 199-211). Springer, Cham.
  • Lemanski, N., Silk, M., Fefferman, N., & Udiani, O. (2021). How territoriality reduces disease transmission among social insect colonies. Behavioral Ecology and Sociobiology, 75(12), 1-13.
  • Leoni, S. (2021). An Agent-Based Model for Tertiary Educational Choices in Italy. Research in Higher Education, 1-28.
  • Lermanda Sandoval, M. O. (2021). Modelo de sistema de detección de intrusos en red basado en especies indicadoras artificiales. (Thesis).
  • Levy, B., Windoloski, K., & Ludlam, J. (2021). Matrix and agent-based modeling of threats to a diamond-backed terrapin population. Mathematical Biosciences, 108672.
  • Levy, M., Peel, A., Dabholkar, S., Zhao, L., Juhl, S., Levites, L., Mills, J., Wu, S., Horn, M.S., & Wilensky, U.(2021). Co-Designing to Understanding Equity-Focus in Computational Thinking (CT) Integrated Science Curricula. Paper presented to the 2021 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Levy, M., Wu, S. P. W., Dabholkar, S., Horn, M. S., & Wilensky, U. (2021). Teachers' Sensemaking of CT Integration and Pedagogical Approaches. In Dabholkar S. (Symposium organizer), Integrating Computational Thinking in Science Curricula: Professional Development and Student Assessment. Presented at the 2021 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Lezia, A., Miano, A., & Hasty, J. (2021). Synthetic Gene Circuits: Design, Implement, and Apply. Proceedings of the IEEE.
  • Li, D., Li, C., & Gu, R. (2021). Evolutionary Game Analysis of Promoting Industrial Internet Platforms to Empower Manufacturing SMEs through Value Cocreation Cooperation. Discrete Dynamics in Nature and Society, 2021.
  • Li, H., Li, C., & Wang, Z. (2021). An agent-based model for exploring the impacts of reciprocal trust on knowledge transfer within an organization. Journal of Business & Industrial Marketing.
  • Li, K., Liu, Y., Wan, H., & Huang, Y. (2021). A discrete-event simulation model for the Bitcoin blockchain network with strategic miners and mining pool managers. Computers & Operations Research, 105365.
  • Li, L., Wang, J., Zhong, X., Lin, J., Wu, N., Zhang, Z., ... & Zhao, Y. (2022). Combined multi-objective optimization and agent-based modeling for a 100% renewable island energy system considering power-to-gas technology and extreme weather conditions. Applied Energy, 308, 118376.
  • Li, W., Cao, S., Hu, K., Cao, J., & Buyya, R. (2021). Blockchain-Enhanced Fair Task Scheduling for Cloud-Fog-Edge Coordination Environments: Model and Algorithm. Security and Communication Networks, 2021.
  • Li, Z., Pradena Miquel, M., & Pinacho-Davidson, P. (2022). Safety-Centric and Smart Outdoor Workplace: A New Research Direction and Its Technical Challenges. In Smart Trends in Computing and Communications (pp. 61-74). Springer, Singapore.
  • Lin, S. Y., Hlynka, A. W., Xu, L., Lu, H., Sediek, O. A., El-Tawil, S., ... & Aguirre, B. (2021). Simple Run-Time Infrastructure (SRTI): An accessible distributed computing platform for interdisciplinary simulation. Journal of Computational Science, 101455.
  • Liermann, V., & Dittmar, H. (2021). BSDS—Balance Sheet Dynamics Simulator (Application ABM). In The Digital Journey of Banking and Insurance, Volume I (pp. 137-159). Palgrave Macmillan, Cham.
  • Liu, C., Bhullar, M. S., Kaur, T., Kumar, J., Reddy, S. R. S., Singh, M., & Kaundun, S. S. (2021). Modelling the Effect and Variability of Integrated Weed Management of Phalaris minor in Rice-Wheat Cropping Systems in Northern India. Agronomy, 11(11), 2331.
  • Liu, H., Wu, M., Liu, X., Gao, J., Luo, X., & Wu, Y. (2021). Simulation of Policy Tools’ Effects on Farmers’ Adoption of Conservation Tillage Technology: An Empirical Analysis in China. Land, 10(10), 1075.
  • Liu, J., & Stacey, P. (2021). Modelling the effects of lockdown and social distancing in the management of the Global Coronavirus Crisis-Why the UK tier system failed.
  • Liu, W., & Agusdinata, D. B. (2021). Dynamics of local impacts in low-carbon transition: Agent-based modeling of lithium mining-community-aquifer interactions in Salar de Atacama, Chile. The Extractive Industries and Society, 100927.
  • Liu, Y., Gao, H., Cai, J., Lu, Y., & Fan, Z. (2021). Urbanization path, housing price and land finance: International experience and China’s facts. Land Use Policy, 105866.
  • Liu, Y., Yang, D., Timmermans, H. J., & de Vries, B. (2021). Simulating the effects of redesigned street-scale built environments on access/egress pedestrian flows to metro stations. Computational Urban Science, 1(1), 1-14.
  • Lloyd, S. J., & Chalabi, Z. (2021). Climate change, hunger and rural health through the lens of farming styles: An agent-based model to assess the potential role of peasant farming. Plos one, 16(2), e0246788.
  • López-Ortiz, E. J., Sancho-Caparrini, F., Martínez-del-Amor, M. Á., Soria-Morillo, L. M., & Álvarez-García, J. A. (2021). Hybrid agent-based methodology for testing response protocols. Knowledge-Based Systems, 107005.
  • Lorenz, F., & Jeyapragasan, G. (2020). The impact of climate change on tri-trophic interactions and crop production. The iScientist, 5(1), 4-12.
  • Lorenz, J. (2021). Epistemology of agent-based modeling. Handbook of Computational Social Science, Volume 1: Theory, Case Studies and Ethics, 13.
  • Lorig, F., Jensen, M., Kammler, C., Davidsson, P., & Verhagen, H. (2021). Comparative Validation of Simulation Models for the COVID-19 Crisis. In Social Simulation for a Crisis (pp. 331-352). Springer, Cham.
  • Love, C., Gresalfi, M., & Knowe, M. (2021). The Tragedy of Lost Ideas: Examining Epistemic Injustice in Pair Programming. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Lovellette, E., & Hexmoor, H. (2021). Lane and Speed Allocation Mechanism for Autonomous Vehicle Agents on a Multi-Lane Highway. Internet of Things, 100356.
  • Lu, P., & Chen, D. (2021). The life cycle model of Chinese empire dynamics (221 BC–1912 AD). The Journal of Mathematical Sociology, 1-37.
  • Lu, P., & Chena, D. (2021). The Life Cycle Model and Empire Dynamics of China.
  • Lu, P., Yang, H., Li, H., Li, M., & Zhang, Z. (2021). Swarm intelligence, social force and multi-agent modeling of heroic altruism behaviors under collective risks. Knowledge-Based Systems, 106725.
  • Lu, P., Yang, H., Li, M., & Zhang, Z. (2021). The sandpile model and empire dynamics. Chaos, Solitons & Fractals, 143, 110615.
  • Lu, P., Zhang, Z., & Li, M. (2021). Big data-drive agent-based modeling of online polarized opinions. Complex & Intelligent Systems, 1-18.
  • Lu, S., Wang, W., Cheng, Y., Yang, C., Jiao, Y., Xu, M., ... & Xu, J. (2021). Food-trade-associated COVID-19 outbreak from a contaminated wholesale food supermarket in Beijing. Journal of Biosafety and Biosecurity.
  • Lu, P., Wen, F., Li, Y., & Chen, D. (2021). Multi-agent modeling of crowd dynamics under mass shooting cases. Chaos, Solitons & Fractals, 153, 111513.
  • Lucherini, E., Sun, M., Winecoff, A., & Narayanan, A. (2021). T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems. arXiv preprint arXiv:2107.08959.
  • Lwin, T., Koike, T., & Dang, J. (2021). A RATIONALIZED SEISMIC DESIGN METHOD FOR BUILDINGS IN EARTHQUAKE-PRONE DEVELOPING COUNTRIES. ASEAN Engineering Journal, 11(4), 266-279.
  • Mabey, C. S., Armstrong, A. G., Mattson, C. A., Salmon, J. L., Hatch, N. W., & Dahlin, E. C. (2021). A computational simulation-based framework for estimating potential product impact during product design. Design Science, 7.
  • Maclay, G. J., & Ahmad, M. (2021). An agent based force vector model of social influence that predicts strong polarization in a connected world. Plos one, 16(11), e0259625.
  • Madamba, T. (2021). Simulating The Effects Of Cross-Contamination Of Escherichia Coli O157: H7 On Fresh-Cut Lettuce During Post-Harvest Processing From An Agent Based Perspective (Doctoral dissertation).
  • Madeira, L. M., Furtado, B. A., & Dill, A. R. (2021). VIDA: A simulation model of domestic VIolence in times of social DistAncing. arXiv preprint arXiv:2101.04057.
  • Mahfooz Ul Haque, H., Saleem, K., & Salman Khan, A. Modeling belief‐desire‐intention reasoning agents for situation‐aware formalisms. Concurrency and Computation: Practice and Experience, e6417.
  • Mahmud, S., Asadi, A., LaCrue, A. R., Akter, T., Hernandez, S., & Pinkley, S. N. (2021). A hybrid agent-based simulation and optimization approach for statewide truck parking capacity expansion. Procedia Computer Science, 184, 33-41.
  • Mahon, C. L., & Pelech, S. (2021). Guidance for analytical methods to cumulative effects assessment for terrestrial species. Environmental Reviews, 29(999), 1-24.
  • Maier-Speredelozzi, V., & Still, B. (2021). Robust Project-Based Organizations for the Design of Complex Engineered Systems. Project Management Journal, 87569728211014369.
  • Maiwald, J., & Schuette, T. (2021). Decentralised Electricity Markets and Proactive Customer Behaviour. Energies 2021, 14, 781.
  • Majid, M. H. A., Arshad, M. R., & Mokhtar, R. M. (2022). Swarm Robotics Behaviors and Tasks: A Technical Review. Control Engineering in Robotics and Industrial Automation, 99-167.
  • Maldonado Castañeda, C. E., Acevedo-Supelano, A. L., Bustacara, M., González-Martínez, C. J., Trujillo Perdomo, J. F., Millán-Hernández, E. M., ... & Silva González, S. L. (2021). Modelamiento basado en agentes (MBA) en estudios de salud pública.
  • Mamboleo, A. A., Doscher, C., & Paterson, A. (2021). A computational modelling approach to human-elephant interactions in the Bunda District, Tanzania. Ecological Modelling, 443, 109449.
  • MANSOORİ, H., GHORBANİ, M., & KOHANSAL, M. R. (2021). Simulation the Effects of climate change and market prices on farm’s structure by using an agent based model. Journal of Agricultural Sciences.
  • Marín-Lora, C., Chover, M., & Sotoca, J. M. (2021, October). A Multi-agent Specification for the Tetris Game. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 169-178). Springer, Cham.
  • Marks, R. (2021). Thirty-Five Years of Computational Economics.
  • Marsay, K. S., Greaves, S., Mahabaleshwar, H., Ho, C. M., Roehl, H., Monk, P. N., ... & Partridge, L. J. (2021). Tetraspanin Cd9b and Cxcl12a/Cxcr4b have a synergistic effect on the control of collective cell migration. Plos one, 16(11), e0260372.
  • Martin, A. A., & Barnas, A. F. (2022). Sitting ducks: Strategies to increase recruitment in common eiders (Somateria mollissima) facing polar bear (Ursus maritimus) predation.
  • Martin, K. & Wilensky U. (2021). How do ants know what to do? Paper presented at the 2021 Annual Meeting of the American Education Research Association (AERA).
  • Martin, K., Horn, M., & Wilensky, U. (2021). Constructivist Dialogue Mapping: A Comparison of Museum Experience. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 1081-1082). Bochum, Germany: International Society of the Learning Sciences.
  • Martinez, I., Bruse, J. L., Florez-Tapia, A. M., Viles, E., & Olaizola, I. G. (2021). ArchABM: an agent-based simulator of human interaction with the built environment. $ CO_2 $ and viral load analysis for indoor air quality. arXiv preprint arXiv:2111.01484.
  • Maruthasalam, A. P. P., Roy, D., & Venkateshan, P. (2021). Modelling driver's reactive strategies in e-hailing platforms: an agent-based simulation model and an approximate analytical model. International Journal of Production Research, 1-19.
  • Marvuglia, A., Bayram, A., Baustert, P., Gutiérrez, T. N., & Igos, E. (2021). Agent-based modelling to simulate farmers’ sustainable decisions: Farmers’ interaction and resulting green consciousness evolution. Journal of Cleaner Production, 129847.
  • Marvuglia, A., Koppelaar, R., & Rugani, B. (2020). The effect of green roofs on the reduction of mortality due to heatwaves: Results from the application of a spatial microsimulation model to four European cities. Ecological Modelling, 438, 109351.
  • Maryasin, O. Y. (2021). Bee-Inspired Algorithm for Groups of Cyber-Physical Robotic Cleaners with Swarm Intelligence. Cyber-Physical Systems: Modelling and Intelligent Control, 167-177.
  • Maszczyk, P., Tałanda, J., Babkiewicz, E., Leniowski, K., & Urban, P. (2021). Daphnia depth selection in gradients of light intensity from different artificial sources: An evolutionary trap?. Limnology and Oceanography.
  • Matsunami, N., Okuhara, S., & Ito, T. (2021). Reward Design for Multi-Agent Reinforcement Learning with a Penalty Based on the Payment Mechanism. Transactions of the Japanese Society for Artificial Intelligence, 36(5), AG21-H_1.
  • Maturo, A., Petrucci, A., Forzano, C., Giuzio, G. F., Buonomano, A., & Athienitis, A. (2021). Design and environmental sustainability assessment of energy-independent communities: The case study of a livestock farm in the North of Italy. Energy Reports.
  • Maw, Y. Y., & Tun, M. T. (2021). SENSITIVITY ANALYSIS OF ANGLE, LENGTH AND BRIM HEIGHT OF THE DIFFUSER FOR THE SMALL DIFFUSER AUGMENTED WIND TURBIN. ASEAN Engineering Journal, 11(4), 280-291.
  • McKelvey, A. (2021). Learning and Integrating CALL Practices to Support English Language Learners: A Case Study of K-12 Classroom Teachers and Their Professional Development (Doctoral dissertation, University of Wyoming).
  • McVeigh, J., & MacLachlan, M. Psychological Governance and COVID-19: A Case Study in Macropsychology. In M. MacLachlan & J. McVeigh (Eds.), Macropsychology: A Population Science for Sustainable Development Goals, 303.
  • Mestari, M. (2021). Evolutionary Heuristic for Avoiding Traffic Jams in Road Network Using A* Search Algorithm. In Innovations in Smart Cities Applications Volume 4: The Proceedings of the 5th International Conference on Smart City Applications (p. 423). Springer Nature.
  • Metcalf, S. J., Reilly, J. M., Jeon, S., Wang, A., Pyers, A., Brennan, K., & Dede, C. (2021). Assessing computational thinking through the lenses of functionality and computational fluency. Computer Science Education, 1-25.
  • Meza, A., Ari, I., Al-Sada, M. S., & Koç, M. (2021). Future LNG competition and trade using an agent-based predictive model. Energy Strategy Reviews, 38, 100734.
  • Miller, B. W., & Frid, L. (2021). A new approach for representing agent-environment feedbacks: coupled agent-based and state-and-transition simulation models. Landscape Ecology, 1-16.
  • Miller, L. (2021). Consumer Preferences and Associated Price Premiums for Agricultural Traits in Maine Markets.
  • Miller Neilan, R., Majetic, G., Gil-Silva, M., Adke, A. P., Carrasquillo, Y., & Kolber, B. J. (2021). Agent-based modeling of the central amygdala and pain using cell-type specific physiological parameters. PLOS Computational Biology, 17(6), e1009097.
  • Milne, R. J., Cotfas, L. A., & Delcea, C. (2021). Minimizing health risks as a function of the number of airplane boarding groups. Transportmetrica B: Transport Dynamics, 1-22.
  • Minh, C. C., & Van Noi, N. (2021). Optimising truck arrival management and number of service gates at container terminals. Maritime Business Review.
  • Miri, F., & Pazzi, R. (2021). A Comprehensive Survey on the Convergence of Vehicular Social Networks and Fog Computing. arXiv preprint arXiv:2112.00143.
  • Mittal, A., Gibson, N. O., Krejci, C. C., & Marusak, A. A. (2021). Crowd-shipping for urban food rescue logistics. International Journal of Physical Distribution & Logistics Management.
  • Miyazawa, A., & Didier, A. (2021). Ana Cavalcanti Pedro Ribeiro.
  • Mohammed, S. J., & Hasson, S. T. (2021, April). A developed Clustering Approach to Model the Data Dissemination Types in a Highway. In 2021 1st Babylon International Conference on Information Technology and Science (BICITS) (pp. 332-336). IEEE.
  • Mohammadi, V., Rahmani, A. M., Darwesh, A., & Sahafi, A. (2021). Trust-based friend selection algorithm for navigability in social Internet of Things. Knowledge-Based Systems, 107479.
  • Mohammadiun, S., Hu, G., Gharahbagh, A. A., Li, J., Hewage, K., & Sadiq, R. (2021). Intelligent Computational Techniques in Marine Oil Spill Management: A Critical Review. Journal of Hazardous Materials, 126425.
  • Möller, R., Furnari, A., Battiato, S., Härmä, A., & Farinella, G. M. (2021). A Survey on Human-aware Robot Navigation. arXiv preprint arXiv:2106.11650.
  • Molin, L. D., Kanwal, J., & Stone, C. (2021, June). Resource availability and the evolution of cooperation in a 3D agent-based simulation. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 93-101).
  • Montagud, A., de León, M. P., & Valencia, A. (2021). Systems Biology at the giga-scale: large multi-scale models of complex, heterogeneous multicellular systems. Current Opinion in Systems Biology, 100385.
  • More, S., Bampidis, V., Benford, D., Bragard, C., Halldorsson, T., Hernández-Jerez, A., Bennekou, S. H., Koutsoumanis, K., Machera, K., Naegeli, H., Nielsen, S. S., Schlatter, J., Schrenk, D., Silano, V., Turck, D., Younes, M., Arnold, G., Dorne, J. L. Maggiore, A., ... Rortais, A. (2021). A systems-based approach to the environmental risk assessment of multiple stressors in honey bees.
  • Mostafizi, A., Koll, C., & Wang, H. (2021). A Decentralized and Coordinated Routing Algorithm for Connected and Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems.
  • Mota, F. P., Steffens, C. R., Adamatti, D. F., Botelho, S. S. D. C., & Rosa, V. (2021). A persuasive multi-agent simulator to improve electrical energy consumption. Journal of Simulation, 1-15.
  • Moškon, M., Komac, R., Zimic, N., & Mraz, M. (2021). Distributed biological computation: from oscillators, logic gates and switches to a multicellular processor and neural computing applications. Neural Computing and Applications, 1-16.
  • Moya, I., Bermejo, E., Chica, M., & Cordón, Ó. (2021). Coral reefs optimization algorithms for agent-based model calibration. Engineering Applications of Artificial Intelligence, 100, 104170.
  • Moya, I., Chica, M., Sáez-Lozano, J. L., & Cordón, Ó. (2021). Simulating the influence of terror management strategies on the voter ideological distance using agent-based modeling. Telematics and Informatics, 101656.
  • Mponela, P. (2021). Options for sustainable agricultural intensification in maize mixed farming systems.
  • Mudrak, G., & Semwal, S. K. (2021). Autonomous Vehicle Decision Making and Urban Infrastructure Optimization. In Intelligent Computing (pp. 1190-1202). Springer, Cham.
  • Mudrak, G., & Semwal, S. K. (2021, November). Using Agent Based Modeling to Frame Autonomous Vehicle Navigation as Complex Systems. In Proceedings of the Future Technologies Conference (pp. 154-172). Springer, Cham.
  • Mulyono, N. B., Pambudi, N. F., Ahmad, L. B., & Adhiutama, A. (2021). Determining response time factors of emergency medical services during the COVID-19 pandemic. International Journal of Emergency Services.
  • Mumford, E. (2021). Marginalized Indigenous Knowledge and Contemporary Swedish Colonialism: The Case of Reindeer Husbandry in Gällivare Forest Sámi Community.
  • Munthali, K. G. (2021). Analysing Road Traffic Situation in Lilongwe: An Agent Based Modelling (ABM) Approach. Advanced Journal of Graduate Research, 10(1), 3-15.
  • Murukutla, S. A., Koushik, S. B., Chinthala, S. P. R., Bobbillapati, A., & Kandaswamy, S. (2021, October). A Simple Agent Based Modeling Tool for Plastic and Debris Tracking in Oceans. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 139-150). Springer, Cham.
  • Musaeus, L. H., & Musaeus, P. (2021, June). Computing and Gestures in High School Biology Education. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 533-539).
  • Mysterud, A., Viljugrein, H., Rolandsen, C. M., & Belsare, A. V. (2021). Harvest strategies for the elimination of low prevalence wildlife diseases. Royal Society Open Science, 8(3), 210124.
  • Nadimi, N., & Eshlaghi, A. T. (2021). Hybrid of System Dynamics-Agent Based Analysis of Mobile Operators Revenue The Case: Digital Service Entry of MCCI Company. Journal of Industrial Management Studies, 19(60), 51-84.
  • Nagaraj, R. K., & D’Souza, M. (2021). A verifiable multi-agent framework for dependable and adaptable avionics. Sādhanā, 46(1), 1-27.
  • Nagasawa, R., Mas, E., Moya, L., & Koshimura, S. (2021). Model-based analysis of multi-UAV path planning for surveying postdisaster building damage. Scientific Reports, 11(1), 1-14.
  • Nakanishi, H., Han, W., Muminovic, M., & Qu, T. (2021). An Agent-Based Bushfire Visualisation to Support Urban Planning: A Case Study of the South Coast, NSW 2019–2020. In Urban Informatics and Future Cities (pp. 371-386). Springer, Cham.
  • Narayanan, B. L., Yosri, A., Ezzeldin, M., El-Dakhakhni, W., & Dickson-Anderson, S. (2021). A complex network theoretic approach for interdependence investigation: An application to radionuclide behavior in the subsurface. Computers & Geosciences, 104913.
  • Naugle, A., Verzi, S., Lakkaraju, K., Swiler, L., Warrender, C., Bernard, M., & Romero, V. (2021). Feedback density and causal complexity of simulation model structure. Journal of Simulation, 1-11.
  • Navasartian, K. (2021). Simulating the Spread and Containment of COVID-19: An Agent-Based Modelling Approach (Doctoral dissertation, Wien).
  • Negahban, A., & Giabbanelli, P. J. (2021). Hybrid Agent-Based Simulation of Adoption Behavior and Social Interactions: Alternatives, Opportunities, and Pitfalls. IEEE Transactions on Computational Social Systems.
  • Nerrise, F. (2021, May). Predictive Agent-Based Modeling of Natural Disasters Using Machine Learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 18, pp. 15976-15977).
  • Neumayr, R. R. (2021). Agent-Based Semiology: Optimizing Office Occupation Patterns with Agent-Based Simulations. In Formal Methods in Architecture (pp. 49-59). Springer, Cham.
  • Newton, A. C., Evans, P. M., Watson, S. C., Ridding, L. E., Brand, S., McCracken, M., ... & Bullock, J. M. (2021). Ecological restoration of agricultural land can improve its contribution to economic development. PloS one, 16(3), e0247850.
  • Nganro, S., Trisutomo, S., Barkey, R., Ali, M., Imura, H., Onishi, A., ... & Mahamud, M. A. (2021). Prediction of Future Land Use and Land Cover (LULC) in Makassar City. TATALOKA, 23(2), 183-189.
  • Nguyen, G. T. H., Nong, D. H., & See, L. (2021). Simulating the Spatial Distribution of Pollutant Loads from Pig Farming using an Agent-based Modeling Approach. Environmental Science and Pollution Research(preprint). Vietnam National University of Agriculture.
  • Nguyen, J., Powers, S. T., Urquhart, N., Farrenkopf, T., & Guckert, M. (2021). An Overview of Agent-based Traffic Simulators. arXiv preprint arXiv:2102.07505.
  • Ni, L., Bausch, G., & Benjamin, R. (2021). Computer science teacher professional development and professional learning communities: a review of the research literature. Computer Science Education, 1-32.
  • Nöldeke, B., Winter, E., Laumonier, Y., & Simamora, T. (2021). Simulating Agroforestry Adoption in Rural Indonesia: The Potential of Trees on Farms for Livelihoods and Environment. Land, 10(4), 385.
  • Nsor, M., & Kapale, K. (2020). Visualization and Simulation of Traffic Flow. Bulletin of the American Physical Society, 65.
  • Ntankouo Njila, R. C., Mostafavi, M. A., & Brodeur, J. (2021). A Decentralized Semantic Reasoning Approach for the Detection and Representation of Continuous Spatial Dynamic Phenomena in Wireless Sensor Networks. ISPRS International Journal of Geo-Information, 10(3), 182.
  • Nunes, A. M., Zwick, M., & Wakeland, W. (2021). Sensitivity analysis of an agent-based simulation model using reconstructability analysis. International Journal of General Systems, 50(3), 319-338.
  • Nur, N. (2021). Developing Temporal Machine Learning Approaches to Support Modeling, Explaining, and Sensemaking of Academic Success and Risk of Undergraduate Students (Doctoral dissertation, The University of North Carolina at Charlotte).
  • Ofori, R. O., & Rouleau, M. D. (2021). Modeling the impacts of floating seaweeds on fisheries sustainability in Ghana. Marine Policy, 127, 104427.
  • Ogunsakin, R., Marin, C. A., & Mehandjiev, N. (2021). Towards engineering manufacturing systems for mass personalisation: a stigmergic approach. International Journal of Computer Integrated Manufacturing, 1-29.
  • Oh, J., & Kim, S. (2021). Distributed Trust Management for Fog Based IoT Environment. Journal of the Korea Institute of Information Security & Cryptology, 31(4), 731-751.
  • Oh, W. S., Yu, D. J., & Muneepeerakul, R. (2021). Efficiency-fairness trade-offs in evacuation management of urban floods: The effects of the shelter capacity and zone prioritization. PloS one, 16(6), e0253395.
  • Oliveira, N., & Secchi, D. (2021). Theory Building, Case Dependence, and Researchers’ Bounded Rationality: An Illustration From Studies of Innovation Diffusion. Sociological Methods & Research, 0049124120986201.
  • Olzer, R. (2021). The Costs (And Benefits) of Standing Out: Alternative Reproductive Behavior and Novel Trait Evolution in the Pacific Field Cricket (Doctoral dissertation, University of Minnesota).
  • Onggo, B. S. & Utomo, D. S. (2021). Dairy supply chain in West Java: Modelling using agent-based simulation and reporting using the stress guidelines. In M. Fakhimi, D. Robertson, & T. Boness (Eds.), Proceedings of the Operational Research Society Simulation Workshop 2021 (SW21). https://doi.org/10.36819/SW21.032
  • Orjuela-Garzon, W., Quintero, S., Giraldo, D. P., Lotero, L., & Nieto-Londoño, C. (2021). A Theoretical Framework for Analysing Technology Transfer Processes Using Agent-Based Modelling: A Case Study on Massive Technology Adoption (AMTEC) Program on Rice Production. Sustainability 2021, 13, 11143.
  • Ornstein, J. T., & Hammond, R. A. (2021). Agent-Based Modeling in the Social Sciences. New Horizons in Modeling and Simulation for Social Epidemiology and Public Health, 22.
  • Orozco-Rivera, J., Ceballos, Y., & Castillo-Grisales, J. A. (2022). Análisis del alto flujo vehicular para una vía de acceso a Medellín usando simulación basada en agentes. Revista UIS Ingenierías, 21(1), 73-82.
  • Otto Syk, M. (2021). Geopolitics of Finance; Modelling the role of states in the international financial system.
  • Ouseph, S. V. V. (2021). A modern paradigm for cloud computing adoption that brings into account the deployment organization's main concerns.
  • Overwater, A., & Yorke-Smith, N. (2021). Agent-based simulation of short-term peer-to-peer rentals: Evidence from the Amsterdam housing market. Environment and Planning B: Urban Analytics and City Science, 23998083211000747.
  • Ozarisoy, B., & Altan, H. (2021). A novel methodological framework for the optimisation of post-war social housing developments in the South-eastern Mediterranean climate: Policy design and life-cycle cost impact analysis of retrofitting strategies. Solar Energy, 225, 517-560.
  • Ozawa, S., Chen, H. H., Rao, G. G., Eguale, T., & Stringer, A. (2021). Value of pneumococcal vaccination in controlling the development of antimicrobial resistance (AMR): Case study using DREAMR in Ethiopia. Vaccine.
  • Ozik, J., Wozniak, J. M., Collier, N., Macal, C. M., & Binois, M. (2021). A Population Data-Driven Workflow for COVID-19 Modeling and Learning.
  • Özgür, K. A. R. T., Genç, O. Ç., & BASCİFTCİ, F. (2021). Speed Compatible Green Wave Corridor with The Internet of Things. Avrupa Bilim ve Teknoloji Dergisi, (28), 411-416.
  • Pahl, C. C., & Ruedas, L. A. (2021). Carnosaurs as Apex Scavengers: Agent-based simulations reveal possible vulture analogues in late Jurassic Dinosaurs. Ecological Modelling, 458, 109706.
  • Palatnik, A., & Abrahamson, D. (2021). Escape from Plato’s cave: An enactivist argument for learning 3d geometry by constructing tangible models. Submitted to CERME12, TWG4.
  • Panjaitan, J. R. H., & Gozan, M. (2021). TECHNO-ECONOMIC EVALUATION OF NITROCELLULOSE PRODUCTION FROM PALM OIL EMPTY FRUIT BUNCHES. ASEAN Engineering Journal, 11(4), 246-254.
  • Paoletti, J., Bisbey, T. M., Zajac, S., Waller, M. J., & Salas, E. (2021). Looking to the Middle of the Qualitative-Quantitative Spectrum for Integrated Mixed Methods. Small Group Research, 1046496421992433.
  • Papageorgiou, A., Chaitanya Munjulury, R., Gårdhagen, R., Amadori, K., & Jouannet, C. (2021). Development of Analysis Capabilities for the Preliminary Sizing and Evaluation of Unmanned Airborne Early Warning Aircraft. In AIAA AVIATION 2021 FORUM (p. 2452).
  • Papageorgiou, A., Ölvander, J., Amadori, K., & Jouannet, C. (2021). Development of Analysis and Simulation Models for Evaluating Airborne Radar Surveillance System of Systems. In AIAA Scitech 2021 Forum (p. 0303).
  • Pardos, Z. A., Rosenbaum, L. F., & Abrahamson, D. (2021). Characterizing learner behavior from touchscreen data. International Journal of Child-Computer Interaction, 100357. https://doi.org/10.1016/j.ijcci.2021.100357
  • Păstrăv, C., Jensen, M., Mellema, R., & Vanhée, L. (2021). Social Simulations for Crises: From Models to Usable Implementations. In Social Simulation for a Crisis (pp. 85-117). Springer, Cham.
  • Patarakin, E., Vachkova, S., & Burov, V. (2021). Agent-based modeling of teacher interaction within a repository of digital objects. In Education and City: Education and Quality of Living in the City (pp. 5013-5013).
  • Patel, J., Katan, J., Perez, L., & Sengupta, R (2021). Transferring decision boundaries onto a geographic space: Agent rules extracted from movement data using classification trees. Transactions in GIS.
  • Pathak, A., Mohan, V. M., & Banerjee, A. (2021). Optimal lockdown strategies: All about time.
  • Paul, R., Behjat, L., & Brennan, R. (2021). USING INDIVIDUAL-BASED MODELING TO BETTER UNDERSTAND THE HIDDEN CURRICULUM OF ENGINEERING. Proceedings of the Canadian Engineering Education Association (CEEA).
  • Paunova, M. (2021). How do they Integrate?: Social Exchange and Reciprocal Integration Among Migrants and Locals. In 5th European Conference on Social Network.
  • Pavlic, T. P., Hanson, J., Valentini, G., Walker, S. I., & Pratt, S. C. (2021). Quorum sensing without deliberation: biological inspiration for externalizing computation to physical spaces in multi-robot systems. Swarm Intelligence, 1-33.
  • Pavlović, B., Ivezić, D., & Živković, M. (2021). State and perspective of individual household heating in Serbia: A survey-based study. Energy and Buildings, 111128.
  • Peel, A., Dabholkar, S., Wu, S., Horn, M.S., Wilensky, U. (2021). An Evolving Definition of Computational Thinking in Science and Mathematics Classrooms. Proceedings of the 5th APSCE International Computational Thinking and STEM in Education Conference 2021, (pp. 119-122).
  • Peel, A., Kelter, J., Wilensky, U., & Horn, M. (2021). Supporting the Integration of Computational Thinking and Science Through Professional Development and Co-design. Presented to the Annual Meeting of the Association of Science Teacher Education (ASTE) 2021. Salt Lake City, UT.
  • Peel, A., Kelter, J., Horn, M., & Wilensky, U. (2021). Designing professional learning experiences to support teachers' computational thinking learning and confidence. Presented at the 2021 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Peel, A., Kelter, J., Zhao, L., Horn, M.S., Wilensky, U. (2022). A Design-Based Research Methodology Utilizing Conjecture Mapping to Frame Embedded Co-design Cycles. Paper accepted to the 2022 Annual Meeting of the National Association of Research in Science Teaching (NARST).
  • Pereira, A., Laureano, R. M., Neto, F., & Macedo, J. (2021, June). Computer simulation of diabetic retinopathy screening adherence: Agent based model with fuzzy logic. In 2021 16th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE.
  • Perez, A. Z., Bone, C., & Stenhouse, G. (2021). Simulating multi-scale movement decision-making and learning in a large carnivore using agent-based modelling. Ecological Modelling, 452, 109568.
  • Perry, G. L. (2021). How far might plant-eating dinosaurs have moved seeds? Biol. Lett. 17 20200689 http://doi.org/10.1098/rsbl.2020.0689.
  • Perry, G. L., Brazier, R. E., & Wilmshurst, J. M. (2021). The role of paleoecology in understanding landscape-level ecosystem dynamics. The Routledge Handbook of Landscape Ecology.
  • Persson, A. (2021). Urban or else? A combined method analysis of the discourse of the Swedish state during the years 2018-2020 on the choice situation of rural youths to move or remain.
  • Pestle, W. J., Hubbell, C., & Hubbe, M. (2021). (DIGSS) Determination of Intervals using Georeferenced Survey Simulation: An R package for subsurface survey. PloS one, 16(9), e0257386.
  • Pfoser, A. Z., & Wenk, C. Towards Large-Scale Agent-Based Geospatial Simulation.
  • Pham, L. M., Parlavantzas, N., Le, H. H., & Bui, Q. H. (2021). Towards a Framework for High-Performance Simulation of Livestock Disease Outbreak: A Case Study of Spread of African Swine Fever in Vietnam. Animals, 11(9), 2743.
  • Phetheet, J., Hill, M. C., Barron, R. W., Gray, B. J., Wu, H., Amanor-Boadu, V., ... & Rossi, M. W. (2021). Relating agriculture, energy, and water decisions to farm incomes and climate projections using two freeware programs, FEWCalc and DSSAT. Agricultural Systems, 193, 103222.
  • Pian, Y., Peng, J., Xu, L., Wu, P., & Li, J. (2021, December). Analysis and simulation optimization of passenger flow in urban rail transit station. In Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021) (Vol. 12058, pp. 262-271). SPIE.
  • Pichon co-directeur, F. Une nouvelle politique d’exécution de simulations stochastiques fondée sur des principes de partitionnement, de sélection et de clonage (Doctoral dissertation, Université d’Artois).
  • Pidiha, N. (2021). Evolution of AEC Project Networks: an Agent-Based Modeling Approach (Doctoral dissertation, Michigan State University).
  • Pillai, M. S., Chaudhary, G., Khari, M., & Crespo, R. G. (2021). Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning. Soft Computing, 1-12.
  • Pineda Ramos, J. F. (2021). Enseñanza y Aprendizaje de los Números Complejos a través de la Historia y la Geometría Dinámica.
  • Pingkuo, L., & Huan, P. (2021). What drives the green and low-carbon energy transition in China?: An empirical analysis based on a novel framework. Energy, 122450.
  • Plantec, E., Aquilanti, L., & Belorgey, R. (2021). Vers l'adaptation de comportements par le biais de l'évolution culturelle pour des essaims de robots autonomes (Doctoral dissertation, LORIA, UMR 7503, Université de Lorraine, CNRS, Vandoeuvre-lès-Nancy).
  • Plazas Escudero, D., Cárdenas-Rodríguez, J. S., & Restrepo Sierra, M. (2021). Agent-Based Model for Studying Diabetes under the Influence of Relationships. Cuadernos de Ingeniería Matemática, 1(01), 1-9.
  • Pietzsch B.W., Peter F.J. and Berger U. (2021) The Effect of Sanitation Felling on the Spread of the European Spruce Bark Beetle=97An Individual-Based Modeling Approach. Front. For. Glob. Change 4:704930. https://doi.org/10.3389/ffgc.2021.704930
  • Pilny, A. (2021). Computational Methods for Studying Group Communication. In The Emerald Handbook of Group and Team Communication Research. Emerald Publishing Limited.
  • Pommerening, A. (2021). Doctoral Studies and All That. In Staying on Top in Academia (pp. 33-50). Springer, Cham.
  • Ponziani, F. A., Tinaburri, A., Eudes, V. S. G., & Zip, C. Some Effect Of Interpersonal Distance Constraints In Modeling Wayout Finding From An Exhibition Hall.
  • Pot, V., Portell, X., Otten, W., Garnier, P., Monga, O., & Baveye, P. C. Accounting for soil architecture and microbial dynamics in microscale models: Current practices in soil science and the path ahead. European Journal of Soil Science.
  • Pratama, R. A. R. J., & Rusdan, M. (2021). Effectiveness of Rastra Bulog Rice Distribution Using Agent Based Modeling and Simulation Tools. Almana: Jurnal Manajemen dan Bisnis, 5(1), 84-92.
  • Pray, I. W., Pizzitutti, F., Bonnet, G., Gonzalez-Gustavson, E., Wakeland, W., Pan, W. K., ... & Cysticercosis Working Group in Peru. (2021). Validation of a spatial agent-based model for Taenia solium transmission (“CystiAgent”) against a large prospective trial of control strategies in northern Peru. PLoS neglected tropical diseases, 15(10), e0009885.
  • Prinz, A. (2021). Teaching Language Engineering Using MPS. In Domain-Specific Languages in Practice (pp. 315-336). Springer, Cham.
  • QIU, L. P., & YANG, L. H. (2021). The research of co-evolution mechanisms between cross-border e-commerce and manufacturing cluster: An Agent-based model. In E3S Web of Conferences (Vol. 235, p. 03048).
  • Qureshi, A., & Ahmad, K. (2021, November). Agents and Secure Contracts in Cyber-Physical Systems: A Simulation. In Proceedings of the Future Technologies Conference (pp. 533-551). Springer, Cham.
  • Racine, E. E., & Bryson, J. J. (2021). Epidemic modeling as a means to reimagine health education and policy post-COVID. Health Education.
  • Radchuk, V., Kramer-Schadt, S., Berger, U., Scherer, C., Backmann, P., & Grimm, V. (2021). Individual-based models. Demographic Methods Across the Tree of Life, 213.
  • Raees, M., Khan, T. A., Mustafa Abbasi, K., Ahmed, A., Fazilat, S., & Ahmed, I. (2021). Context-Aware Services Using MANETs for Long-Distance Vehicular Systems: A Cognitive Agent-Based Model. Scientific Programming, 2021.
  • Rahimi, M., Navimipour, N. J., Hosseinzadeh, M., Moattar, M. H., & Darwesh, A. (2021). Toward the efficient service selection approaches in cloud computing. Kybernetes.
  • Railsback, S. F., & Arcata, C. A. (2021). InSALMO 7 Model Description.
  • Raimbault, J. (2021). Simulating urban dynamics and international governance of transportation infrastructure projects. arXiv preprint arXiv:2108.13915.
  • Raimbault, J. (2021). A multiscale model of urban morphogenesis. arXiv preprint arXiv:2103.17241.
  • Raimbault, J. (2021). Strong coupling between scales in a multi-scalar model of urban dynamics. arXiv preprint arXiv:2101.12725.
  • Rajak, B., Mallick, S., & Gaurav, K. (2021). Role of Information Communication and Technology at Kumbha Mela–2019 (Prayagraj). Ilkogretim Online, 20(5).
  • Rakotonarivo, S., Bell, A., Abernethy, K., Minderman, J., Duthie, A., Redpath, S., ... & Bunnefeld, N. (2021). The role of incentive-based instruments and social equity in conservation conflict interventions. Ecology and Society, 26(2).
  • Ramadhan, R., Salman, F., Mori, A., & Abdoellah, O. S. (2021). Shifting Cultivation, Palm Oil Plantation and Indirect Deforestation: A Study on Dusun Tonggong, Parindu, West Kalimantan, Indonesia. Journal of Sustainable Forestry, 1-20.
  • Rambu Ngana, F., & Eka Karyawati, A. A. I. N. (2021). Scenario modelling as planning evidence to improve access to emergency obstetric care in eastern Indonesia. Plos one, 16(6), e0251869.
  • Ramkumar, S., Mueller, M., Pyka, A., & Squazzoni, F. (2021). Diffusion of eco-innovation through inter-firm network targeting: An agent-based model. Journal of Cleaner Production, 130298.
  • Ramkumar, S., & Oh, W. S. (2021). AIforGoodSimulator-Modeling Covid-19 Spread and Potential Interventions in Refugee Camps v1. 0.0. CoMSES Computational Model Library.
  • Ratnadass, A., Avelino, J., Fernandes, P., Letourmy, P., Babin, R., Deberdt, P., ... & Van Den Berg, J. (2021). Synergies and tradeoffs in natural regulation of crop pests and diseases under plant species diversification. Crop Protection, 105658.
  • Reed, S. K. Complex Systems (pp. 209-220). Oxford University Press.
  • Regnath, F., Berger, C., & Mahdavi, A. (2022, May). The impact of occupants' energy awareness and thermal preferences on buildings' performance. In CLIMA 2022 conference.
  • Reinhardt, O., Warnke, T., & Uhrmacher, A. M. (2022). Agent-Based Modelling and Simulation with Domain-Specific Languages. In Towards Bayesian Model-Based Demography (pp. 113-134). Springer, Cham.
  • Ren, B., Wang, L., Wang, X., & Chen, J. (2021). Simulating Energy-Saving and Consuming Behaviours in the Design and Manufacturing Process with Adjacent Networks. In Recent Advances in Sustainable Energy and Intelligent Systems (pp. 441-451). Springer, Singapore.
  • Resende, L. P. A. O papel da construção de nicho na evolução e ecologia da socialidade em aranhas.
  • Retzlaff, C. O., Ziefle, M., & Valdez, A. C. (2021). The History of Agent-Based Modeling in the Social Sciences. In International Conference on Human-Computer Interaction (pp. 304-319). Springer, Cham.
  • Revuelta, E. C., Chávez, M. J., Vera, J. A. B., Rodríguez, Y. F., & Sánchez, M. C. (2021). Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms. Measurement, 108898.
  • Reyes-Mercado, P. FinTech Strategy: Linking Entrepreneurship, Finance, and Technology. Springer Nature.
  • Roanes-Lozano, E., Solano-Macías, C., & Roanes-Macías, E. A simplified introduction to virus propagation using Maple's Turtle Graphics package.
  • Robinson, J. T. (2021). Development of an Agent-Based Model to Recapitulate Murine Patellar Tendon Healing as a Function of Age (Doctoral dissertation, Tulane University School of Science and Engineering).
  • Rocha, É. G. D., Brigatti, E., Niebuhr, B. B., Ribeiro, M. C., & Vieira, M. V. (2021). Dispersal movement through fragmented landscapes: the role of stepping stones and perceptual range. Landscape Ecology, 1-19.
  • Rodriguez-Lopez, J. M., Schickhoff, M., Sengupta, S., & Scheffran, J. (2021). Technological and social networks of a pastoralist artificial society: agent-based modeling of mobility patterns. Journal of Computational Social Science, 1-27.
  • Rollins, M. L., & Griffen, B. D. (2021). Optimal Memory in Food-caching Organisms.
  • Romanowska, I., Wren, C., & Crabtree, S. (2021). Agent-Based Modeling for Archaeology: Simulating the Complexity of Societies. Santa Fe, NM: SFI Press.
  • Romero-Mujalli, D., Rochow, M., Kahl, S., Paraskevopoulou, S., Folkertsma, R., Jeltsch, F., & Tiedemann, R. Adaptive and non-adaptive plasticity in changing environments: implications for sexual species with different life history strategies. Authorea Preprints.
  • Rothrock, L., Abraham, A., Graf, A., Rodopman, M., & Nold, D. (2021). Aiding decision makers to reopening of places of worship. Human Factors and Ergonomics in Manufacturing & Service Industries.
  • Roxburgh, N., Stringer, L. C., Evans, A., GC, R. K., Malleson, N., & Heppenstall, A. (2021). Nepal Stressor Interaction Model (Nepal SIM).
  • Saba, J., Hel-Or, H., & Levy, S. T. (2021). Much. Matter. in. Motion: learning by modeling systems in chemistry and physics with a universal programing platform. Interactive Learning Environments, 1-20.
  • Saldanha, J., Adamatti, D. F., & Dimuro, G. (2021). Social identity theory applied to the game of self-regulation of social exchanges based on multiagent systems. Journal of Simulation, 1-14.
  • Saleem, K., & Haque, H. M. U. (2020). Modelling Situation-Aware Formalism Using BDI Reasoning Agents. In Context-Aware Systems and Applications, and Nature of Computation and Communication (pp. 169-181). Springer, Cham.
  • Sambuaga, R. D., & Lee, H. S. (2021). Optimized Evacuation Plan and Decision Support System Development with Agent-Based Modelling and GIS Analysis for Tsunami Events in Pandeglang, Banten, Indonesia. Journal of Coastal Research, 114(sp1), 509-513.
  • Samon, S., & Levy, S. T. (2021). The Role of Physical and Computer-Based Experiences in Learning Science Using a Complex Systems Approach. Science & Education, 1-37.
  • Sandoval, S. M., & Alvarado-Monroy, A. (2021). La modelización como vehículo para el desarrollo del razonamiento covariacional en educación secundaria. Quadrante, 30(2), 147-178.
  • Sandoval-Félix, J., Castañón-Puga, M., & Gaxiola-Pacheco, C. G. (2021). Analyzing Urban Public Policies of the City of Ensenada in Mexico Using an Attractive Land Footprint Agent-Based Model. Sustainability, 13(2), 714.
  • Sari, R. F., Ilmananda, A. S., & Romano, D. M. (2021). Social trust-based blockchain-enabled social media news verification system. Journal of Universal Computer Science, 27(9), 979-998.
  • Saunders, D. (2021). How to Put the Cart Behind the Horse in the Cultural Evolution of Gender. Philosophy of the Social Sciences, 00483931211049770.
  • Saxe, J. G. (2021). Claudio Cioffi-Revilla. Handbook of Computational Social Science, Volume 1: Theory, Case Studies and Ethics, 2.
  • Schloesser, D. S., Hollenbeck, D., & Kello, C. T. (2021). Individual and collective foraging in autonomous search agents with human intervention. Scientific Reports, 11(1), 1-13.
  • Schön, S., Marcus, C., Amadori, K., & Jouannet, C. (2021). Integration of Multi-Fidelity Models with Agent-Based Simulation for System of Systems. In AIAA AVIATION 2021 FORUM (p. 2996).
  • Schoville, B. J., Brown, K. S., & Wilkins, J. (2021). A Lithic Provisioning Model as a Proxy for Landscape Mobility in the Southern and Middle Kalahari. Journal of Archaeological Method and Theory, 1-26.
  • Schlüter, M., Lindkvist, E., Wijermans, N., & Polhill, G. Agent-based modelling.(2021). The Routledge Handbook of Research Methods for Social-Ecological Systems, 383.
  • Schmolke, A., Bartell, S. M., Roy, C., Desmarteau, D., Moore, A., Cox, M. J., ... & Brain, R. Applying a Hybrid Modeling Approach to Evaluate Potential Pesticide Effects and Mitigation Effectiveness for an Endangered Fish in Simulated Oxbow Habitats. Environmental Toxicology and Chemistry.
  • Schulz, J., & Mayerhoffer, D. M. (2021). Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms. Journal of Business Economics, 1-29.
  • Scorrano, M., & Danielis, R. (2021). Simulating electric vehicle uptake in Italy in the small-to-medium car segment: A system dynamics/agent-based model parametrized with discrete choice data. Research in Transportation Business & Management, 100736.
  • Secchi, D. (2021). An Unusual Diffusion Model. In Computational Organizational Cognition: A Study on Thinking and Action in Organizations. Emerald Publishing Limited.
  • Secchi, D. (2021). The Operational Boundaries of Docility. In Computational Organizational Cognition: A Study on Thinking and Action in Organizations. Emerald Publishing Limited.
  • Sedigh, A. H. A., Purvis, M. K., Savarimuthu, B. T. R., Frantz, C. K., & Purvis, M. A. (2021). Impact of different belief facets on agents' decision - A refined cognitive architecture to model the interaction between organisations' institutional characteristics and agents' behavior. In A. Aler Tubella, S. Cranefield, C. Frantz, F. Meneguzzi, & W. Vasconcelos (Eds.), Coordination, organizations, institutions, norms, and ethics for governance of multi-agent systems XIII: International Workshops COIN 2017 and COINE 2020, Sao Paulo, Brazil, May 8–9, 2017 and Virtual Event, May 9, 2020, Revised Selected Papers, p. 133. Springer Nature.
  • Şendurur, P., & Sendurur, E. (2022). Students as Gamers: Design, Code, and Play. In Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning (pp. 868-887). IGI Global.
  • Sengupta, S., Scheffran, J., & Kovalevsky, D. (2021). Agent Adaptation in an Urban Coastal Scenario: Applying the VIABLE Framework. 12. Deutsche Klimatagung, Online-Tagung, 15. bis 18. März 2021.
  • Sengupta, S., Scheffran, J., & Kovalevsky, D. (2021, April). A Single-Agent Urban Coastal Adaptation Model: Adaptive decision-making within the VIABLE modeling framework. In EGU General Assembly Conference Abstracts (pp. EGU21-12752).
  • Shaaban, M., & Scheffran, J. (2021). A Dynamic-Agent-Based Sustainability Assessment of Energy Systems. In Energy Systems Evaluation (Volume 1) (pp. 161-181). Springer, Cham.
  • Shafqat, F., Khan, M. N. A., & Shafqat, S. (2021). SmartHealth: IoT-Enabled Context-Aware 5G Ambient Cloud Platform. In IoT in Healthcare and Ambient Assisted Living (pp. 43-67). Springer, Singapore.
  • Shaharuddin, R. A., & Misro, M. Y. (2021, November). Traffic simulation using agent based modelling. In AIP Conference Proceedings (Vol. 2423, No. 1, p. 020035). AIP Publishing LLC.
  • Shaheen, J. (2021). Are Agent-based Models Universal Approximators. Academia Letters, 2.
  • Shanaa, M., & Abdallah, S. (2020, November). Agent-based simulation for COVID-19 outbreak within a semi-closed environment. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) (pp. 231-236). IEEE.
  • Shandera, S., Matsick, J. L., Hunter, D. R., & Leblond, L. (2021). RASE: Modeling cumulative disadvantage due to marginalized group status in academia. PloS one, 16(12), e0260567.
  • Shapiro, B., & Crooks, A. (2021, July). Kinetic Action and Radicalization: A Case Study of Pakistan. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 321-330). Springer, Cham.
  • Sherard, M. K., & Petrosino, A. J. (2021). Language, Modeling and Power: A Methodology for Analyzing Discourse in Interaction. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Shi, Z., Li, Y., & Jaberi-Douraki, M. (2021). Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes. PLOS Computational Biology, 17(9), e1009413.
  • Shiang, C. W., & Hussain, N. (2021). Modelling of Crowd Evacuation with Communication Strategy using Social Force Model. Journal of Optimization in Industrial Engineering.
  • Shiflet, A. B., Shiflet, G. W., & Pendarvis, M. P. (2020). Biology for the Global Citizen, A New Non-Majors Biology Text with Laboratories Using Computer Simulations.
  • Shiwei, Y. U. A. N., Xin, L. I., & Erhu, D. U. (2021). Progress and Prospect of Agent-Based Modeling for Water Resources Management. Advances in Earth Science, 36(9), 899-910.
  • Shoole, A. A., & Wadi, M. (2021). Multiagent systems application for the smart grid protection. Renewable and Sustainable Energy Reviews, 149, 111352.
  • Shojaati, N., & Osgood, N. D. (2021). Dynamic Computational Models and Simulations of the Opioid Crisis: A Comprehensive Survey. ACM Transactions on Computing for Healthcare (HEALTH), 3(1), 1-25.
  • Shou, W., Wang, J., & Wu, P. (2021). The application of simulation in lean production research: a critical review and future directions. Engineering, Construction and Architectural Management.
  • Siar, S. A., Keramati, M., & Motadel, M. (2021). Agent-Based Simulation of Consumer Behavior in Impulse Buying. Journal of Industrial Management Studies, 19(62), 99-138.
  • Siddiqui, J., Mehjabeen, M., & Stapleton, P. (2021). Emergence of corporate political activities in the guise of social responsibility: dispatches from a developing economy. Accounting, Auditing & Accountability Journal.
  • Siddiqui, S. Y., Ahmad, I., Khan, M. A., Khan, B. S., Ali, M. N., Naseer, I., ... & Usama, H. M. (2021). AIoT Enabled Traffic Congestion Control System Using Deep Neural Network.
  • Silva, M. I. G., Silva, R. A. G., López, H. A. J., & Ontiveros, A. A. (2021). A mechanism of Individualistic Indirect Reciprocity with internal and external dynamics. arXiv preprint arXiv:2105.14144.
  • Silva, E. M., Moura, G., & Da Silva, S. (2021). Monetary Policy Experiments in an Agent-Based Macroeconomic Model. Open Access Library Journal, 8(5), 1-14.
  • Silva, T. (2021). Complexity theory and the historical study of religion: navigating the transdisciplinary space between the Humanities and the Natural Sciences. História da Historiografia: International Journal of Theory and History of Historiography, 14(36), 167-196.
  • Silva, T. F. D., Araújo, M. S., Ferro Junior, R. J. C., Costa, L. F. D., Andrade, J. P. B., & Campos, G. A. L. D. (2021, November). Intelligent Agents for Observation and Containment of Malicious Targets Organizations. In Brazilian Conference on Intelligent Systems (pp. 48-63). Springer, Cham.
  • Silveira, N. J. C., Ferraz, D., de Mello, D. S., Polloni-Silva, E., do Nascimento Rebelatto, D. A., & Moralles, H. F. (2021). Determinants of Absorptive Capacity: a systematic literature review. Revista Gestão da Produção Operações e Sistemas, 16(2), 122.
  • Singh, P., Kaur, A., Batth, R. S., Aujla, G. S., & Masud, M. (2021). Service vs Protection: A Bayesian Learning Approach for Trust Provisioning in Edge of Things Environment. IEEE Internet of Things Journal.
  • Singh, S., France, A. M., Chen, Y. H., Farnham, P. G., Oster, A. M., & Gopalappa, C. (2021). Progression and transmission of HIV (PATH 4.0)-A new agent-based evolving network simulation for modeling HIV transmission clusters. Mathematical Biosciences and Engineering, 18(3), 2150-2181.
  • Smarzhevskiy, I. A. (2021). The Simulation Model of Collective Management Decision-Making Implemented in Netlogo. In Industry Competitiveness: Digitalization, Management, and Integration: Volume 2 (pp. 349-357). Springer International Publishing.
  • Smith, A. P. (2021). neworder: a dynamic microsimulation framework for Python. Journal of Open Source Software, 6(63), 3351.
  • Sobkowicz, P., & Sobkowicz, A. (2021). Agent Based Model of Anti-Vaccination Movements: Simulations and Comparison with Empirical Data. Vaccines, 9(8), 809.
  • Sobkowicz, P.(2021) The Role of Chance in Individual Sports: an Agent-Based Approach for Fencing Tournaments.
  • Sonawane, C., Yirga, G., & Carter, N. H. Public health and economic benefits of spotted hyenas Crocuta crocuta in a peri‐urban system. Journal of Applied Ecology.
  • Sood, S. K., Sood, V., & Mahajan, I. (2021). An intelligent healthcare system for predicting and preventing dengue virus infection. Computing, 1-39.
  • Souidi, M. E. H., Maarouk, T. M., & Ledmi, A. (2021). Multi-agent Ludo Game Collaborative Path Planning based on Markov Decision Process. In Inventive Systems and Control (pp. 37-51). Springer, Singapore.
  • Spanoudakis, N. I. (2021). Engineering Multi-agent Systems with Statecharts. SN Computer Science, 2(4), 1-21.
  • Spurný, J., Kopeček, I., Ošlejšek, R., Plhák, J., & Caputo, F. (2021). The prisoner’s dilemma in the workplace: how cooperative behavior of managers influence organizational performance and stress. Kybernetes.
  • Squire, K. D. (2021). From virtual to participatory learning with technology during COVID-19. E-Learning and Digital Media, 20427530211022926.
  • Staffini, A., Svensson, A. K., Chung, U. I., & Svensson, T. (2021). An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study. JMIR Medical Informatics, 9(4), e24192.
  • Steinbacher, M., Raddant, M., Karimi, F., Cuena, E., Alfarano, S., Iori, G., & Lux, T. (2021). Advances in the Agent-based Modeling of Economic and Social Behavior. SN Business & Economics.
  • Steinberg, S., & Gresalfi, M. (2021). Agency and Expressivity in Programming Play. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Strawhacker, A., Kafai, Y., T. Giang, M., Fields, D., & Tofel-Grehl, C. (2021, June). Designing the Virtual SPIKEY-20 Epidemic: Engaging Youth in Seeking Information and Using Personal Protection. In Interaction Design and Children (pp. 558-562).
  • Su, M., Cho, J. Y., Chi, M. T., Boucher, N., & Vanbibber, B. Designing Simulation Module to Diagnose Misconceptions in Learning Natural Selection.
  • Su, Y., Jiang, X., & Lin, Z. (2021). Simulation and Relationship Strength: Characteristics of Knowledge Flows Among Subjects in a Regional Innovation System. Science, Technology and Society, 09717218211020476.
  • Subrahmanyam, V. S. C., Raman, A. V., Krishna, S. S., Sitharthan, I., Basha, S. S., Prabavathy, B., & Deborah, S. A. (2021). Smart Warehouse Management System. In Recent Trends in Renewable Energy Sources and Power Conversion (pp. 99-114). Springer, Singapore.
  • Sulis, E., & Tambuscio, M. (2020). Simulation of misinformation spreading processes in social networks: An application with NetLogo. In G. Webb, Z. Zhang, V. S. Tseng, G. Williams, M. Vlachos, & L. Cao (Eds.), International Conference on Data Science and Advanced Analytics (DSAA) (pp. 614-618). IEEE. https://doi.org/10.1109/DSAA49011.2020.00086
  • Sulis, E., & Terna, P. (2021). An Agent-based Decision Support for a Vaccination Campaign. Journal of Medical Systems, 45(11), 1-7.
  • Sun, T., Bu, F., Liu, X., & Fu, Y. (2020, December). Modeling and Simulation of Group Drug-related Incident Evolution. In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (pp. 1247-1252). IEEE.
  • Swanson, H., Sherin, B., Wilensky, U. (2021). Characterizing student theory building in computational modeling activities. Proceedings of the 15th International Conference of the Learning Sciences. Bochum, Germany: International Society of the Learning Sciences.
  • Swanson, H., Sherin, B., & Wilensky, U. (2021). Refining student thinking through computational modeling. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.. International Society of the Learning Sciences.
  • Tałanda, J. (2021). The effect of artificial light at night on interactions between planktivorous fish and their cladoceran prey.
  • Taghavi, A., Khaleghparast, S., & Eshghi, K. (2021). Optimal Agent Framework: A Novel, Cost-Effective Model Articulation to Fill the Integration Gap between Agent-Based Modeling and Decision-Making. Complexity, 2021.
  • Tancredi, S., Abdu, R., Abrahamson, D., & Balasubramaniam, R. (2021). Modeling nonlinear dynamics of fluency development in an embodied-design mathematics learning environment with Recurrence Quantification Analysis. International Journal of Child-Computer Interaction, 100297. https://doi.org/10.1016/j.ijcci.2021.100297
  • Tejera Linares, M. D. C. (2021). Aprendiendo de la enfermedad COVID-19.
  • ten Broeke, G., van Voorn, G., Ligtenberg, A., & Molenaar, J. (2021). The Use of Surrogate Models to Analyse Agent-Based Models. Journal of Artificial Societies and Social Simulation, 24(2).
  • Termos, A., Picascia, S., & Yorke-Smith, N. (2021). Agent-Based Simulation of West Asian Urban Dynamics: Impact of Refugees. Journal of Artificial Societies and Social Simulation, 24(1), 1-2.
  • Terna, P. Matematica e simulazione per la morfologia e la dinamica spaziale. Volume 10-Numero 4-Luglio 2020, 177.
  • Ternes, P., Ward, J. A., Heppenstall, A., Kumar, V., Kieu, L. M., & Malleson, N. (2021). Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters. Open Research Europe, 1(131), 131.
  • Thasnimol, C. M., & Rajathy, R. (2021). An ideal solution for the deployment of photo voltaic generators using an agent-based nash differential evolution (NashDE) algorithm. International Journal of Emerging Electric Power Systems.
  • Thneibat, M., Thneibat, M., Al-Shattarat, B., & Al-kroom, H. (2021). Development of an agent-based model to understand the diffusion of value management in construction projects as a sustainability tool. Alexandria Engineering Journal.
  • Thomas, S. R. (2021). Effects of Transformational Leadership and Employability on Employee Retention: An Agent-Based Model (Doctoral dissertation, Arizona State University).
  • Tian, D., Zhang, M., Zhao, A., Wang, B., Shi, J., & Feng, J. (2021). Agent-based modeling and simulation of edible fungi growers' adoption behavior towards fungal chaff recycling technology. Agricultural Systems, 103138.
  • Tiram, E., & Sinuany-Stern, Z. (2021). Overview of Simulation in Higher Education: Methods and Applications. In Handbook of Operations Research and Management Science in Higher Education (pp. 81-115). Springer, Cham.
  • Townsend, D. (2021). Validation and Inference of Agent Based Models. arXiv preprint arXiv:2107.03619.
  • Trentesaux, D. (2021). A Multi-agent Model for the Multi-plant Multi-product Physical Internet Supply Chain Network. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA 2020, 435.
  • Trentesaux, D., & Chauvin, C. (2021). A Benchmarking Platform for Human-Machine Cooperation in Cyber-Physical Manufacturing Systems. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA 2020, 313.
  • Trentesaux, D., & Karnouskos, S. (2021). Engineering ethical behaviors in autonomous industrial cyber-physical human systems. Cognition, Technology & Work, 1-14.
  • Troitzsch, K. G. (2021). Formal Design Methods and the Relation Between Simulation Models and Theory: A Philosophy of Science Point of View. In Pathways Between Social Science and Computational Social Science (pp. 21-45). Springer, Cham.
  • Troitzsch, K. G. (2021). Validating Simulation Models: The Case of Opinion Dynamics. In Pathways Between Social Science and Computational Social Science (pp. 123-155). Springer, Cham.
  • Truong, V. T., Baverel, P. G., Lythe, G. D., Vicini, P., Yates, J. W., & Dubois, V. F. (2021). Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models. CPT: Pharmacometrics & Systems Pharmacology.
  • Tsompanas, M. A., Fyrigos, I. A., Ntinas, V., Adamatzky, A., & Sirakoulis, G. C. (2021). Cellular automata implementation of Oregonator simulating light-sensitive Belousov–Zhabotinsky medium. Nonlinear Dynamics, 1-13.
  • Tucker, G. E., Hutton, E. W., Piper, M. D., Campforts, B., Gan, T., Barnhart, K. R., ... & Syvitski, J. (2021). CSDMS: A community platform for numerical modeling of Earth-surface processes. Geoscientific Model Development Discussions, 1-40.
  • Tucker-Raymond, E., Cassidy, M., & Puttick, G. (2021). Science teachers can teach computational thinking through distributed expertise. Computers & Education, 104284.
  • Tulang, A. B. (2021). Online Learning Communities Amid the COVID-19 Pandemic: An Agent-Based Model. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 6294-6302.
  • Tullis, J. G., & Fraundorf, S. H. (2021). Selecting effectively contributes to the mnemonic benefits of self-generated cues. Memory & Cognition, 1-17.
  • Uhde, A., & Hassenzahl, M. (2021). Simulating Social Acceptability With Agent-based Modeling. arXiv preprint arXiv:2105.06730.
  • Uzzo, S. M., Cramer, C. B., Sayama, H., & Faux, R. (2021). NetSci High: Bringing Agency to Diverse Teens Through the Science of Connected Systems. Northeast Journal of Complex Systems (NEJCS), 3(2), 2.
  • Valente, J. A., Caceffo, R., Bonacin, R., dos Reis, J. C., Gonçalves, D. A., & Baranauskas, M. C. C. (2021). Embodied‐based environment for kindergarten children: Revisiting constructionist ideas. British Journal of Educational Technology.
  • Van Buskirk, A. N., Rosenberry, C. S., Wallingford, B. D., Domoto, E. J., McDill, M. E., Drohan, P. J., & Diefenbach, D. R. (2021). Modeling how to achieve localized areas of reduced white-tailed deer density. Ecological Modelling, 109393.
  • Vanhée, L. (2021). Engineering Social Simulations for Crises. In Social Simulation for a Crisis (pp. 353-378). Springer, Cham.
  • Vasishta, A. (2021). Understanding Ideal Social Networking Strategies Based on Relational Mobility and Environmental Stability.
  • Vasylieva, O., Butvin, B., & Shtyfurak, Y. (2021). Methodological Approach to Agent-Based Modeling of Social Networks (No. 6291). EasyChair.
  • Vázquez, G. C., Cristóbal, R. V., Romero, E. A., & Alonso, J. U. (2021). Dinámicas de asistencia al supermercado “El Farolito” en condiciones de pandemia. Política y Cultura, (55), 151-175.
  • Veldt, N., Benson, A. R., & Kleinberg, J. (2021). Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components. arXiv preprint arXiv:2110.14859.
  • Velghe, F., De Wilde, F., Snellinx, S., Farahbakhsh, S., Belderbos, E., Peral, C., ... & Dietrich, T. (2021). Volatile Fatty Acid platform–a cornerstone for the circular bioeconomy. FEMS Microbiology Letters.
  • Veloso, P., & Krishnamurti, R. (2021). Mapping generative models for architectural design. The Routledge Companion to Artificial Intelligence in Architecture, 29.
  • Veloso, P., & Krishnamurti, R. (2021). Self-learning Agents for Spatial Synthesis. In Formal Methods in Architecture (pp. 265-276). Springer, Cham.
  • Vermeer, W. H., Smith, J. D., Wilensky, U., & Brown, C. H. (2021). High-Fidelity Agent-Based Modeling to Support Prevention Decision-Making: an Open Science Approach. Prevention Science, 1-12.
  • Vasishta, Angela, (2021). Understanding Ideal Social Networking Strategies Based on Relational Mobility and Environmental Stability Undergraduate Honors Theses. Paper 1731.
  • Vázquez, G. C., Cristóbal, R. V., Romero, E. A., & Alonso, J. U. (2021). Dinámicas de asistencia al supermercado “El Farolito” en condiciones de pandemia. Política y Cultura, (55), 151-175.
  • Vodopivec, N., Adam, C., & Chanteau, J. P. (2021). Modeling opinion leader's role in the diffusion of innovation. arXiv preprint arXiv:2101.11260.
  • Walzberg, J., Burton, R., Zhao, F., Frost, K., Muller, S., Carpenter, A., & Heath, G. (2022). An investigation of hard-disk drive circularity accounting for socio-technical dynamics and data uncertainty. Resources, Conservation and Recycling, 178, 106102.
  • Wang, J., Yin, J., Khan, R. U., Wang, S., & Zheng, T. (2021). A Study of Inbound Logistics Mode Based on JIT Production in Cruise Ship Construction. Sustainability 2021, 13, 1588.
  • Wang, J. (2021). Understanding Carsharing-Facilitating Neighborhood Preferences. (Thesis).
  • Wang, W. (2021). Spatial Analysis of COVID-19 Risk Based on Different Lockdown Strategies-a Case Study for Storrs Campus Community, University of Connecticut. Authorea Preprints.
  • Wang, Y., Ge, J., & Comber, A. (2021). Simulation model of pedestrian flow based on multi-agent system and Bayesian Nash equilibrium. AGILE: GIScience Series, 2, 1-7.
  • Wang, Y., Zhang, Q., Li, Q., Wang, J., Sannigrahi, S., Bilsborrow, R., ... & Song, C. (2021). Role of social networks in building household livelihood resilience under payments for ecosystem services programs in a poor rural community in China. Journal of Rural Studies.
  • Wang, Y. Y., & Bu, F. L. (2021, August). Emotional Interaction Computing of Actors in the Mass Incidents. In International Conference on Intelligent Computing (pp. 18-30). Springer, Cham.
  • Wang, Z. (2021). SIMULATION-BASED TSUNAMI EVACUATION RISK ASSESSMENT AND RISK-INFORMED MITIGATION (Doctoral dissertation, Colorado State University).
  • Wang, Z., & Chen, A. (2021). On ISRC Rumor Spreading Model for Scale-Free Networks with Self-Purification Mechanism. Complexity, 2021.
  • Wang, Z., & Jia, G. (2021). Simulation-Based and Risk-Informed Assessment of the Effectiveness of Tsunami Evacuation Routes Using Agent-Based Modeling: A Case Study of Seaside, Oregon. International Journal of Disaster Risk Science, 1-21.
  • Wang, Z., & Jia, G. (2021). Tsunami evacuation risk assessment and probabilistic sensitivity analysis using augmented sample-based approach. International Journal of Disaster Risk Reduction, 102462.
  • Wang, Z. Y., Shi, P. J., Zhang, X. B., Wang, Y. S., & Xie, X. Y. (2021). Simulation of Lanzhou urban land expansion based on multi-agent model. Ying Yong Sheng tai xue bao= The Journal of Applied Ecology, 32(6), 2169-2179.
  • Wasesa, M., Ramadhan, F. I., Nita, A., Belgiawan, P. F., & Mayangsari, L. (2021). Impact of overbooking reservation mechanism on container terminal’s operational performance and greenhouse gas emissions. The Asian Journal of Shipping and Logistics.
  • Watzek, J., Hauber, M. E., Jack, K. M., Murrell, J. R., Tecot, S. R., & Brosnan, S. F. (2021). MODELLING Collective Decision-Making: Insights Into COLLECTIVE anti-predator Behaviors From AN Agent-Based APPROACH. Behavioural Processes, 104530.
  • Webster, R. (2021). Dynamic Causal Inference Using a Hardware Implementation of Spiking Neurons (Doctoral dissertation, University of Otago).
  • Wens, M. L., van Loon, A. F., Veldkamp, T. I., & Aerts, J. C. (2021). Education, financial aid and awareness can reduce smallholder farmers’ vulnerability to drought under climate change. Natural Hazards and Earth System Sciences Discussions, 1-36.
  • White, A. L., & Gaff, H. D. (2021). Application and Modeling of a Tick-Killing Robot, TickBot. In The Mathematics of Patterns, Symmetries, and Beauties in Nature (pp. 31-57). Springer, Cham.
  • Widiyanto, S., Adi, D., Nurdin, N., & Fadila, F. (2021). Agent-Based Simulation for Evaluating the Effect of Different Walking and Driving Speed on Disaster Evacuation in Aceh. IPTEK The Journal of Engineering, 7(2), 50-58.
  • Will, M., Groeneveld, J., Frank, K., & Müller, B. (2021). Informal risk-sharing between smallholders may be threatened by formal insurance: Lessons from a stylized agent-based model. PloS one, 16(3), e0248757.
  • Williams, Timothy. Agent-Based Modeling of Resilience in Smallholder Agriculture: Toward Robust Models and Equitable Outcomes. Diss. 2021.
  • Wilsdorf, P., Wolpers, A., Hilton, J., Haack, F., & Uhrmacher, A. M. (2021). Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns. arXiv preprint arXiv:2109.06776.
  • Wojcieszak, M., Sobkowicz, P., Yu, X., & Bulat, B. (2021). What Information Drives Political Polarization? Comparing the Effects of In-group Praise, Out-group Derogation, and Evidence-based Communications on Polarization. The International Journal of Press/Politics, 19401612211004418.
  • Wooditch, A. (2021). The Benefits of Patrol Officers Using Unallocated Time for Everyday Crime Prevention. Journal of Quantitative Criminology, 1-25.
  • Wu, A. S., Mathias, H. D., Giordano, J. P., & Pherwani, A. (2021). Collective control as a decentralized task allocation testbed.
  • Wu, H., & Zhou, Y. (2021). Optimal degree of openness in open innovation: A perspective from knowledge acquisition & knowledge leakage. Technology in Society, 67, 101756.
  • Wu, R., Wang, Z., & Shi, Q. (2021). Increment of Heterogeneous Knowledge in Enterprise Innovation Ecosystem: An Agent-Based Simulation Framework. Complexity, 2021.
  • Wu, S., Wang, X., & Su, J. (2021). Statistical analysis of the community lockdown for COVID-19 pandemic. Applied Intelligence, 1-18.
  • Wu, S., Horn, M., & Wilensky, U. (Apr 2021). Positioning teachers as co-designers to integrate CT practices in STEM. Related Paper-Set on Integrating Computational Thinking in Science Curricula: Teacher Professional Development and Student Assessment. NARST Annual Conference (NARST 2021). Online Presentation.
  • Wu, S. P. W., Peel, A., Bain, C., Horn, M. S. & Wilensky, U. (2021). Different Paths, Same Direction: How Teachers Learn Computational Thinking in STEM Practices through Professional Development. In Looi, C.K., Wadhwa, B., Dagiené, V., Seow, P., Kee, Y.H., & Wu, L.K. (Eds.) Proceedings of the 5th APSCE International Computational Thinking and Stem in Education Conference (CTE) (pp.52-58).
  • Wu, S., Jones, B. (*), Swanson, H., Horn, M., Wilensky, U. (2021). A Tale of Two PDs: Exploring Teachers' Experiences in Co-designing Computational Activities. Proceedings of the 15th International Conference of the Learning Sciences. Bochum, Germany: International Society of the Learning Sciences.
  • Wu, S. P. W., Anton, G., Bain, C., Peel, A. N., Horn, M. S., & Wilensky, U. (2021). Tools and resources for integrating computational thinking into your science classes. Presented at the NSTA National Conference 2021, Chicago, IL.
  • Xiang, F., Chen, K., Su, J., Liu, H., & Zhang, W. (2021). Penetration Planning and Design Method of Unmanned Aerial Vehicle Inspired by Biological Swarm Intelligence Algorithm. Wireless Communications and Mobile Computing, 2021.
  • Xiang, L., Shen, G. Q., Li, D., Tan, Y., & Jin, X. (2021). A Multi-Agent Platform to Explore Strategies for Age-Friendly Community Projects in Urban China. The Gerontologist.
  • Xiong, M., Wang, Y., & Cheng, Z. (2021, December). Research on Modeling and Simulation of Information Cocoon Based on Opinion Dynamics. In 2021 The 9th International Conference on Information Technology: IoT and Smart City (pp. 161-167).
  • Xu, L., Mak, S., & Brintrup, A. (2021). Will bots take over the supply chain? Revisiting Agent-based supply chain automation. International Journal of Production Economics, 108279.
  • Xue, X., Chen, F., Zhou, D., Wang, X., Lu, M., & Wang, F. Y. (2021). Computational Experiments for Complex Social Systems--Part I: The Customization of Computational Model. IEEE Transactions on Computational Social Systems.
  • Yadav, A., & Berthelsen, U. D. (Eds.). (2021). Computational Thinking in Education: A Pedagogical Perspective. Routledge.
  • Yang, J. S. (2021). Dynamics of Firm’s Investment in Education and Training: An Agent-based Approach. Computational Economics, 1-35.
  • Yao, H., Jiang, Y., & Yang, R. (2021). Reconstruction Method of Landscape Planning Mode based on VR Technology and Wireless Communication Technology.
  • Yletyinen, J., Perry, G. L. W., Stahlmann-Brown, P., Pech, R., & Tylianakis, J. M. (2021). Multiple social network influences can generate unexpected environmental outcomes. Scientific Reports, 11(1), 1-14.
  • Yoon, S. A. (2021). Complex Systems Research in K12 Science Education: A Focus on What Works for Whom and under Which Conditions.
  • Yuan, F. (2021). Smart city next-gen social networks system based on software reconstruction model and cognitive computing. Social Network Analysis and Mining, 11(1), 1-14.
  • Yu, J. (2021). Evolving autonomous agents with simulated brains using L2L and Netlogo (No. FZJ-2022-00853). Jülich Supercomputing Center.
  • Yu, X., Nian, F., Yao, Y., & Luo, L. (2021). Phase Transition in Group Emotion. IEEE Transactions on Computational Social Systems.
  • Yu, Y., Yazan, D. M., Bhochhibhoya, S., & Volker, L. (2021). Towards Circular Economy Through Industrial Symbiosis in the Dutch Construction Industry: A Case of Recycled Concrete Aggregates. Journal of Cleaner Production, 126083.
  • Zagaria, C., Schulp, C. J., Zavalloni, M., Viaggi, D., & Verburg, P. H. (2021). Modelling transformational adaptation to climate change among crop farming systems in Romagna, Italy. Agricultural Systems, 188, 103024.
  • Zander, S. (2021). Wirkungsgefüge für einen systemischen Zugang zum mathematischen Modellieren nutzen. In Neue Materialien für einen realitätsbezogenen Mathematikunterricht 8 (pp. 119-132). Springer Spektrum, Wiesbaden.
  • Zanker, M., Bureš, V., & Tučník, P. (2021). Environment, Business, and Health Care Prevail: A Comprehensive, Systematic Review of System Dynamics Application Domains. Systems, 9(2), 28.
  • Zhang, G., Li, H., He, R., & Lu, P. (2021). Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions. Complex & Intelligent Systems, 1-19.
  • Zhang, J. (2021). Agent-Based Optimizing Match Between Passenger Demand and Service Supply for Urban Rail Transit Network With NetLogo. IEEE Access, 9, 32064-32080.
  • Zhang, J. (2021). Is competition sufficient to drive observed retail location and revenue patterns? An agent-based case study (Master's thesis, University of Waterloo).
  • Zhang, J., & Robinson, D. T. (2021). Replication of an agent-based model using the Replication Standard. Environmental Modelling & Software, 105016.
  • Zhang, T., Dong, P., Zeng, Y., & Ju, Y. (2022). Analyzing the diffusion of competitive smart wearable devices: An agent-based multi-dimensional relative agreement model. Journal of Business Research, 139, 90-105.
  • Zheng, C. (2021). Evolutionary Game Analysis of Knowledge Sharing in Low-Carbon Innovation Network. Complexity, 2021.
  • Zheng, G., Gong, B., & Zhang, Y. (2021). Dynamic Network Security Mechanism Based on Trust Management in Wireless Sensor Networks. Wireless Communications and Mobile Computing, 2021.
  • Zhong C. (2021). Adaptive Active Immune Policy for Sensor Nodes in the Internet of Things. Advances in Artificial Intelligence and Security Cham 2021. Springer International Publishing Pages: 514-525.
  • Zhou, Q. (2021). Detecting the public’s information behaviour preferences in multiple emergency events. Journal of Information Science, 01655515211027789.
  • Zhou, S., Fu, H., Tao, S., Han, Y., & Mao, M. (2021). Bridging the top-down and bottom-up approaches to smart urbanization? A reflection on Beijing’s Shuangjing International Sustainable Development Community Pilot. International Journal of Urban Sciences, 1-23.
  • Zhu, L., Ding, C., Zhu, T., & Wang, Y. (2021). A review on the forward osmosis applications and fouling control strategies for wastewater treatment. Frontiers of Chemical Science and Engineering, 1-20.
  • Zhu, Z., Yi, W., Ruifeng, F., Liya, L., & Meng, Q. Modeling on Anti-UAV System-of-systems Combat OODA Loop Based on NetLogo. Journal of System Simulation, 33(8), 1791.
  • Zia, K., Farooq, U., & Al Ajmi, S. (2021). Finding the Impact of Market Visibility and Monopoly on Wealth Distribution and Poverty Using Computational Economics. Computational Economics, 1-25.
  • Zolfagharipoor, M. A., & Ahmadi, A. (2021). Agent-based modeling of participants' behaviors in an inter-sectoral groundwater market. Journal of Environmental Management, 299, 113560.
  • Zong, X., Liu, A., Wang, C., Ye, Z., & Du, J. (2021, September). Indoor evacuation model based on visual-guidance artificial bee colony algorithm. In Building Simulation (pp. 1-14). Tsinghua University Press.
  • Zouhri, S., El Baroudi, M., & Saadi, S. (2021). Agent-Based Model for Proteins Interaction inside Cancer Cell. American Journal of Computational and Applied Mathematics, 11(2), 42-50.
  • Züfle, A., Wenk, C., Pfoser, D., Crooks, A., Kim, J. S., Kavak, H., ... & Jin, H. (2021). Urban life: a model of people and places. Computational and Mathematical Organization Theory, 1-32.
  • Zuo, Y., & Zhao, X. (2021). Effects of herding behavior of tradable green certificate market players on market efficiency: insights from heterogeneous agent model. Frontiers in Energy, 1-20.
  • Zia, K., Farooq, U., Shafi, M., & Ferscha, A. (2021). On the effectiveness of multi-feature evacuation systems: an agent-based exploratory simulation study. PeerJ Computer Science, 7, e531.
  • Zijlstra, T. W., de Vries, H., & Sterck, E. H. (2021). Emotional bookkeeping and differentiated affiliative relationships: Exploring the role of dynamics and speed in updating relationship quality in the EMO-model. PloS one, 16(4), e0249519.
  • Zohar, A. R., & Levy, S. T. (2021). From feeling forces to understanding forces: The impact of bodily engagement on learning in science. Journal of Research in Science Teaching, 1– 36.
  • Zouhri, S., El Baroudi, M., & Saadi, S. (2021). Agent-Based Model for Proteins Interaction inside Cancer Cell. American Journal of Computational and Applied Mathematics, 11(2), 42-50.
  • Zuccotti, C. V., Lorenz, J., Paolillo, R., Sánchez, A. R., & Serka, S. (2021). Exploring the dynamics of neighborhood ethnic segregation with agent-based modelling: an empirical application to Bradford.
  • Ильинский, А. И. (2021). АГЕНТНОЕ МОДЕЛИРОВАНИЕ РАЗВИТИЯ СЛОЖНОЙ НАЛОГОВОЙ ЭКОСИСТЕМЫ В СЛУЧАЕ РАЗМЫВАНИЯ НАЛОГОВОЙ БАЗЫ ПРИ ВНЕДРЕНИИ SUPTECH И REGTECH. Хроноэкономика, (4 (32)), 60-63.
  • Волобуев, Н. А., Гайдамашко, И. В., Грошев, И. В., Логинов, Е. Л., Эриашвили, Н. Д., & Шкута, А. А. (2021). Информационно-цифровая детерминация учебного процесса в современных условиях информационного общества и глобальных коммуникаций. Образование. Наука. Научные кадры, (4), 245-249.
  • Бурова, А. А., Буров, С. С., Парыгин, Д. С., Финогеев, А. Г., & Смирнова, Т. В. (2021). ПАНЕЛЬ АДМИНИСТРИРОВАНИЯ ПЛАТФОРМЫ МНОГОАГЕНТНОГО МОДЕЛИРОВАНИЯ С ВОЗМОЖНОСТЬЮ ПОСТРОЕНИЯ ГРАФИЧЕСКИХ ОТЧЕТОВ. International Journal of Open Information Technologies, 9(12), 4-14.
  • Петров, А. П. Ч., Ахременко, А. С., Жеглов, С. А., & Кручинская, Е. В. (2021). Is Network Structure Important for Protest Mobilization? Findings from Agent-Based Modeling. Мониторинг общественного мнения: экономические и социальные перемены, (6).
  • 郑荣, 王晓宇, & 张艺源. (2021). 基于 ACP 理论的企业竞争情报智能系统构建研究. 情报理论与实践, 44(12), 148.
  • 陈榕, & 吴才琴. (2021). 新冠肺炎疫情防控措施效果仿真研究. 台州学院学报.
  • 张爽. (2021). 珠海市香洲城区洪涝灾害模拟及行车影响仿真 (Master's thesis, 河北工程大学).

2020

  • Abdalbaki, S. M. (2020). A cellular automata modelling approach in household water use. Journal of Water, Sanitation and Hygiene for Development.
  • Abdulsattar, H., Siam, M. R. K., & Wang, H. (2020). Characterisation of the impacts of autonomous driving on highway capacity in a mixed traffic environment: an agent-based approach.
  • Abubakar, H., Yusuf, S., & Abdurrahman, Y. (2020). Discrete Artificial Dragonflies Algorithm in Agent Based Modelling for Exact Boolean kSatisfiability Problem. Journal of Advances in Mathematics and Computer Science, 115-134.
  • Accolla, C., Vaugeois, M., Grimm, V., Moore, A. P., Rueda‐Cediel, P., Schmolke, A., & Forbes, V. E. (2020). A review of key features and their implementation in unstructured, structured, and agent‐based population models for ecological risk assessment. Integrated Environmental Assessment and Management.
  • Adamatti, D. F. Circadian Rhythm and Pain: Mathematical Model Based on Multiagent Simulation. In Ambient Intelligence–Software and Applications: 11th International Symposium on Ambient Intelligence (Vol. 1, p. 309). Springer Nature.
  • Adamatti, D. F. Development of a Multiagent Simulator to Genetic Regulatory Networks. In Ambient Intelligence–Software and Applications: 11th International Symposium on Ambient Intelligence (p. 279). Springer Nature.
  • Aghaie, V., Alizadeh, H., & Afshar, A. (2020). Emergence of social norms in the cap-and-trade policy: An agent-based groundwater market. Journal of Hydrology, 125057.
  • Ahmed, N., Alo, R., Amelink, C., Baek, Y. Y., Chudhary, A., Collins, K., ... & Kenyon, R. (2020). net. science: A Cyberinfrastructure for Sustained Innovation in Network Science and Engineering.
  • Akwafuo, S. E., Abah, T., & Oppong, J. R. (2020). Evaluation of the Burden and Intervention Strat-effigies of TB-HIV Co-Infection in West Africa. J Infect Dis Epidemiol, 6, 143.
  • Alaghband, M., & Garibay, I. (2020). Effects of Non-Cognitive Factors on Post-Secondary Persistence of Deaf Students: An Agent-Based Modeling Approach. arXiv preprint arXiv:2006.12624.
  • Al Barghuthi, N. B., & Togher, M. (2020, March). Analysis of Frameworks for Traffic Agent Simulations. In International Symposium on Intelligent Computing Systems (pp. 44-54). Springer, Cham.
  • Aljarah, R., & Mahmood, B. (2020, February). Towards the Impact of Mobility Patterns on Network Resources in Smart Cities. In 2020 6th International Engineering Conference “Sustainable Technology and Development"(IEC) (pp. 126-130). IEEE.
  • Aljumah, A., Kaur, A., Bhatia, M., & Ahamed Ahanger, T. (2020). Internet of things‐fog computing‐based framework for smart disaster management. Transactions on Emerging Telecommunications Technologies, e4078.
  • Al-Khulaidy, A., & Swartz, M. (2020, May). Along the border: an agent-based model of migration along the United States-Mexico border. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-12).
  • Alm, J., Gerbrands, P., & Kirchler, E. (2020). Using "responsive regulation" to reduce tax base erosion. Regulation & Governance.
  • Al-Madhlom, Q., Al-Ansari, N., Hamza, B. A., Laue, J., & Hussain, H. M. (2020). Seepage Velocity: Large Scale Mapping and the Evaluation of Two Different Aquifer Conditions (Silty Clayey and Sandy). Hydrology, 7(3), 60.
  • Alonso Vicario, S., Mazzoleni, M., Bhamidipati, S., Gharesifard, M., Ridolfi, E., Pandolfo, C., & Alfonso, L. (2020). Unravelling the influence of human behaviour on reducing casualties during flood evacuation. Hydrological Sciences Journal.
  • Alsassa, S., Lefèvre, T., Laugier, V., Stindel, E., & Ansart, S. (2020). Modeling Early Stages of Bone and Joint Infections Dynamics in Humans: A Multi-Agent, Multi-System Based Model. Frontiers in molecular biosciences, 7, 26.
  • Al Shamsi, A. A. (2020). School Auditorium Evacuation Simulation. International Journal of Information Technology and Language Studies, 4(2).
  • Alvarado, M., & Arroyo, R. (2020). Cancer Metastasis and the Immune System Response: CM-IS Modeling by Ising Model. Research in Computing Science, 149(5), 123-129.
  • Amadei, B. (2020). Agent-Based and System Dynamics Modeling of Water Field Services. Challenges, 11(2), 13.
  • Amir, S., Asif, F. M., & Roci, M. (2020). Towards Circular Economy: Enhanced Decision-Making in Circular Manufacturing Systems. In Sustainable Consumption and Production, Volume II (pp. 257-279). Palgrave Macmillan, Cham.
  • An, S., Bates, R., Hammock, J., Rugaber, S., Weigel, E., & Goel, A. (2020). Scientific Modeling Using Large Scale Knowledge. In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., & Millán, E. (eds), Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12164. Springer, Cham.
  • Anderson, E., & Wendel, D. (2020). Learning Science Through Coding: An Investigation Into the Design of a Domain Specific Modeling Experience. The Interdisciplinarity of the Learning Sciences.
  • Anderson, S., & Anderson, S. D. (2020, June). Coding and Music Creation in a Multi-Agent Environment. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 527-528).
  • Anderson, T., Leung, A., Dragicevic, S., & Perez, L. (2020). Modeling the geospatial dynamics of residential segregation in three Canadian cities: An agent‐based approach. Transactions in GIS.
  • Anderson, T., Leung, A., Perez, L., & Dragićević, S. (2020). Investigating the Effects of Panethnicity in Geospatial Models of Segregation. Applied Spatial Analysis and Policy, 1-23.
  • Angenendt, M., Bäcker, A., Merten, H., Müller, M. M., Zons, G., Sokolov, E., ... & Symposion, P. The impact of the opposition in established democracies.
  • Angourakis, A., Bates, J., Baudouin, J. P., Giesche, A., Ustunkaya, M. C., Wright, N., ... & Petrie, C. A. (2020). How to ‘downsize’a complex society: an agent-based modelling approach to assess the resilience of Indus Civilisation settlements to past climate change. Environmental Research Letters.
  • Aniruddha, B., & Abi Tamim, V. (2020). Modelling the challenges of managing free-ranging dog populations. Scientific Reports (Nature Publisher Group), 10(1).
  • Anne, L., Anandakumar, S., Mahendran, A., Ghalib, M. R., & Ghosh, U. A. Study and Analysis of Trust Management System in Cloud Technologies. In Applications of Artificial Intelligence for Smart Technology (pp. 220-232). IGI Global.
  • Antczak, T., Weron, R., & Zabawa, J. (2020). Data-driven simulation modeling of the checkout process in supermarkets: Insights for decision support in retail operations (No. WORMS/20/16). Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
  • Aqib, M., & Ukil, A. (2020, November). Modelling of electric vehicle charging and discharging profile to mimic real life scenario at charging stations. In 2020 IEEE REGION 10 CONFERENCE (TENCON) (pp. 501-505). IEEE.
  • Aragón, A., Gaither, M. J., & Madden, M. (2020). MIXED GEOSPATIAL METHODS BASELINE STUDY TO EVALUATE AND MODEL GENTRIFICATION ALONG THE WESTSIDE ATLANTA BELTLINE, USA. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 527-531.
  • Araújo, E., & Gerritsen, C. (2020). Creating a temporal pattern for street robberies using ABM and data from a small city in South East Brazil. Agent-Based Modelling for Criminological Theory Testing and Development, 146.
  • Araújo, M. S., da Silva, T. F., Sampaio, V. A., Melo, G. F., Junior, R. J. F., da Costa, L. F., ... & de Campos, G. A. (2020, October). Cooperative Observation of Smart Target Agents. In Brazilian Conference on Intelligent Systems (pp. 77-92). Springer, Cham.
  • Araujo-Granda, P., Gras, A., Ginovart, M., & Moulton, V. (2020). INDISIM-Denitrification, an individual-based model for study the denitrification process. Journal of industrial microbiology & biotechnology, 47(1), 1-20.
  • Arden, R., Ruseno, N., & Hidayat, Y. A. (2020). CARGO OPTIMIZATION IN AN AIRLINE USING AGENT–BASED MODELLING.
  • Arnould-Pétré, M., Guillaumot, C., Danis, B., Féral, J. P., & Saucède, T. Individual-based model of population dynamics in a sea urchin of the Kerguelen Plateau (Southern Ocean), Abatus cordatus, under changing environmental conditions. Ecological Modelling, 440, 109352.
  • Asgharpourmasouleh, A., Fattahzadeh, M., Mayerhoffer, D., & Lorenz, J. (2020). On the Fate of Protests: Dynamics of Social Activation and Topic Selection Online and in the Streets. In Computational Conflict Research (pp. 141-164). Springer, Cham.
  • Ashiku, L., & Dagli, C. (2020). Agent Based Cybersecurity Model for Business Entity Risk Assessment. In 2020 IEEE International Symposium on Systems Engineering (ISSE) (pp. 1-6). IEEE.
  • Aslan, U., LaGrassa, N., Horn, M., & Wilensky, U. (2020). Putting the Taxonomy into Practice: Investigating Students’ Learning of Chemistry with Integrated Computational Thinking Activities. Paper presented at the American Education Research Association (AERA) Conference. San Francisco, CA.
  • Assa, J., & Lengfelder, C. (2020). Can Enhancing Capabilities Promote Energy Justice? An Agent-Based Model Approach. Mendeley Data, 1.
  • Augustijn, E. W., Abdulkareem, S. A., Sadiq, M. H., & Albabawat, A. A. (2020, April). Machine Learning to Derive Complex Behaviour in Agent-Based Modellzing. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 284-289). IEEE.
  • Azim, M. A., Sathasivam, S., Alzaeemi, S. A. S., & Mahmood, M. (2019). Agent Based Modeling for Comparing the Performances of Hyperbolic and Zeng and Martinez Activations Functions. International Journal of Computer Networks and Communications Security, 7(12), 250-257.
  • Azucena, J., Alkhaleel, B., Liao, H., & Nachtmann, H. (2020). Hybrid simulation to support interdependence modeling of a multimodal transportation network. Simulation Modelling Practice and Theory, 102237.
  • Baccega, D. (2020). An agent-based model to support SARS-CoV-2 infection control strategies at school.
  • Badham, J., Kee, F., & Hunter, R. F. (2020). Network structure influence on simulated network interventions for behaviour change. Social Networks, 64, 55-62.
  • Baena, B., Cobian, C., Larios, V. M., Orizaga, J. A., Maciel, R., Cisneros, M. P., & Beltran-Ramirez, J. R. (2020). Adapting food supply chains in Smart Cities to address the impacts of COVID19 a case study from Guadalajara metropolitan area. In 2020 IEEE International Smart Cities Conference (ISC2) (pp. 1-8). IEEE.
  • Baker, E., Barbillon, P., Fadikar, A., Gramacy, R. B., Herbei, R., Higdon, D., ... & Sacks, J. (2020). Stochastic Simulators: An Overview with Opportunities. arXiv preprint arXiv:2002.01321.
  • Bai, S. (2020). Simulations of COVID-19 spread by spatial agent-based model and ordinary differential equations. International Journal of Simulation and Process Modelling, 15(3), 268-277.
  • Bain, C., & Wilensky, U. (2020, February). Vectors of CT-ification: Integrating Computational Activities in STEM Classrooms. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 1361-1361).
  • BAIN, C., DABHOLKAR, S., & WILENSKY, U. (2020). Confronting Frame Alignment in CT Infused STEM Classrooms. CoolThink@ JC, 91.
  • Bain, C., Anton, G., Horn, M., Wilensky, U. (2020). Back to Computational Transparency: Co-design with Teachers to Integrate Computational Thinking in Science Classrooms. Proceedings of the International Conference for the Learning Sciences (ICLS 2020), Nashville, USA: ISLS.
  • Bampoh, D. K., Earl, J. E., & Zollner, P. A. (2020). Simulating the relative effects of movement and sociality on the distribution of animal-transported subsidies. Theoretical Ecology, 1-14.
  • Bano, A., Ud Din, I., & Al-Huqail, A. A. (2020). AIoT-Based Smart Bin for Real-Time Monitoring and Management of Solid Waste. Scientific Programming, 2020.
  • Bao, H., Dong, H., Jia, J., Peng, Y., & Li, Q. (2020). Impacts of land expropriation on the entrepreneurial decision-making behavior of land-lost peasants: An agent-based simulation. Habitat International, 95, 102096.
  • Barabashev, A. G. (2020). Constructive ending: how to finalize the conclusion and discussion of a research project and a journal article. Handbook of Research Methods in Public Administration, Management and Policy, 395.
  • Baral, N., Gunaratne, C., Jayalath, C., Rand, W., Senevirathna, C., & Garibay, I. (2020). Negative Influence Gradients Lead to Lowered Response Capacity on Social Networks.
  • Barazza, E., & Strachan, N. (2020). The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors. Energy Research & Social Science, 65, 101458.
  • Barazza, E., & Strachan, N. The key role of historic path-dependency and competitor imitation on the electricity sector low-carbon transition. Energy Strategy Reviews, 33, 100588.
  • Barbet, V., Bourlès, R., & Rouchier, J. (2020). Informal risk-sharing cooperatives: the effect of learning and other-regarding preferences. Journal of Evolutionary Economics, 1-28.
  • Barbosa, P., Schumaker, N. H., Brandon, K. R., Bager, A., & Grilo, C. (2020). Simulating the consequences of roads for wildlife population dynamics. Landscape and Urban Planning, 193, 103672.
  • Barker, A. K., Scaria, E., Alagoz, O., Sethi, A. K., & Safdar, N. (2020). Reducing C. difficile in children: An agent-based modeling approach to evaluate intervention effectiveness. Infection Control & Hospital Epidemiology, 1-9.
  • Barthelemy, J., Amirghasemi, M., Arshad, B., Fay, C., Forehead, H., Hutchison, N., ... & Perez, P. (2020). Problem-Driven and Technology-Enabled Solutions for Safer Communities: The case of stormwater management in the Illawarra-Shoalhaven region (NSW, Australia). Handbook of Smart Cities, 1-28.
  • Bartlett, T. (2020). Privacy and Security Management Practices of Emerging Technologies: Internet of Things (Doctoral dissertation, Robert Morris University).
  • Bassi, F., Bauermann, T., Lang, D., & Setterfield, M. (2020). Is capacity utilization variable in the long run? An agent-based sectoral approach tomodeling hysteresis in the normal rate of capacity utilization.
  • Basu, D., & Panorkou, N. (2020, January). Utilizing mathematics to examine sea level rise as an environmental and a social issue. In Mathematics Education Across Cultures: Proceedings of the 42nd Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education.
  • Bautista-Torres, A., Bautista-Aleman, E. A., & Espitia-Cuchango, H. E. (2020). Cellular Automata Implemented on FPGA Based on Totalistic Rules for Deterministic Systems.
  • Beckedorf, J., Hartung, D., & Sittig, P. (2020). 15. Analyzing high volumes of German court decisions in an interdisciplinary class of law and computer science students. Computational Legal Studies: The Promise and Challenge of Data-Driven Research, 328.
  • Bedar, R. A. H., & Al-Shboul, M. (2020). The Effect of Using STEAM Approach on Developing Computational Thinking Skills among High School Students in Jordan. International Journal of Interactive Mobile Technologies, 14(14).
  • Belgrad, B. A., & Griffen, B. D. (2020). Which mechanisms are responsible for population patterns across different quality habitats? A new approach. Oikos.
  • Bell, A. (2020). Two Approaches to Teaching with NetLogo: Examining the Role of Structure and Agency.
  • Belsare, A. V., Gompper, M. E., Keller, B., Sumners, J., Hansen, L., & Millspaugh, J. J. (2020). An agent-based framework for improving wildlife disease surveillance: A case study of chronic wasting disease in Missouri white-tailed deer. Ecological Modelling, 417, 108919.
  • Belsare, A., Gompper, M., Keller, B., Sumners, J., Hansen, L., & Millspaugh, J. (2020). Size Matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling framework. MethodsX, 100953.
  • Belsare, A. V., & Stewart, C. M. (2020). OvCWD: An agent‐based modeling framework for informing chronic wasting disease management in white‐tailed deer populations. Ecological Solutions and Evidence, 1(1).
  • Berger, C., & Mahdavi, A. (2020). Review of current trends in agent-based modeling of building occupants for energy and indoor-environmental performance analysis. Building and Environment, 173, 106726.
  • Bezzaoucha, F. S., Sahnoun, M. H., & Benslimane, S. M. (2020). Multi-component modeling and classification for failure propagation of an offshore wind turbine. International Journal of Energy Sector Management.
  • Biagetti, A., Ferrando, A., & Mascardi, V. (2020, October). The DigForSim Agent Based Simulator of People Movements in Crime Scenes. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 42-54). Springer, Cham.
  • Bidoki, N. H., Schiappa, M., Sukthankar, G., & Garibay, I. (2020). Modeling social coding dynamics with sampled historical data. Online Social Networks and Media, 16.
  • Bielik, T., Fonio, E., Feinerman, O., Duncan, R. G., & Levy, S. T. (2020). Working Together: Integrating Computational Modeling Approaches to Investigate Complex Phenomena. Journal of Science Education and Technology, 1-18.
  • Biermann, L. (2020). Assessing the impacts of human disturbance on wildlife: insights from wildfowl on the Exe Estuary (Doctoral dissertation, Bournemouth University).
  • Bilsborrow, R. E. (2020). Economic and Related Aspects of Land Use on Islands: A Meta Perspective. In Land Cover and Land Use Change on Islands (pp. 11-62). Springer, Cham.
  • Bina, K., & Moghadas, N. (2020). BIM-ABM simulation for emergency evacuation from conference hall, considering gender segregation and architectural design. Architectural Engineering and Design Management, 1-15.
  • Binhomaid, O., & Hegazy, T. (2020). Agent-based Simulation of Workers’ Behaviors, Productivity, and Safety around Construction Obstacles. Canadian Journal of Civil Engineering, (ja).
  • Bodine, E. N., Panoff, R. M., Voit, E. O., & Weisstein, A. E. (2020). Agent-Based Modeling and Simulation in Mathematics and Biology Education. Bulletin of Mathematical Biology, 82(8), 1-19.
  • Bologov, A. (2020). Assessement of the Ability of Elective Choice System. Review of Business and Economics Studies, (3).
  • Bommel, P. (2020). Participatory modelling and interactive simulation to support the management of the commons (Doctoral dissertation, Université de Montpellier).
  • Borer, B. (2020). A marriage made in soil-quantifying bacterial life in soil hotspots using individual-based and metabolic network modeling (Doctoral dissertation, ETH Zurich).
  • Bosse, S. (2020, September). Self-organising Urban Traffic Control on Micro-level Using Reinforcement Learning and Agent-Based Modelling. In Proceedings of SAI Intelligent Systems Conference (pp. 745-764). Springer, Cham.
  • Boukehila, A., & Taleb, N. (2020, February). Statistical Study To Detect Emergent Behaviours. In 2020 2nd International Conference on Mathematics and Information Technology (ICMIT) (pp. 164-168). IEEE.
  • Boyd, R., Walker, N., Hyder, K., Thorpe, R., Roy, S., & Sibly, R. (2020). SEASIM-NEAM: a Spatially-Explicit Agent-based SIMulator of NorthEast Atlantic Mackerel population dynamics. MethodsX, 101044.
  • Brady, C., Gresalfi, M., Steinberg, S., & Knowe, M. (2020). Debugging for Art’s Sake: Beginning Programmers’ Debugging Activity in an Expressive Coding Context. The Interdisciplinarity of the Learning Sciences.
  • Brady, C., Stroup, W. M., Petrosino, A. & Wilensky, U. J. (2020) Amplifying the Restructuration Potential of Agent-Based Modeling Through Group-Based Activity Structures [Symposium]. AERA Annual Meeting San Francisco, CA http://tinyurl.com/szuaxl3 (Conference Canceled)
  • Brahmbhatt, M., & Sonar, S. (2020). Transit Time Comparison of Different Modes of Transportation. Studies in Indian Place Names, 40(9), 88-93.
  • Brainard, J., Hunter, P. R., & Hall, I. R. (2020). An agent-based model about the effects of fake news on a norovirus outbreak. Revue d'Épidémiologie et de Santé Publique.
  • Braun, B., Taraktaş, B., Beckage, B., & Molofsky, J. (2020). Phase transitions and social distancing control measures for SARS-CoV-2 on small world networks. arXiv preprint arXiv:2005.09751.
  • Braun, B., Taraktaş, B., Beckage, B., & Molofsky, J. (2020). Simulating phase transitions and control measures for network epidemics caused by infections with presymptomatic, asymptomatic, and symptomatic stages. PLOS ONE, 15(9), e0238412.
  • Brearcliffe, D. (2020). Non-Pharmaceutical Herd Immunity using Homemade Masks (No. 4432). EasyChair.
  • Brearcliffe, D., & Crooks, A. (2020). Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning (No. 4403). EasyChair.
  • Breen, C. D., & Frezza, S. (2020). Charismatic Leadership and the Formation of Hate Groups. International Annals of Criminology, 1-36.
  • Brignone, S., Grimaldi, R., Denicolai, L., & Palmieri, S. (2020). Intelligenza artificiale, robot e rappresentazione della conoscenza. Il Laboratorio di simulazione del comportamento e robotica educativa" Luciano Gallino".
  • Brodskiy, V. A., Pimenov, D. M., Chernov, P. L., Dzhamaldinova, M. D., & Kurdyukova, N. O. (2020). A Review of Agent-Based Modeling in the Cooperative Sector of Economics. Frontier Information Technology and Systems Research in Cooperative Economics, 261-268.
  • Broniec, W., An, S., Rugaber, S., & Goel, A. K. (2020). Using VERA to explain the impact of social distancing on the spread of COVID-19. arXiv preprint arXiv:2003.13762.
  • Brughmans, T. (2020). Evaluating the Potential of Computational Modelling for Informing Debates on Roman Economic Integration. In Complexity Economics (pp. 105-123). Palgrave Macmillan, Cham.
  • Buechley, L. Self-Directed Constructionist Communities: Interview with Leah Buechley. In Holbert, N., Berland, M., & Kafai, Y. B. (eds.), Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs, 381.
  • Buenaventura, A., Calgo, C. J., Bardeloza, D. K. D., Libatique, N. J. C., & Tangonan, G. L. (2020). Agent-based modeling of the spread of fire in urban settlements in the Philippines. Proceedings of the Samahang Pisika ng Pilipinas.
  • Buhat, C. A. H., Lutero, D. S., Olave, Y. H., Torres, M. C., & Rabajante, J. F. (2020). Modeling the Transmission of Respiratory Infectious Diseases in Mass Transportation Systems. medRxiv.
  • Buhat, C. A. H., Rabajante, J. F., & Paller, V. G. V. (2020). Spatiotemporal modeling of parasite aggregation among fish hosts in a lentic ecosystem. Modeling Earth Systems and Environment, 1-17.
  • Buhat, C. A., & Villanueva, S. K. (2020). Determining the effectiveness of practicing non-pharmaceutical interventions in improving virus control in a pandemic using agent-based modelling. Mathematics in Applied Sciences and Engineering, 1(4), 423-438.
  • Bulson, L., Becher, M. A., McKinley, T. J., & Wilfert, L. (2020). Long‐term effects of antibiotic treatments on honeybee colony fitness–a modelling approach. Journal of Applied Ecology.
  • Burbach, L., Belavadi, P., Halbach, P., Kojan, L., Plettenberg, N., Nakayama, J., ... & Valdez, A. C. (2020, July). Netlogo vs. Julia: Evaluating Different Options for the Simulation of Opinion Dynamics. In International Conference on Human-Computer Interaction (pp. 3-19). Springer, Cham.
  • Cabrera, M. A. S., & Barrientos, A. H. (2020). Feasibility Study for Using Energy-Harvesting Floor in Urban Public Transportation System: Case of Subway Stations. Journal of Electrical Power & Energy Systems, 4(1), 11-21.
  • Cabrera-Becerril, A., Peralta, R., Miramontes, P., Vargas-de-Leon, C., & Alonso, R. (2020). Increase of non-vaccine human papillomavirustypes in a group of HPV-vaccinated Mexicanwomen. Evidence of Pathogenic StrainReplacement. medRxiv.
  • Caetano-Anollés, G., Mughal, F., Aziz, M. F., Koç, I., Caetano-Anollés, K., Caetano-Anollés, D., & Mittenthal, J. E. (2020). Linkage: A “double tale” of module creation in evolving networks. Untangling Molecular Biodiversity, pp. 91-168. https://doi.org/10.1142/9789814656627_0003
  • Calabrò, G., Inturri, G., Le Pira, M., Pluchino, A., & Ignaccolo, M. (2020). Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization. Transportation Research Procedia, 45, 234-241.
  • Calabrò, G., Torrisi, V., Inturri, G., & Ignaccolo, M. (2020). Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization. European Transport Research Review, 12, 1-11.
  • Calcagno, S. F., Mailleret, L., Malausa, T., & Vercken, E. (2020). Shifts from pulled to pushed range expansions.
  • Camargo, P., Mattos, S., & Goldenberg, C. (2020, February). Complexity and Collective Intelligence on Demand for a Sustainable Future. In 2020 IEEE 14th International Conference on Semantic Computing (ICSC) (pp. 347-349). IEEE.
  • Camp, J., Nelson, K., Philip, C. E., Moravec, M., Scheffler, D. W., & Johnson, P. (2020). Utilizing Agent-Based Modeling to Evaluate Operational Impacts of an Incident and Possible Alternatives on US Waterways. Transportation Research Record, 0361198120941504.
  • Camparotti, C. E. S. (2020). Analysis of industrial symbiosis through agent-based simulation: application in the agro-industrial sector.
  • Campos, R. F. D. A., Cunha, D. A. D., & Bueno, N. P. (2020). Information dissemination in socio-ecological systems: Analysis of a hybrid model of System Dynamics and Agent-Based Modeling. Nova Economia, 30(1), 257-286.
  • Caprioli, C., Bottero, M., & De Angelis, E. (2020). Supporting Policy Design for the Diffusion of Cleaner Technologies: A Spatial Empirical Agent-Based Model. ISPRS International Journal of Geo-Information, 9(10), 581.
  • Cárdenas González, L., & Soto Lozano, C. (2020). Modelación de un sistema inteligente de tráfico vehicular por medio de una simulación basada en agentes.
  • Cardinot, M. (2020). Coevolutionary spatial game theory: The impact of abstention. Small, 4(4), 1-7.
  • Carney, M., & Davies, B. (2020). Agent-Based Modeling, Scientific Reproducibility, and Taphonomy: A Successful Model Implementation Case Study.
  • Carpente, M. S., Guijarro-Berdiñas, B., Alonso-Betanzos, A., Rodríguez-Arias, A., & Dimitru, A. (2020). An Agent-Based Model to Simulate the Spread of a Virus Based on Social Behavior and Containment Measures. In Multidisciplinary Digital Publishing Institute Proceedings (Vol. 54, No. 1, p. 22).
  • Carrella, E., Bailey, R., & Madsen, J. (2020). Calibrating agent-based models with linear regressions. Journal Of Artificial Societies and Social Simulation, 23(1).
  • Carr-Markell, M. (2020). Effects of native prairie forbs on the foraging choices and recruitment behavior of honey bees (Apis mellifera).
  • Ceballos, Y. F., Galarcio-Noguera, J. D., Maya-Duque, P. A., & Ramirez-Cordoba, G. L. (2020). Agent-based Model for Environmental Awareness and Extended Producer Responsibility in Developing Countries. Scientia et Technica, 25(3), 430-437.
  • Centorrino, P., Corbetta, A., Cristiani, E., & Onofri, E. (2020). Managing Crowded Museums: Visitors Flow Measurement, Analysis, Modeling, and Optimization. arXiv preprint arXiv:2006.16830.
  • Chakarov, A. G. (2020). Integrating Computational Thinking into Middle School Science Curriculum Using Programmable Sensor Technologies (Doctoral dissertation, University of Colorado at Boulder).
  • Challenge, S., Jansens, R., Kingston, M., Landess, M., Morrison, B., Dubey, M., & Guerin, S. (2020). It’s ‘Bout To Get Lit Up In Here.
  • Chappin, É. J., Nikolic, I., & Yorke-Smith, N. (2020). Agent-based modelling of the social dynamics of energy end use. In Energy and Behaviour (pp. 321-351). Academic Press.
  • Chattoe-Brown, E. (2020). Why questions like ‘do networks matter?’matter to methodology: how Agent-Based Modelling makes it possible to answer them. International Journal of Social Research Methodology, 1-14.
  • Chen, B., Chen, H., Ning, D., Zhu, M., Ai, C., Qiu, X., & Dai, W. (2020). A Two-Tier Partition Algorithm for the Optimization of the Large-scale Simulation of Information Diffusion in Social Networks. Symmetry, 12(5), 843.
  • Chen, H. C., Han, Q., & De Vries, B. (2020). Modeling the spatial relation between urban morphology, land surface temperature and urban energy demand. Sustainable Cities and Society, 102246.
  • Chen, K., Li, Y., & Linderman, K. (2020). Supply Network Resilience Learning: An Exploratory Data Analytics Study.
  • Chen, S., He, Q., & Xiao, H. (2020). A study on cross-border e-commerce partner selection in B2B mode. Electronic Commerce Research, 1-21.
  • Chen, S., Wu, J., Pan, Y., Ge, J., & Huang, Z. (2020). Simulation and case study on residential stochastic energy use behaviors based on human dynamics. Energy and Buildings, 110182.
  • Chen, S., Zhang, H., Guan, J., & Rao, Z. (2020, March). Agent-based modeling and simulation of stochastic heat pump usage behavior in residential communities. In Building Simulation (pp. 1-19). Tsinghua University Press
  • Chen, Y., Chen, F., Lin, Z., & Pan, X. (2020). Comprehensive Evaluation of Underground Garage Traffic Design Scheme Based on Data Envelopment Analysis. In CICTP 2020 (pp. 4077-4088).
  • Cheng, C., Luo, Y., & Yu, C. (2020). Dynamic mechanism of social bots interfering with public opinion in network. Physica A: Statistical Mechanics and its Applications, 124163.
  • Chennoufi, M., & Bendella, F. (2020). Fuzzy controller and emotional model for evacuation of virtual crowd behaviors. Intelligent Decision Technologies, 14(2), 199-214.
  • Chennoufi, M., Bendella, F., & Bouzid, M. (2020). Best A* discovery for multi agents planning. International Journal of Operational Research, 38(3), 343-363.
  • CHIRIȚĂ, N., & NICA, I. (2020). Analysis of the impact generated by COVID-19 in banking institutions and possible economic effects. Theoretical and Applied Economics, 22(3 (624), Autumn), 21-40.
  • Christensen, C., & Salmon, J. (2020). An agent-based modeling approach for simulating the impact of small unmanned aircraft systems on future battlefields. The Journal of Defense Modeling and Simulation, 1548512920963904.
  • Christensen, D., & Lombardi, D. (2020). Understanding Biological Evolution Through Computational Thinking. Science & Education, 1-43.
  • Chudzinska, M., Dupont, Y. L., Nabe-Nielsen, J., Maia, K. P., Henriksen, M. V., Rasmussen, C., ... & Trøjelsgaard, K. (2020). Combining the strengths of agent-based modelling and network statistics to understand animal movement and interactions with resources: example from within-patch foraging decisions of bumblebees. Ecological Modelling, 430, 109119.
  • Chudzinska, M., Nabe-Nielsen, J., Smout, S., Aarts, G., Brasseur, S., Graham, I., ... & McConnell, B. AgentSeal: Agent-based model describing movement of marine central-place foragers. Ecological Modelling, 440, 109397.
  • Collard, P. (2020). Second-order micromotives and macrobehaviour. Journal of Computational Social Science, 1-21.
  • Colombi, A., Scianna, M., & Preziosi, L. (2020). Collective migration and patterning during early development of zebrafish posterior lateral line. Philosophical Transactions of the Royal Society B, 375(1807), 20190385.
  • Conti, E., Di Mauro, L. S., Pluchino, A., & Mulder, C. (2020). Testing for top‐down cascading effects in a biomass‐driven ecological network of soil invertebrates. Ecology and Evolution.
  • Cooksey, R. W. (2020). Specialised Statistical Procedures. In Illustrating Statistical Procedures: Finding Meaning in Quantitative Data (pp. 557-693). Springer, Singapore.
  • Cooper, G. S., Willcock, S., & Dearing, J. A. (2020). Regime shifts occur disproportionately faster in larger ecosystems. Nature Communications, 11(1), 1-10.
  • Costa, L., Araújo, M., Silva, T., Junior, R., Andrade, J., & Campos, G. (2020, January). Comparative Study of Neural Networks Techniques in the Context of Cooperative Observations. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional (pp. 563-574). SBC.
  • Cotfas, L. A., Delcea, C., Milne, R. J., & Salari, M. (2020). Evaluating Classical Airplane Boarding Methods Considering COVID-19 Flying Restrictions.
  • Craze, G. J. (2020). Inflammation-Associated Mood Deterioration and the Degradation of Affective Climate: An Agent-Based Model (Doctoral dissertation, Case Western Reserve University).
  • Crespi, C., Fargetta, G., Pavone, M., Scollo, R. A., & Scrimali, L. (2020, November). A Game Theory Approach for Crowd Evacuation Modelling. In International Conference on Bioinspired Methods and Their Applications (pp. 228-239). Springer, Cham.
  • Cuevas, E. (2020). An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in Biology and Medicine, 103827.
  • Cuevas, E., Gálvez, J., Avila, K., Toski, M., & Rafe, V. (2020). A new metaheuristic approach based on agent systems principles. Journal of Computational Science, 101244.
  • Cui, L., He, T., Jiang, Y., Li, M., Wang, O., Jiajue, R., ... & Xia, W. (2020). Predicting the intervention threshold for initiating osteoporosis treatment among postmenopausal women in China: a cost-effectiveness analysis based on real-world data. Osteoporosis International, 31(2), 307-316.
  • Cunha, M. E. S., Rossetti, R. J., & Campos, P. (2020). Modelling Smart Cities Through Socio-Technical Systems. In 2020 IEEE International Smart Cities Conference (ISC2) (pp. 1-8). IEEE.
  • Dabholkar, S., Peel, A, Anton, G., Horn, M. & Wilensky, U. (2020). Analysis of teachers’ involvement in co-design and implementation of CT (Computational Thinking) integrated biology units. Paper accepted at the American Education Research Association (AERA) Conference, San Francisco, CA, USA.
  • Dabholkar, S., & Wilensky, U. (2020). DESIGNING COMPUTATIONAL MODELS AS EMERGENT SYSTEMS MICROWORLDS TO SUPPORT LEARNING OF SCIENTIFIC INQUIRY.
  • Daems, D. (2020). A Review and Roadmap of Online Learning Platforms and Tutorials in Digital Archaeology. Advances in Archaeological Practice, 8(1), 87-92.
  • Daghriri, T., & Ozmen, O. (2020). Quantifying the Effects of Social Distancing on the Spread of COVID-19. Available at SSRN 3696983.
  • Dahirel, M., Bertin, A., Haond, M., Blin, A., Lombaert, E., Calcagno, V., ... & Vercken, E. (2020). Shifts from pulled to pushed range expansions caused by reductions in connectedness. bioRxiv.
  • Dalle Nogare, D., & Chitnis, A. B. (2020, April). NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium. In Seminars in cell & developmental biology (Vol. 100, pp. 186-198). Academic Press.
  • Davidson, A. L., Khaddage, F., & Ogata, H. (2020). Thematic Working Group. Learners and learning contexts: New alignments for the digital age, 18.
  • D'Auria, M., Scott, E. O., Lather, R. S., Hilty, J., & Luke, S. (2020). Assisted Parameter and Behavior Calibration in Agent-based Models with Distributed Optimization.
  • Davis, N., Polhill, J. G., & Aitkenhead, M. J. Measuring heterogeneity in soil networks: a network analysis and simulation-based approach. Ecological Modelling, 439, 109308.
  • de Boer, A., Krul, L., Fehr, M., Geurts, L., Kramer, N., Urbieta, M. T., ... & Hepburn, P. A. (2020). Animal-free strategies in food safety & nutrition: What are we waiting for? Part I: Food safety. Trends in Food Science & Technology.
  • Deboscker, S., Séverac, F., Gaudart, J., Ménard, C., Meyer, N., & Lavigne, T. (2020). An agent-based model to simulate the transmission of vancomycin-resistant enterococci according different prevention and control measures. Infection Control & Hospital Epidemiology, 1-7.
  • De Kock, P., & Carta, S. (2020). Trojans of ambiguity vs resilient regeneration: visual meaning in cities. Construction Economics and Building.
  • De Masi, G., & Ferrante, E. (2020). Quality-dependent adaptation in a swarm of drones for environmental monitoring. In 2020 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1-6). IEEE.
  • de Oca, E. M., Suppi, R., De Giusti, L., & Naiouf, M. (2020). Green High Performance Simulation for AMB models of Aedes aegypti/Simulacion Green de Alto Rendimiento de un Modelo Basado en Agentes del Mosquito Aedes aegypti. Journal of Computer Science & Technology, 20(1), 15-23.
  • Delcea, C., Cotfas, L. A., Bradea, I. A., Boloș, M. I., & Ferruzzi, G. (2020). Investigating the Exits’ Symmetry Impact on the Evacuation Process of Classrooms and Lecture Halls: An Agent-Based Modeling Approach. Symmetry, 12(4), 627.
  • Delcea, C., Cotfas, L. A., Craciun, L., & Molanescu, A. G. (2020). An agent-based modeling approach to collaborative classrooms evacuation process. Safety science, 121, 414-429.
  • Delcea, C., Milne, R. J., & Cotfas, L. A. (2020). Determining the Number of Passengers for Each of Three Reverse Pyramid Boarding Groups with COVID-19 Flying Restrictions. Symmetry, 12(12), 2038.
  • de Mingo López, L. F., Blas, N. G., Castellanos Peñuela, A. L., & Castellanos Peñuela, J. B. (2020). Swarm Intelligence Models: Ant Colony Systems Applied to BNF Grammars Rule Derivation. International Journal of Foundations of Computer Science, 31(01), 103-116.
  • de Oliveira Zamberlan, A., Bordini, R. H., Kurtz, G. C., & Fagan, S. B. (2020). Multi-Agent Systems, Simulation and Nanotechnology. In Multi Agent Systems-Strategies and Applications. IntechOpen.
  • Derbal, Y. (2020). Agent-Based Model of Cell Signaling in Cancer. In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-4). IEEE.
  • Dhariwal, M., & Dhariwal, S. (2020, April). Let's Chance: Playful Probabilistic Programming for Children. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems Extended Abstracts (pp. 1-7).
  • Dhou, K. (2020). A new chain coding mechanism for compression stimulated by a virtual environment of a predator–prey ecosystem. Future Generation Computer Systems, 102, 650-669.
  • Dhou, K., & Cruzen, C. (2020). A highly efficient chain code for compression using an agent-based modeling simulation of territories in biological beavers. Future Generation Computer Systems.
  • Diaconescu, A., Di Felice, L. J., & Mellodge, P. (2020). Exogenous coordination in multi-scale systems: How information flows and timing affect system properties. Future Generation Computer Systems.
  • Díaz Monsalvea, J., Enríquez Corredor, I., Pinto Moreno, Á. M., & Sánchez Santamaría, J. (2020). Evaluación de una emulación de un sistema ASRS acoplado a un sistema de compras.
  • Di Fiore, A. (2020). The Promise of Spatially Explicit Agent-Based Models for Primatology Research. Spatial Analysis in Field Primatology: Applying GIS at Varying Scales, 280.
  • Dignum, F., Dignum, V., Davidsson, P., Ghorbani, A., van der Hurk, M., Jensen, M., ... & Mellema, R. (2020). Analysing the combined health, social and economic impacts of the corovanvirus pandemic using agent-based social simulation. arXiv preprint arXiv:2004.12809.
  • Di Mauro, L. S., Mulder, C., Conti, E., & Pluchino, A. (2020). Robustness of soil ecosystems under different regimes of management. arXiv preprint arXiv:2005.13414.
  • Dinh, L., & Parulian, N. (2020). COVID‐19 pandemic and information diffusion analysis on Twitter. Proceedings of the Association for Information Science and Technology, 57(1), e252.
  • Dogaroglu, B., & Caliskanelli, S. P. (2020). Investigation of car park preference by intelligent system guidance. Research in Transportation Business & Management, 100567.
  • Domínguez Cañizares, R., & Cannella, S. (2020). Insights on Multi-Agent Systems Applications for Supply Chain Management.
  • Dominguez, R., & Cannella, S. (2020). Insights on Multi-Agent Systems Applications for Supply Chain Management. Sustainability, 12(5), 1935.
  • Dong, J., Liu, R., Qiu, Y., & Crossan, M. (2020). Should knowledge be distorted? Managers' knowledge distortion strategies and organizational learning in different environments. The Leadership Quarterly, 101477.
  • D'Orazio, M., Bernardini, G., & Quagliarini, E. (2020). A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria.
  • D'Orazio, M., Bernardini, G., & Quagliarini, E. (2020). How to restart? An agent-based simulation model towards the definition of strategies for COVID-19" second phase" in public buildings. arXiv preprint arXiv:2004.12927.
  • Dore, K. M., Sewell, D., Mattenet, E. M., & Turner, T. R. (2020). GIS and GPS Techniques in an Ethnoprimatological Investigation of St Kitts Green Monkey (Chlorocebus sabaeus) Crop-Foraging Behavior. Spatial Analysis in Field Primatology: Applying GIS at Varying Scales, 403.
  • Dos Santos, A. T., Machado, C. M., & Adamatti, D. F. (2020). Circadian Rhythm and Pain: Mathematical Model based on Multiagent Simulation. Journal of Medical Systems, 44(10), 1-9.
  • Dou, Y., Xue, X., Wu, C., Luo, X., & Wang, Y. (2020). Interorganizational Diffusion of Prefabricated Construction Technology: Two-Stage Evolution Framework. Journal of Construction Engineering and Management, 146(9), 04020114.
  • Douglas, A., Mazzuchi, T., & Sarkani, S. (2020). A stakeholder framework for evaluating the‐ilities of autonomous behaviors in complex adaptive systems. Systems Engineering.
  • Drummond, F. A., & Collins, J. A. (2020). Field Perimeter Trapping to Manage Rhagoletis mendax (Diptera: Tephritidae) in Wild Blueberry. Journal of Economic Entomology.
  • Du, J., Zhao, D., Issa, R. R., & Singh, N. (2020). BIM for Improved Project Communication Networks: Empirical Evidence from Email Logs. Journal of Computing in Civil Engineering, 34(5), 04020027.
  • Duarte, R. A., Silva, D. F., & Alvarado, M. (2020). Cancer metastasis and the immune system response: modeling the micro-environment by Ising hamiltonian. Suplemento de la Revista Mexicana de Física, 1(4), 25-31.
  • Dubovi, I., Levy, S. T., Levy, M., Zuckerman Levin, N., & Dagan, E. (2020). Glycemic control in adolescents with type 1 diabetes: Are computerized simulations effective learning tools?. Pediatric Diabetes, 21(2), 328-338.
  • DuHadway, S., & Narasimhan, R. (2020). Subverting Process‐Based Controls: Oscillation in Automotive Recalls and a Simulation on Opportunism within a Network. Decision Sciences.
  • Eitzel, M. V., Solera, J., Wilson, K., Neves, K., Fisher, A., Veski, A., ... & Mhike Hove, E. (2020). Indigenous climate adaptation sovereignty in a Zimbabwean agro-pastoral system: exploring definitions of sustainability success using a participatory agent-based model. Ecology and Society, 25(4).
  • Eitzel, M. V., Solera, J., Wilson, K. B., Neves, K., Fisher, A. C., Veski, A., ... & Mhike Hove, E. (2020). Using mixed methods to construct and analyze a participatory agent-based model of a complex Zimbabwean agro-pastoral system. PloS one, 15(8), e0237638.
  • Elfakir, A., Tkiouat, M., Pakgohara, A., & Fairchild, R. (2020). Can Real Options Reduce Moral hazards in Profit and Loss sahring contracts?: A Behavioural Approach Using Game Theory and Agent Based Simulation.
  • Elffers, H., Gerritsen, C., & Birks, D. (2020). Agent-based modeling for testing and developing theories. Agent-Based Modelling for Criminological Theory Testing and Development, 187.
  • El-Khateeb, E., Burkhill, S., Murby, S., Amirat, H., Rostami-Hodjegan, A., & Ahmad, A. (2020). Physiological-based pharmacokinetic modeling trends in pharmaceutical drug.
  • Elzinga, D. C., Boggess, E., Collignon, J., Riederer, A., & Capaldi, A. (2020). An agent‐based model determining a successful reintroduction of the extinct passenger pigeon. Natural Resource Modeling, e12292.
  • Engebretsen, B. J. (2020). Teaching Through COVID-19 Part I: COVID-19, Public, and Global Health: It's Personal. Teaching through COVID-19 in Science Education and Civic Engagement, 12(2), 27.
  • ERÜMİT, A. K., ÖNGÖZ, S., & AKSOY, D. A. (2020). Designing A Computer Programming Environment For Gifted Students: A Case Study.
  • Escudero Marin, P. (2020). Using agent-based modelling and simulation to model performance measurement in healthcare (Doctoral dissertation, Lancaster University).
  • Evans, B. P., Glavatskiy, K., Harré, M. S., & Prokopenko, M. (2020). The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model. arXiv preprint arXiv:2009.06914.
  • Evans, L. C., Oliver, T. H., Sims, I., Greenwell, M. P., Melero, Y., Watson, A., ... & Walters, R. J. (2020). Behavioural modes in butterflies: their implications for movement and searching behaviour. Animal Behaviour, 169, 23-33.
  • Ezzat, H. M. (2020). Behavioral agent-based framework for interacting financial markets. Review of Economics and Political Science.
  • Fajardo, S., Hofstede, G. J., de Vries, M., Kramer, M. R., & Bernal, A. (2020). Gregarious Behavior, Human Colonization and Social Diferentiation: An Agent-based Model. SocArXiv. September, 26.
  • Farjamirad, M., & Niknami, K. A. (2020). Frequency of Using Stone Ossuaries in Marvdasht Plain (Fourth–Seventh Century AD): Explaining Funerary Patterns Through Agent-Based Modelling. In Archaeology of Iran in the Historical Period (pp. 363-371). Springer, Cham.
  • Fard, G. G., Bradley, E., & Peleg, O. (2020). Data-Driven Modeling of Resource Distribution in Honeybee Swarms. bioRxiv.
  • Faria, L. F. F. D., Asevedo, L. F. D., Vieira, J. G. V., & Silva, J. E. A. R. D. (2020). A combined approach of multiple-criteria decision analysis and discrete-event simulation: lessons learned from a fleet composition study. World Review of Intermodal Transportation Research, 9(2), 97-119.
  • Farias, G., Leitzke, B., Born, M., Aguiar, M., & Adamatti, D. (2020). Water Resources Analysis: An Approach based on Agent-Based Modeling. Revista de Informática Teórica e Aplicada, 27(2), 81-95.
  • Farjam, M., & Bravo, G. (2020). Fixing Sample Biases in Experimental Data Using Agent-Based Modelling. In Advances in Social Simulation (pp. 155-159). Springer, Cham.
  • Fazio, C. (2020). Active Learning Methods and Strategies to Improve Student Conceptual Understanding: Some Considerations from Physics Education Research. In Research and Innovation in Physics Education: Two Sides of the Same Coin (pp. 15-35). Springer, Cham.
  • Fedriani, J. M., Ayllón, D., Wiegand, T., & Grimm, V. (2020). Intertwined effects of defaunation, increased tree mortality and density compensation on seed dispersal. Ecography.
  • Feinberg, A., Ghorbani, A., & Herder, P. M. (2020). Commoning toward urban resilience: The role of trust, social cohesion, and involvement in a simulated urban commons setting. Journal of Urban Affairs, 1-26.
  • Feinberg, A., Hooijschuur, E., & Ghorbani, A. (2020). Simulation of Behavioural Dynamics Within Urban Gardening Communities. In Advances in Social Simulation (pp. 161-167). Springer, Cham.
  • Fekih, S. (2020). People Displacement in a Conflict Zone–A Case Study of Mosul Battle, Iraq (Master's thesis, University of Twente).
  • Feliciani, T., Flache, A., & Mäs, M. (2020). Persuasion without polarization? Modelling persuasive argument communication in teams with strong faultlines. Computational and Mathematical Organization Theory, 1-32.
  • Feliciani, T., Moorthy, R., Lucas, P., & Shankar, K. (2020). Grade Language Heterogeneity in Simulation Models of Peer Review. Journal of Artificial Societies and Social Simulation, 23(3), 1-8.
  • Ferreira, D. C. S., Tejada, J., & dos Santos Bispo, G. R. (2020). A NetLogo implementation of the norms and meta-norms game: Behavior Analysis meets Agent Based Modeling.
  • Fichera, A., Pluchino, A., & Volpe, R. (2020). From self-consumption to decentralized distribution among prosumers: A model including technological, operational and spatial issues. Energy Conversion and Management, 217, 112932.
  • Fichera, A., Pluchino, A., & Volpe, R. (2020). Modelling Energy Distribution in Residential Areas: A Case Study Including Energy Storage Systems in Catania, Southern Italy. Energies, 13(14), 3715.
  • Fioretti, G., & Lomi, A. (2020). Emergence of Organizations out of Garbage Can Dynamics. Available at SSRN 3581357.
  • Fioretti, G., & Policarpi, A. (2020). The Less Intelligent the Elements, the More Intelligent the Whole. Or, Possibly Not?. Or, Possibly Not.
  • Flood, V. J., Shvarts, A., & Abrahamson, D. (2020). Teaching with embodied learning technologies for mathematics: Responsive teaching for embodied learning. ZDM Mathematics Education, 52(7), 1307-1331. https://doi.org/10.1007/s11858-020-01165-7
  • Fouladvand, J., Mouter, N., Ghorbani, A., & Herder, P. (2020). Formation and Continuation of Thermal Energy Community Systems: An Explorative Agent-Based Model for the Netherlands. Energies, 13(11), 2829.
  • Fränken, J. P., & Pilditch, T. (2020). Cascades across networks are sufficient for the formation of echo chambers: An agent-based model.
  • Frantz, C. K. (2020). Impact of Meta-roles on the Evolution of Organisational Institutions. In Multi-Agent-Based Simulation XXI: 21st International Workshop, MABS 2020, Auckland, New Zealand, May 10, 2020, Revised Selected Papers (p. 66). Springer Nature.
  • Friedrich, I. D., Hirnsperger, M., & Bauer, S. Understanding the Demographic Future of Small Arctic Villages Using Agent-Based Modeling.
  • Fu, Q., Tian, Y., & Sun, J. (2020). Integration of an Agent-Based Joint Route and Departure Time Choice Model with the Dynamic Traffic Assignment Package. In CICTP 2020 (pp. 3087-3099).
  • Füllsack, M., Kapeller, M., Plakolb, S., & Jäger, G. (2020). Training LSTM-Neural Networks on Early Warning Signals of declining cooperation in simulated Repeated Public Good Games. MethodsX, 100920.
  • Gah, E. (2020). Ant-Inspired Control Strategies for Collective Transport by Dynamic Multi-Robot Teams with Temporary Leaders (Doctoral dissertation, Arizona State University).
  • Gajary, L. C. (2020). Hybridizing agent-based with system dynamics models: principles for theory development in public policy and management research. Handbook of Research Methods in Public Administration, Management and Policy, 63.
  • Galindro, A., Matias, J., Cerveira, A., Santos, C., & Marta-Costa, A. (2020). Prediction of Viticulture Farms Behaviour: An Agent-Based Model Approach. In The Changing Role of SMEs in Global Business (pp. 155-178). Palgrave Macmillan, Cham.
  • Gallagher, C. A., Grimm, V., Kyhn, L. A., Kinze, C. C., & Nabe-Nielsen, J. (2020). Movement and seasonal energetics mediate vulnerability to disturbance in marine mammal populations.
  • Gao, J., Zheng, D., & Yang, S. (2020). Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics. Personal and Ubiquitous Computing, 1-14.
  • Gao, S., Song, X., & Ding, R. (2020). Promoting Information Transfer in Collaborative Projects through Network Structure Adjustment. Journal of Construction Engineering and Management, 146(2), 04019108.
  • García, A. P., & Rodríguez-Patón, A. (2020). Applying Evolutionary Metaheuristics for Parameter Estimation of Individual-Based Models. arXiv preprint arXiv:2005.12841.
  • Garcia Filho, C. (2020). Simulating social distancing measures in household and close contact transmission of SARS-CoV-2. Cadernos de Saúde Pública, 36(5).
  • Garibo i Orts, Ó., Conejero, J. A., & Urchueguía, J. F. (2020). Rational Design of a Genetic Finite State Machine: Combining Biology, Engineering, and Mathematics for Bio-Computer Research. Mathematics, 8(8), 1362.
  • Garrigan, S. R. (2020). Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools. In Handbook of Research on Integrating Computer Science and Computational Thinking in K-12 Education (pp. 30-44). IGI Global.
  • Gatto, J. V., & Trexler, J. C. (2020). Speed and directedness predict colonization sequence post-disturbance. Oecologia, 1-15.
  • Gautam, A., Bortz, W., & Tatar, D. (2020, February). Abstraction Through Multiple Representations in an Integrated Computational Thinking Environment. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 393-399).
  • Geaves, L. (2020). Agent-Based Modeling of Flood Insurance Futures. In Oxford Research Encyclopedia of Natural Hazard Science.
  • Gendreau Chakarov, A., Biddy, Q., Jacobs, J., Recker, M., & Sumner, T. (2020, August). Opening the Black Box: Investigating Student Understanding of Data Displays Using Programmable Sensor Technology. In Proceedings of the 2020 ACM Conference on International Computing Education Research (pp. 291-301).
  • Gersie, S. (2020). PREDICTING CATTLE GRAZING DISTRIBUTIONS: AN AGENT-BASED MODELING APPROACH. 2020-CSU Theses and Dissertations.
  • Gesell, S. B., de la Haye, K., Sommer, E. C., Saldana, S. J., Barkin, S. L., & Ip, E. H. (2020). Identifying Social Network Conditions that Facilitate Sedentary Behavior Change: The Benefit of Being a “Bridge” in a Group-based Intervention. International Journal of Environmental Research and Public Health, 17(12), 4197.
  • Geyer, J., Nguyen, J., Farrenkopf, T., & Guckert, M. (2020, October). AGADE Traffic 2.0-A Knowledge-Based Approach for Multi-agent Traffic Simulations. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 417-420). Springer, Cham.
  • Ghaitaranpour, A., Mohebbi, M., Koocheki, A., & Ngadi, M. O. (2020). An agent-based coupled heat and water transfer model for air frying of doughnut as a heterogeneous multiscale porous material. Innovative Food Science & Emerging Technologies, 102335.
  • Gharakhanlou, N. M., & Hooshangi, N. (2020, July). Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (case study: Urmia, Iran). Informatics in Medicine Unlocked, 20.
  • Gharakhanlou, N. M., Hooshangi, N., & Helbich, M. (2020). A Spatial Agent-Based Model to Assess the Spread of Malaria in Relation to Anti-Malaria Interventions in Southeast Iran. ISPRS International Journal of Geo-Information, 9(9), 549.
  • Gilbert, L. Teaching Geoscience Tools for Addressing Societal Grand Challenges: A Unique Study-Away Experience During COVID-19. Teaching through COVID-19, 32.
  • Girwidz, R. (2020). Simulating waves and macroscopic phonons. European Journal of Physics.
  • Gkiolmas, A., Stoumpa, A., Chalkidis, A., & Skordoulis, C. (2020). A Combination of Historical Physics Documents and Other Teaching Tools for the Instruction of Prospective Teachers in Chaos and Complexity. In Fundamental Physics and Physics Education Research (pp. 251-261). Springer, Cham.
  • Godois, L. M., Adamatti, D. F., & Emmendorfer, L. R. (2020). A multi-agent-based algorithm for data clustering. Progress in Artificial Intelligence, 1-9.
  • Gomes, E., Banos, A., Abrantes, P., Rocha, J., & Schläpfer, M. (2020). Future land use changes in a peri-urban context: Local stakeholder views. Science of The Total Environment, 137381.
  • Gomez, M. M., & Weiss, M. B. (2020). A comprehensive secondary market model for virtualized wireless connectivity. Telecommunications Policy, 44(10), 102021.
  • Gona, R. (2020). Application of Micro Cloud for Cooperative Vehicles (Doctoral dissertation, Southern Illinois University at Carbondale).
  • Gong, Y. (2020). Influence Among Preferences and Its Transformation to Behaviors in Groups. In Group Decision and Negotiation: A Multidisciplinary Perspective: 20th International Conference on Group Decision and Negotiation, GDN 2020, Toronto, ON, Canada, June 7–11, 2020, Proceedings (p. 104). Springer Nature.
  • González-Méndez, M., Olaya, C., Fasolino, I., Grimaldi, M., & Obregón, N. (2020). Agent-Based Modeling for Urban Development Planning based on Human Needs. Conceptual Basis and Model Formulation. Land Use Policy, 105110.
  • Grajdura, S. A., Borjigin, S. G., & Niemeier, D. A. (2020, November). Agent-based wildfire evacuation with spatial simulation: a case study. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (pp. 56-59).
  • Grantham, E. O., & Giabbanelli, P. J. (2020, May). Creating perceptual uncertainty in agent-based models with social interactions. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-12).
  • Gravel-Miguel, C., & Coward, F. Palaeolithic Social Networks and Behavioural Modernity. In Brughmans, T., Mills, B., Munson, J., & Peeples, M. (eds.), The Oxford Handbook of Archaeological Network Research. Oxford: Oxford University Press.
  • Greco, A., Pluchino, A., Caddemi, S., Caliò, I., & Cannizzaro, F. (2020). On profile reconstruction of Euler–Bernoulli beams by means of an energy based genetic algorithm. Engineering with Computers, 36(1), 239-250.
  • Greenberg, B. (2020). Improving Information Exchange in Disaster Response: Responder Behavior and the Effects of Organizational Design (Doctoral dissertation, The George Washington University).
  • Green, D. G., Klomp, N. I., Rimmington, G., & Sadedin, S. (2020). Virtual Worlds: The Role of Simulation in Ecology. In Complexity in Landscape Ecology (pp. 177-195). Springer, Cham.
  • Gregg, R. (2020). Multi-Scale Modeling of the Innate Immune System: A Dynamic Investigation into Pathogenic Detection (Doctoral dissertation, University of Pittsburgh).
  • Gresalfi, M., Brady, C., Knowe, M., & Steinberg, S. (2020). Engaging in a New Practice: What Are Students Doing When They Are “Doing” Debugging?.
  • Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAngelis, D. L., ... & Johnston, A. S. (2020). The ODD protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism. Journal of Artificial Societies and Social Simulation, 23(2).
  • Groff, E. R., & Badham, J. (2020). Examining guardianship against theft. Agent-Based Modelling for Criminological Theory Testing and Development, 71.
  • GRONAU, S., HADERSDORFER, J., NÖLDEKE, B., PETRUSJANZ, N., STÜTZEL, H., & WINTER, E. (2020). Food security in rural Zambia.
  • Grover, S., Ventures, L. G., Biswas, G., Farris, A. V., Sengupta, P., Covitt, B. A., ... & Horn, M. (2020). Integrating STEM and Computing in PK-12: Operationalizing Computational Thinking for STEM Learning and Assessment. The Interdisciplinarity of the Learning Sciences.
  • Guerrin, F. (2020). Agent-Based Modelling of a Simple Synthetic Rangeland Ecosystem. In Landscape Modelling and Decision Support (pp. 179-215). Springer, Cham.
  • Gulied, M., Al Nouss, A., Khraisheh, M., & AlMomani, F. (2020). Modeling and simulation of fertilizer drawn forward osmosis process using Aspen Plus-MATLAB model. Science of The Total Environment, 700, 134461.
  • Gumzej, R., & Rakovska, M. (2020). Simulation Modeling and Analysis for Sustainable Supply Chains. In Sustainable Logistics and Production in Industry 4.0 (pp. 145-160). Springer, Cham.
  • Guo, X., Chen, J., Azizi, A., Fewell, J., & Kang, Y. (2020). Dynamics of Social Interactions, in the Flow of Information and Disease Spreading in Social Insects Colonies: Effects of Environmental Events and Spatial Heterogeneity. Journal of Theoretical Biology, 110191.
  • Hadjimichael, A., Gold, D., Hadka, D., & Reed, P. (2020). Rhodium: Python Library for Many-Objective Robust Decision Making and Exploratory Modeling. Journal of Open Research Software, 8(1).
  • Hai-xiang, G. U. O., Jin-ling, L. I., Long-hui, L. I. U., & Xiao-ling, K. E. (2020). й Ⱥ ݱ Ķ Agent . ϵͳ ѧ , 22(4), 579-585.
  • Hajmohammad, S., & Shevchenko, A. (2020). Mitigating sustainability risk in supplier populations: an agent-based simulation study. International Journal of Operations & Production Management.
  • Hall, S. M. (2020). Opportunities and Obstacles in the Transition to a Distributed Network of Rooftop Solar: A Multi-Method Approach.
  • Han, C. K., Sadiq, A. S., Mirjalili, S., & Tahir, A. (2020). Trust aware crowd associated network-based approach for optimal waste management in smart cities.
  • Han, X. (2020, December). Influence of exits and evacuees on evacuation efficiency. In IOP Conference Series: Earth and Environmental Science (Vol. 608, No. 1, p. 012031). IOP Publishing.
  • Hanaček, K., Langemeyer, J., Bileva, T., & Rodríguez-Labajos, B. Understanding environmental conflicts through cultural ecosystem services-the case of agroecosystems in Bulgaria. Ecological Economics, 179, 106834.
  • Hannoun, G. J., Murray-Tuite, P., Heaslip, K., & Chantem, T. (2020). Meso-and Micro-scopic Routing of an Emergency Response Vehicle with Connected Vehicle Technologies.
  • Hartbauer, M. (2020). From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising. In Bio-Inspired Technology. IntechOpen.
  • Hartmann, S. (2020). A Conversation about Modeling in Philosophy.
  • Hasbach, J. D., Witte, T. E., & Bennewitz, M. (2020, July). On the Importance of Adaptive Operator Training in Human-Swarm Interaction. In International Conference on Human-Computer Interaction (pp. 311-329). Springer, Cham.
  • Hassan, I. M., & Hassan, K. R. (2020). Vehicular Social Networks and Vehicular Ad-hoc Networks, Applications, Modelling Tools and Challenges: A Survey. International Journal of Computer Applications, 975, 8887.
  • Hasson, S. T., & Hussein, Z. (2020, February). Correlation among network centrality metrics in complex networks. In 2020 6th International Engineering Conference “Sustainable Technology and Development"(IEC) (pp. 54-58). IEEE.
  • Hauke, J., Achter, S., & Meyer, M. (2020). Theory development via replicated simulations and the added value of standards. The journal of artificial societies and social simulation, 23(1).
  • He, Z., Huang, D., Fang, J., & Wang, B. (2020). Stakeholder Conflict Amplification of Large-Scale Engineering Projects in China: An Evolutionary Game Model on Complex Networks. Complexity, 2020.
  • Healy, C., Pekins, P. J., Atallah, S., & Congalton, R. G. (2020). Using agent-based models to inform the dynamics of winter tick parasitism of moose. Ecological Complexity, 41, 100813.
  • Helikar, T. (2020). The Need for Research-Grade Systems Modeling Technologies for Life Science Education. Trends in Molecular Medicine.
  • Herberich, M. M., Gayler, S., Anand, M., & Tielbörger, K. (2020). Biomass–density relationships of plant communities deviate from the self‐thinning rule due to age structure and abiotic stress. Oikos.
  • Hernández, P., Pena, C., Ramos, A., & Gómez-Cadenas, J. J. (2020). A simple formulation of non-Markovian SEIR. arXiv preprint arXiv:2005.09975.
  • Hess, B., Dreber, N., Liu, Y., Wiegand, K., Ludwig, M., Meyer, H., & Meyer, K. M. (2020). PioLaG: a piosphere landscape generator for savanna rangeland modelling. Landscape Ecology, 1-22.
  • Highlander, H., & Singley, A. (2020). COVID-19: A Mathematical Model for the Effect of Social Distancing on the Spread of COVID-19. Letters in Biomathematics.
  • Hjorth, A., Head, B., Brady, C. & Wilensky, U. (2020). LevelSpace – a NetLogo Extension for Multi-Level Agent-Based Modeling. Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-4.
  • Hoffmann, B., Urquhart, N., Chalmers, K., & Guckert, M. (2020). Athos: An Extensible DSL for Model Driven Traffic and Transport Simulation. Modellierung 2020.
  • Hokamp, S. Agent-based Modeling of Human Exposure to Urban Environmental Stressors–A Docking Study.
  • Hooten, M., Wikle, C., & Schwob, M. (2020). Statistical Implementations of Agent‐Based Demographic Models. International Statistical Review.
  • Hou, J., Yu, T., & Xiao, R. (2020, September). Structure Reversal of Online Public Opinion for the Heterogeneous Health Concerns under NIMBY Conflict Environmental Mass Events in China. In Healthcare (Vol. 8, No. 3, p. 324). Multidisciplinary Digital Publishing Institute.
  • Hsiao, Y. (2020). Evaluating the Mobilization Effect of Online Political Network Structures: A Comparison between the Black Lives Matter Network and Ideal Type Network Configurations. Social Forces.
  • Huang, M., & Pape, A. D. (2020). The Impact of Online Consumer Reviews on Online Sales: The Case-Based Decision Theory Approach. Journal of Consumer Policy, 1-28.
  • Huber, R., Hang, X., Keller, K., & Finger, R. (2020). FARMIND: Farm Interaction and Decision Model.
  • Hui, W., Xin-gang, Z., Ling-zhi, R., & Fan, L. (2020). An agent-based modeling approach for analyzing the influence of market participants' strategic behavior on green certificate trading. Energy, 119463.
  • Hussein, S. E. (2020). Rehabilitation Center Planning using Multi-agent Systems (Dept. E). MEJ. Mansoura Engineering Journal, 34(1), 22-30.
  • Husssein, A. A., Salman, M. A., Al Essa, H. A., & Hussein, N. Y. (2020). Developing Agent-Based Model for Colorization. Journal of University of Babylon for Pure and Applied Sciences, 147-157.
  • Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., ... & McElhaney, K. (2020). C2STEM: a System for Synergistic Learning of Physics and Computational Thinking. Journal of Science Education and Technology, 29(1), 83-100.
  • Hutchins, N. M., Biswas, G., Wolf, R. C., Chin, D. B., Grover, S., Ventures, L. G., & Blair, K. P. (2020). Computational Thinking in Support of Learning and Transfer. The Interdisciplinarity of the Learning Sciences.
  • Hutchins, N. M., Biswas, G., Zhang, N., Snyder, C., Lédeczi, Á., & Maróti, M. (2020). Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Learning through Computational Modeling. International Journal of Artificial Intelligence in Education, 1-44.
  • Hwang, I. (2020). An Agent-Based Model of Firm Size Distribution and Collaborative Innovation. Journal of Artificial Societies and Social Simulation, 23(1), 1-9.
  • Iasiello, C. A. (2020, May). Using agent based modeling to interpret underlying factors of underrepresentation of minorities in Hollywood films. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-12).
  • Iasiello, C., Crooks, A., & Wittman, S. (2020, October). The Human Resource Management Parameter Experimentation Tool. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 298-307). Springer, Cham.
  • Ibrahim, B. K., Mahdi, M. A., & Salman, M. A. (2020, April). Triple Mobile Anchors Approach for Localization in WSN. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 174-179). IEEE.
  • Imran, M., Rodrigues, J. J., Kamal, A. E., Ahmed, E., Xia, F., & Awan, I. (2020). IEEE Access Special Section Editorial: Survivability Strategies for Emerging Wireless Networks. IEEE Access, 8, 225219-225225.
  • Innocenti, E., Detotto, C., Idda, C., Parker, D. C., & Prunetti, D. (2020). An iterative process to construct an interdisciplinary ABM using MR POTATOHEAD: An application to Housing Market Models in touristic areas. Ecological Complexity, 44, 100882.
  • Insua, D. R., Baylon, C., & Vila, J. (Eds.). (2020). Security Risk Models for Cyber Insurance. CRC Press.
  • Ionuț, N. I. C. A. (2020). Simulation of financial contagion effect using the NetLogo software at the level of the banking network. Theoretical and Applied Economics, 22(3 (624), Autumn), 55-74.
  • Irgens, G. A., Dabholkar, S., Bain, C., Woods, P., Hall, K., Swanson, H., ... & Wilensky, U. (2020). Modeling and Measuring High School Students’ Computational Thinking Practices in Science. Journal of Science Education and Technology, 29(1), 137-161.
  • Isgro, F. (2020). Fuselage Design Studies to Improve Boarding Performance of Novel Passenger Aircraft: An Approach from Knowledge-Based Engineering and Agent-Based Modelling.
  • Ivanek, R., & Wiedmann, M. (2020). CPS 2018 RFP FINAL PROJECT REPORT.
  • Ivars-Silva, F., Rossetti, R. J., & Porto, P. (2020). Emotional Contagion Modeled Through the Empathy Quotient: An Epidemiological Analogy Towards Social Sustainability. In 2020 IEEE International Smart Cities Conference (ISC2) (pp. 1-8). IEEE.
  • Izquierdo, S. S., & Izquierdo, L. R. “TEST TWO, CHOOSE THE BETTER” LEADS TO HIGH COOPERATION IN THE CENTIPEDE GAME.
  • Jablonski, K. E., Boone, R. B., & Meiman, P. J. (2020). Predatory plants and patchy cows: modeling cattle interactions with toxic larkspur amid variable heterogeneity. Rangeland Ecology & Management, 73(1), 73-83.
  • Jacobson, M. J., Goldwater, M., Markauskaite, L., Lai, P. K., Kapur, M., Roberts, G., & Hilton, C. (2020). Schema abstraction with productive failure and analogical comparison: Learning designs for far across domain transfer. Learning and Instruction, 65, 101222.
  • Jacquet, J. M., & Barkallah, M. (2020). Anemone: A workbench for the Multi-Bach coordination language. Science of Computer Programming, 102579.
  • Jaffer, M. A. (2020, November). Can Zakat Charity Help Reduce Economic Inequality?. In International Conference of Zakat (pp. 279-294).
  • Jager, W., Abramczuk, K., Komendant-Brodowska, A., Baczko-Dombi, A., Fecher, B., Sokolovska, N., & Spits, T. (2020). Looking into the Educational Mirror: Why Computation Is Hardly Being Taught in the Social Sciences, and What to Do About It. In Advances in Social Simulation (pp. 239-245). Springer, Cham.
  • Jager, W., & Yamu, C. (2020). 19. Simulating community dynamics for transitional urban planning processes. Handbook on Planning and Complexity, 373.
  • Jagielski, P. M. (2020). Exploring the Energetic Consequences and Decision-Making Behaviours of Polar Bears (Ursus maritimus) Foraging on Common Eider (Somateria mollissima) Seaduck Eggs on Mitivik Island, Nunavut (Doctoral dissertation, University of Windsor (Canada)).
  • Jayadevan, A., Nayak, R., Karanth, K. K., Krishnaswamy, J., DeFries, R., Karanth, K. U., & Vaidyanathan, S. (2020). Navigating paved paradise: Evaluating landscape permeability to movement for large mammals in two conservation priority landscapes in India. Biological Conservation, 247, 108613.
  • Jessica, S. Y. (2020). Multi-Scale, Multi-Class Agent-Based Models of Biological Systems (Doctoral dissertation, Northwestern University).
  • Jiang, F., Zhang, J., & Zhao, X. (2020). Research on the influence mechanism of resettlers participation in migrant work in the context of relationship network. Peer-to-Peer Networking and Applications, 1-10.
  • Jiang, G., Feng, X., Liu, W., & Liu, X. (2020). Clicking position and user posting behavior in online review systems: A data-driven agent-based modeling approach. Information Sciences, 512, 161-174.
  • Jiang, H., Chen, C., Zhao, S., & Wu, Y. (2020). Evolution of a Technology Standard Alliance Based on an Echo Model Developed through Complex Adaptive System Theory. Complexity, 2020.
  • Jiang, X., & Zhao, B. (2020). Modeling on the epidemic of coronavirus disease 2019. J Bio Med Open Access, 1(1), 103.
  • Jimenez, A. F., Cardenas, P. F., Canales, A., Jimenez, F., & Portacio, A. (2020). A survey on intelligent agents and multi-agents for irrigation scheduling. Computers and Electronics in Agriculture, 105474.
  • Johanes, P. (2020). Technology as a Gateway to a Philosophy of the Learning Sciences. Stanford University.
  • Joshi, M. Y., Flacke, J., & Schwarz, N. (2020). Do microfinance institutes help slum-dwellers in coping with frequent disasters? An agent-based modelling study. International Journal of Disaster Risk Reduction, 101627.
  • Kaaronen, R. O., & Strelkovskii, N. (2020). Cultural Evolution of Sustainable Behaviors: Pro-environmental Tipping Points in an Agent-Based Model. One Earth, 2(1), 85-97.
  • Kabora, T. K., Stump, D., & Wainwright, J. (2020). How did that get there? Understanding sediment transport and accumulation rates in agricultural landscapes using the ESTTraP agent-based model. Journal of Archaeological Science: Reports, 29, 102115.
  • Kafai, Y., Hutchins, N., Snyder, C., Brennan, K., Haduong, P., DesPortes, K., ... & Fields, D. (2020). Turning Bugs into Learning Opportunities: Understanding Debugging Processes, Perspectives and Pedagogies.
  • Kaiser, K. E., Flores, A. N., & Vernon, C. R. (2020). Janus: A Python Package for Agent-Based Modeling of Land Use and Land Cover Change. Journal of Open Research Software, 8(1).
  • Kaligotla, C., Yücesan, E., & Chick, S. E. (2020). Diffusion of competing rumours on social media. Journal of Simulation, 1-21.
  • Kampik, T., & Najjar, A. (2020). Simulating, Off-Chain and On-Chain: Agent-Based Simulations in Cross-Organizational Business Processes. Information, 11(1), 34.
  • Kantasa-ard, A., Nouiri, M., Bekrar, A., Ait el cadi, A., & Sallez, Y. (2020). Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand. International Journal of Production Research, 1-25.
  • Karimi, M. J., & Vaez-Zadeh, S. (2020). An Agent-Based Model for Electric Energy Policy Assessment. Electric Power Systems Research, 106903.
  • Karnouskos, S., Leitao, P., Ribeiro, L., & Colombo, A. W. (2020). Industrial Agents as a Key Enabler for Realizing Industrial Cyber-Physical Systems: Multiagent Systems Entering Industry 4.0. IEEE Industrial Electronics Magazine, 14(3), 18-32.
  • Karsai, I., Schmickl, T., & Kampis, G. (2020). Forest Fires: Fire Management and the Power Law. In Resilience and Stability of Ecological and Social Systems (pp. 63-77). Springer, Cham.
  • Karsai, I., Schmickl, T., & Kampis, G. (2020). Habitat Fragmentation. In Resilience and Stability of Ecological and Social Systems (pp. 47-61). Springer, Cham.
  • Katerndahl, D. A., Burge, S. K., Ferrer, R. L., Wood, R., & Montanez Villacampa, M. D. P. (2020). Modeling Women’s Need For Action in Violent Relationships. Journal of interpersonal violence, 0886260519900943.
  • Katz, K., & Naug, D. (2020). A mechanistic model of how metabolic rate can interact with resource environment to influence foraging success and lifespan. Ecological Modelling, 416, 108899.
  • Kaur, H., Kaur, H., & Singh, A. (2020). Multi-agent Based Recommender System for Netflix. In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India (pp. 211-221). Springer, Singapore.
  • Kazil, J., Masad, D., & Crooks, A. (2020, October). Utilizing Python for Agent-Based Modeling: The Mesa Framework. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 308-317). Springer, Cham.
  • Kelsey, J., & Kadivar, S. (2020). Stakeholder-driven design and appraisal in hydroelectric projects: a participatory gaming approach.
  • Kelter, J. Z., Peel, A., Bain, C., Anton, G., Dabholkar, S., Aslan, Ü., Horn, M., & Wilensky, U. (2020). Seeds of (r)Evolution: Constructionist Co-Design with High School Science Teachers. In B. Tangney, J. Rowan Byrne, & C. Girvan (Eds.), Proceedings of the 2020 Constructionism Conference, Dublin, Ireland, May 26—May 29, 2020 (p.497-505). (ISBN 978-1-911566-09-0)
  • Khadim, S., Riaz, F., Jabbar, S., Khalid, S., & Aloqaily, M. (2020). A non-cooperative rear-end collision avoidance scheme for non-connected and heterogeneous environment. Computer Communications, 150, 828-840.
  • Khan, I., Lewis, M., & Cañamero, L. (2020, July). Modelling the Social Buffering Hypothesis in an Artificial Life Environment. In Artificial Life Conference Proceedings (pp. 393-401). One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press.
  • Khansari, N., & Hewitt, E. (2020). Incorporating an agent-based decision tool to better understand occupant pathways to GHG reductions in NYC buildings. Cities, 97, 102503.
  • Kharlov, L. (2020). Modelling the Disruptive Innovations. Review of Business and Economics Studies, (3).
  • Khelfa, B., & Tordeux, A. (2020). DYNAMIC SAFETY ANALYSIS FOR AUTOMATED DRIVING.
  • Kim, I., & Kwon, H. (2020). Assessing the Impacts of Urban Land Use Changes on Regional Ecosystem Services According to Urban Green Space Policies Via the Patch-Based Cellular Automata Model. Environmental Management, 1-13.
  • Klaßmann, S., Dahmen, N., & Seifert, U. (2020). A Digital Habitat for interdisciplinary music research and teaching.
  • Knöös Franzén, L., Schön, S., Papageorgiou, A., Staack, I., Ölvander, J., Krus, P., ... & Jouannet, C. (2020). A System of Systems Approach for Search and Rescue Missions. In AIAA Scitech 2020 Forum (p. 0455).
  • Koehler, M., Slater, D. M., Jacyna, G., & Thompson, J. R. (2020). Modeling COVID-19 for lifting non-pharmaceutical interventions. medRxiv.
  • Koľveková, G., Raisová, M., Zoričak, M., & Gazda, V. (2020). Endogenous Shared Punishment Model in Threshold Public Goods Games. Computational Economics, 1-25.
  • Kooi, B. W., & Kooijman, S. A. L. M. (2020). A cohort projection method to follow deb-structured populations with periodic, synchronized and iteroparous reproduction. Ecological Modelling, 436, 109298.
  • Koretsky, M. D. (2020). An interactive virtual laboratory addressing student difficulty in differentiating between chemical reaction kinetics and equilibrium. Computer Applications in Engineering Education, 28(1), 105-116.
  • Koralewski, T. E., Wang, H. H., Grant, W. E., Brewer, M. J., Elliott, N. C., Westbrook, J. K., ... & Michaud, J. P. (2020). Integrating Models of Atmospheric Dispersion and Crop-Pest Dynamics: Linking Detection of Local Aphid Infestations to Forecasts of Region-Wide Invasion of Cereal Crops. Annals of the Entomological Society of America.
  • Koralewski, T. E., Wang, H. H., Grant, W. E., LaForest, J. H., Brewer, M. J., Elliott, N. C., & Westbrook, J. K. (2020). Toward near-real-time forecasts of airborne crop pests: Aphid invasions of cereal grains in North America. Computers and Electronics in Agriculture, 179, 105861.
  • Korb, S., & Sacks, R. (2020). Agent-Based Simulation of General Contractor–Subcontractor Interactions in a Multiproject Environment. Journal of Construction Engineering and Management, 147(1), 04020151.
  • Krawczyk, M. J., & Kułakowski, K. (2020). How to be influential being weakly connected. Physica D: Nonlinear Phenomena, 132644.
  • Kshirsagar, J., Hayatnagarkar, H., & Dewan, A. (2020). EPIRUST: TOWARDS A FRAMEWORK FOR LARGE-SCALE AGENT-BASED EPIDEMIOLOGICAL SIMULATIONS USING RUST LANGUAGE.
  • Kuznetsov, A. V., Halaimova, A. V., Ufimtseva, M. A., & Chelebieva, E. S. (2020). Blocking a chemical communication between Trichoplax organisms leads to their disorderly movement. International Journal of Parallel, Emergent and Distributed Systems, 1-10.
  • Kynigos, C. Half-Baked Constructionism: A Strategy to Address the Challenge of Infusing Constructionism in Education in Greece. In Holbert, N., Berland, M., & Kafai, Y. B. (eds.), Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs, 61.
  • Laguna-Sanchez, G. A., & Lopez-Sauceda, J. (2020). Agent-Supported Heuristic Model for the Dynamic Spread of Infectious Diseases.
  • Laili, Y., Zhang, L., & Luo, Y. (2020). A pattern-based validation method for the credibility evaluation of simulation models. SIMULATION, 96(2), 151-167.
  • Lall, M. (2020). AN AGENT-BASED SIMULATION OF AN ALTERNATIVE PARKING BAY CHOICE STRATEGY. The South African Journal of Industrial Engineering, 31(2), 107-115.
  • Larson, H. (2020). Agent-Based Modeling of Locust Foraging and Social Behavior.
  • LEBLOND, V., DESBUREAUX, L., & BIELECKI, V. (2020). A NEW AGENT-BASED SOFTWARE FOR DESIGNING AND OPTIMIZING EMERGING MOBILITY SERVICES: APPLICATION TO CITY OF RENNES.
  • Lee, J. S., & Wolf-Branigin, M. (2020). Innovations in modeling social good: A demonstration with juvenile justice intervention. Research on Social Work Practice, 30(2), 174-185.
  • Lee, J. Y., Sadler, N. C., Egbert, R. G., Anderton, C. R., Hofmockel, K. S., Jansson, J. K., & Song, H. S. (2020). Deep Learning Predicts Microbial Interactions from Self-organized Spatiotemporal Patterns. Computational and Structural Biotechnology Journal.
  • Lee, S., & Clinedinst, L. (2020). Mathematical Biology: Expand, Expose, and Educate!. Bulletin of Mathematical Biology, 82(9), 1-15.
  • Legaspi, J., Canfield, C. I., Gill, K. S., Wyglinski, A. M., & Bhadai, S. V. (2020, May). Integrated Agent-Based Model for Broadband Resource Allocation Analysis. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (pp. 1-5). IEEE.
  • Leitzke, B., Pereira, L., & Adamatti, D. (2020, January). Simulação Multiagente para Controle de Poluição na Água. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional (pp. 142-153). SBC.
  • Lestari, D. P., Sabri, A., Handhika, T., Sari, I., & Fahrurozi, A. (2020, May). The simulation of evacuation from multistorey building using NetLogo. In IOP Conference Series: Materials Science and Engineering (Vol. 854, No. 1, p. 012060). IOP Publishing.
  • Lévy, P., Zhang, Y., & van de Pol, M. (2020). DESIGNING A POLICY MAK-ING TOOL TO MITIGATE SE-CURITY OF SUPPLY RISKS.
  • Li, F., Du, T. C., & Wei, Y. (2020). Enhancing supply chain decisions with consumers’ behavioral factors: An illustration of decoy effect. Transportation Research Part E: Logistics and Transportation Review, 144, 102154.
  • Li, S., Liu, Z., & Li, Y. (2020). Temporal and spatial evolution of online public sentiment on emergencies. Information Processing & Management, 57(2), 102177.
  • Li, Y., Schoenfeld, A. H., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On Computational Thinking and STEM Education.
  • Li, Z., Fang, A., Cui, H., Ding, J., Liu, B., Xie, G., ... & Xing, D. (2020). Synthetic bacterial consortium enhances hydrogen production in microbial electrolysis cells and anaerobic fermentation. Chemical Engineering Journal, 127986.
  • Liao, M., Zhang, J., & Wang, R. (2020). A dynamic evolutionary game model of web celebrity brand eWOM marketing control strategy. Asia Pacific Journal of Marketing and Logistics.
  • Liao, M., Qi, L., & Zhang, J. (2020). The Dynamic Evolution Mechanism of Heterogeneous OWOM—An Improved Viral Marketing Model. Information, 11(3), 140.
  • Lin, G., Palopoli, M., & Dadwal, V. (2020). From Causal Loop Diagrams to System Dynamics Models in a Data-Rich Ecosystem. In Leveraging Data Science for Global Health (pp. 77-98). Springer, Cham.
  • Lin, S. Y. (2020). Distributed Simulation of Interdependencies in Community Resilience (Doctoral dissertation).
  • Lira, M. (2020). How Knowledge-in-Pieces Informs Research in Math-Bio Education.
  • Lira, M. (2020). Why Knowledge Analysis Changes the Design of Computational Learning Environments in Biology Education.
  • Lisianti, S., Hagijanto, A. D., & Malkisedek, M. H. (2020). Kajian Visual Siger dalam Budaya Kontemporer Masyarakat Lampung. Jurnal DKV Adiwarna, 1(16), 11.
  • Liu, C., Jackson, L. V., Hutchings, S. J., Tuesca, D., Moreno, R., Mcindoe, E., & Kaundun, S. S. (2020). A holistic approach in herbicide resistance research and management: from resistance detection to sustainable weed control. Scientific Reports, 10(1), 1-9.
  • Liu, C., Zhou, H., & Liu, H. (2020). The Difference of FOIPW between the Eastern Coastal Areas and Other Areas of China. Journal of Coastal Research, 103(sp1), 222-225.
  • Liu, C. J., Liu, Z., Chai, Y. J., & Liu, T. T. (2020). Review of Virtual Traffic Simulation and Its Applications. Journal of Advanced Transportation, 2020.
  • Liu, G., Ye, J., & Argyres, C. (2020). Modeling and simulation of the knowledge growth process among new energy technology firms in the distributed innovation network. DYNA-Ingeniería e Industria, 95(1).
  • Liu, J., Zhang, M., & Nikita, N. Agent-based design research to explore the effectiveness of bottom-up organizational design in shaping sustainable vernacular landscapes: A case in Hailar, China. Landscape and Urban Planning, 205, 103961.
  • London, J. O. N. (2020). African-American High-Tech Enterprises: Agent-Based Modeling and Simulation for Innovation (Doctoral dissertation, University of Bridgeport).
  • London, J. O. N., & Sheikh, N. J. (2020). Innovation in African-American high-tech enterprises: a multi-agent approach. Entrepreneurship and Sustainability Issues, 7(4), 3101-3121.
  • Lorenz, F., & Jeyapragasan, G. (2020). The impact of climate change on tri-trophic interactions and crop production. The iScientist, 5(1), 4-12.
  • Lorenz, J., Neumann, M., & Schröder, T. (2020). Individual attitude change and societal dynamics: Computational experiments with psychological theories.
  • LORENZ, W. E., & WURZER, G. (2020). FLÄVIZ IN THE REZONING PROCESS.
  • Lorenz, W., & Wurzer, G. (2020). FRACAM: A 2.5 D Fractal Analysis Method for Facades. Education and Digital Theory - Ethics, Cybernetics, Feedback, Theory, 1, 495-504.
  • Lorig, F., & Timm, I. J. (2020). Simulation-Based Data Acquisition. In Principles of Data Science (pp. 1-15). Springer, Cham.
  • Lu, J., Liu, X., Feng, Y., & Lin, X. Simulation and Analysis of Community Energy Consumption Based on Multi-agent Modeling. In 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) (pp. 1280-1284). IEEE.
  • Lu, P., Zhang, Z., Li, M., Chen, D., & Yang, H. (2020). Agent-based modeling and simulations of terrorist attacks combined with stampedes. Knowledge-Based Systems, 106291.
  • Lu, Q., Fricke, G. M., Ericksen, J. C., & Moses, M. E. (2020). Swarm Foraging Review: Closing the Gap Between Proof and Practice. Current Robotics Reports, 1-11.
  • Lubida, A. P., Rajabi, M., Pilesjö, P., & Mansourian, A. (2020). Investigating an Agent Based Modelling approach for SDI planning: A case study of Tanzania NSDI development. South African Journal of Geomatics, 9(2), 198-218.
  • Luke, S. (2020). Assisted Parameter and Behavior Calibration in Agent-Based Models with Distributed Optimization. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection: 18th International Conference, PAAMS 2020, L’Aquila, Italy, October 7–9, 2020, Proceedings (p. 93). Springer Nature.
  • Luo, H., Wang, Z., Yang, S., Yang, H., & Gong, Y. (2020, June). Influence Among Preferences and Its Transformation to Behaviors in Groups. In International Conference on Group Decision and Negotiation (pp. 104-119). Springer, Cham.
  • Luthra, M., Izquierdo, E. J., & Todd, P. M. (2020, July). Cognition Evolves with the Emergence of Environmental Patchiness. In Artificial Life Conference Proceedings (pp. 450-458). One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press.
  • Luurssen-Masurel, N., Weel, A., Hazes, J., & De Jong, P. (2020). OP0283 COMPARING COST-UTILITY OF DMARDS IN SERONEGATIVE RHEUMATOID ARTHRITIS PATIENTS; A TREACH SUBANALYSIS.
  • Lv, X., Li, N., Xu, X., & Yang, Y. (2020). Understanding the emergence and development of online travel agencies: a dynamic evaluation and simulation approach. Internet Research.
  • Lynch, C. J., Diallo, S. Y., Kavak, H., & Padilla, J. J. (2020). A content analysis-based approach to explore simulation verification and identify its current challenges. Plos one, 15(5), e0232929.
  • Maestro-Prieto, J. A., Rodríguez, S., Casado, R., & Corchado, J. M. (2020). Agent organisations: from independent agents to virtual organisations and societies of agents. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(4), 55-70.
  • Magessi, N. T., & Antunes, L. (2020). Influences of Innovation in Market Value. In Advances in Social Simulation (pp. 291-305). Springer, Cham.
  • Maggi, E., & Vallino, E. (2020). Price-based and motivation-based policies for sustainable urban commuting: An agent-based model. Research in Transportation Business & Management, 100588.
  • Mahdavi, A. (2020). Bringing HIM closer to HER.
  • Mahmood, B. M., & Dabdawb, M. M. (2020). The Pandemic COVID-19 Infection Spreading Spatial Aspects: A Network-Based Software Approach. AL-Rafidain Journal of Computer Sciences and Mathematics, 14(1), 159-170.
  • Manson, S., An, L., Clarke, K. C., Heppenstall, A., Koch, J., Krzyzanowski, B., ... & Tesfatsion, L. (2020). Methodological issues of spatial agent-based models. JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 23(1).
  • Mao, C., Yu, X., Zhou, Q., Harms, R., & Fang, G. (2020). Knowledge growth in university-industry innovation networks–Results from a simulation study. Technological forecasting and social change, 151, 119746.
  • Maqbool, A., Afzal, F., & Razia, A. (2020). Disaster Mitigation in Urban Pakistan Using Agent Based Modeling with GIS. ISPRS International Journal of Geo-Information, 9(4), 203.
  • Maradan, M. (2020). Uncertainty in deliberate lexical interventions: Exploring Esperanto speakers’ opinions through corpora.
  • Marconi, L., & Cecconi, F. (2020). Opinion dynamics and consensus formation in a Deffuant model with extremists and moderates. arXiv preprint arXiv:2010.01534.
  • Mardani, S., Rahman, A., & Nafissi, N. (2020). An Agent-based Modeling for Breast Tissue Simulation and the Growth and Spread of Tumor in Various Breast Cancer States. Journal of Health and Biomedical Informatics, 6(4), 272-287.
  • Marilisa, C., Chiara, L., Mercatali, L., Ibrahim, T., & Emanuele, G. (2020). An in-silico study of cancer cell survival and spatial distribution within a 3D microenvironment. Scientific Reports (Nature Publisher Group), 10(1).
  • Marshall, S. (2020). Modelling the impact of alternative educational qualifications on the New Zealand higher education system.
  • Martin, Bain, Swanson, Horn & Wilensky (2020). Building Blocks: Designing scientific, domain-specific block-based modeling environments. ICLS, Nashville, TN.
  • Martin, K., Horn, M., & Wilensky, U. (2020). Constructivist Dialogue Mapping Analysis of Ant Adaptation. Informatics in Education, 18(1).
  • Martin, K., & Sengupta, P. (2020). Multi-Agent Simulations of Intra-colony Violence in Ants. Proceedings of the International Conference of Complex Systems.
  • Martin Bicher, S., Rippinger, C., Urach, C., Brunmeir, D., Siebert, U., & Popper, N. (2020). Evaluation of Contact-Tracing Policies Against the Spread of SARS-CoV-2 in Austria–An Agent-Based Simulation.
  • Martins, A. C. (2020). Senescence, change, and competition: when the desire to pick one model harms our understanding. arXiv preprint arXiv:2011.04172.
  • Marvuglia, A., Koppelaar, R., & Rugani, B. (2020). The effect of green roofs on the reduction of mortality due to heatwaves: Results from the application of a spatial microsimulation model to four European cities. Ecological Modelling, 109351.
  • Matsuda, M., Kondo, T., Kawai, W., Hamanaka, J., Matsushita, N., Chino, S., ... & Kimura, F. (2020). E-Catalogues of Equipment for Constructing an Injection Molding Digital Eco-Factory. In EcoDesign and Sustainability I (pp. 501-516). Springer, Singapore.
  • Matsumoto, Y. (2020). Effects of mucus trail following on the distance between individuals of opposite sex and its influence on the evolution of the trait in the Ezo abalone Haliotis discus hannai. PeerJ, 8, e8710.
  • Mazar, M., Bettayeb, B., Klement, N., & LOUIS, A. (2020). Dynamic scheduling of robotic mildew treatmentby UV-c in horticulture.
  • McAlpine, A., Kiss, L., Zimmerman, C., & Chalabi, Z. (2020). Agent-based modeling for migration and modern slavery research: a systematic review. Journal of Computational Social Science, 1-90.
  • McDonald, W. (2020). Design and Implementation of an Agent-Based Model of Pertussis with Performance Considerations (Doctoral dissertation, University of Saskatchewan).
  • McGill, E., Petticrew, M., Marks, D., McGrath, M., Rinaldi, C., & Egan, M. (2020). Applying a complex systems perspective to alcohol consumption and the prevention of alcohol‐related harms in the 21st century: a scoping review. Addiction.
  • McGill, M. M., & Decker, A. (2020, June). Tools, Languages, and Environments Used in Primary and Secondary Computing Education. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 103-109).
  • McKay, V. R., Cambey, C. L., Combs, T. B., Stubbs, A. W., Pichon, L. C., & Gaur, A. H. (2020). Using a Modeling-Based Approach to Assess and Optimize HIV Linkage to Care Services. AIDS and Behavior, 1-11.
  • McLean, A., McDonald, W., & Goodridge, D. (2020). Simulation Modeling as a Novel and Promising Strategy for Improving Success Rates With Research Funding Applications: A Constructive Thought Experiment. JMIR Nursing, 3(1), e18983.
  • McMullen, P. R. (2020). An Agent-Based Approach to the Newsvendor Problem with Price-Dependent Demand. American Journal of Operations Research, 10(4), 101-110.
  • McMullen, P. R. (2020). Social Distancing via Coulomb’s Law. Applied Mathematics, 11(07), 532.
  • Meles, T., & Ryan, L. (2020). Adoption of Renewable Home Heating Systems: An Agent-Based Model of Heat Pump Systems in Ireland.
  • Mellacher, P., & Scheuer, T. (2020). Wage Inequality, Labor Market Polarization and Skill-Biased Technological Change: An Evolutionary (Agent-Based) Approach. Computational Economics, 1-46.
  • Menczer, F., Fortunato, S., & Davis, C. A. (2020). A First Course in Network Science. Cambridge University Press.
  • Meskini, F. Z., & Aboulaich, R. (2020, June). A New Cooperative Insurance Based On Blockchain Technology: Six Simulations To Evaluate The Model. In 2020 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-7). IEEE.
  • Miao, H., Hashemi-Beni, L., Mulrooney, T., Kurkalova, L. A., Liang, C. L., Jha, M., & Monty, G. (2020). SPATIAL DIFFERENCES IN FRESH VEGETABLE SPENDING: A CASE STUDY IN GUILFORD COUNTY, NORTH CAROLINA. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 44, 73-77.
  • Mierlo, S. V., Vangheluwe, H., Breslav, S., Goldstein, R., & Khan, A. (2020). Extending Explicitly Modelled Simulation Debugging Environments with Dynamic Structure. ACM Transactions on Modeling and Computer Simulation (TOMACS), 30(1), 1-25.
  • Milne, R. J., Cotfas, L. A., Delcea, C., Crăciun, L., & Molănescu, A. G. (2020). Adapting the reverse pyramid airplane boarding method for social distancing in times of COVID-19. Plos one, 15(11), e0242131.
  • Milne, R. J., Cotfas, L. A., Delcea, C., Salari, M., Crăciun, L., & Molănescu, A. G. (2020). Airplane Boarding Method for Passenger Groups When Using Apron Buses. IEEE Access, 8, 18019-18035.
  • Milne, R. J., Delcea, C., & Cotfas, L. A. (2020). Airplane Boarding Methods that Reduce Risk from COVID-19. Safety Science, 105061.
  • Mintram, K. S., Maynard, S. K., Brown, A. R., Boyd, R., Johnston, A. S. A., Sibly, R. M., ... & Tyler, C. R. (2020). Applying a Mechanistic Model to Predict Interacting Effects of Chemical Exposure and Food Availability on Fish Populations. Aquatic Toxicology, 105483.
  • Misra, A. (2020). Entrepreneurship in the Rural Labour Market: An Agent-Based Modelling Approach (Doctoral dissertation, University of Essex).
  • Modu, B., Polovina, N., & Konur, S. (2020). Agent-Based Modelling of Malaria Transmission Dynamics. arXiv preprint arXiv:2004.06477.
  • Montes de Oca, E. S., Suppi, R., De Giusti, L. C., & Naiouf, M. (2020). Green High Performance Simulation for AMB models of Aedes aegypti. Journal of Computer Science & Technology, 20.
  • Moradi, S., & Nejat, A. (2020). RecovUS: An Agent-Based Model of Post-Disaster Household Recovery. Journal of Artificial Societies and Social Simulation, 23(4), 1-13.
  • Mozahem, N. A. (2020). Social cognitive theory and women’s career choices: an agent—based model simulation. Computational and Mathematical Organization Theory, 1-26.
  • Murphy, K. J., Ciuti, S., & Kane, A. (2020). An introduction to agent‐based models as an accessible surrogate to field‐based research and teaching. Ecology and Evolution.
  • Muschett, G., & Morales, N. S. (2020). Using Ecological Modelling to Assess the Long-Term Survival of the West-Indian Manatee (Trichechus manatus) in the Panama Canal. Water, 12(5), 1275.
  • Nagasawa, R., Mas, E., Moya, L., & Koshimura, S. (2020). Multi-UAV path planning methodology for postdisaster building damage surveying.
  • Nakagawa, M., Bahr, K., & Lo-Iacono-Ferreira, V. (2020). Human Aspects of Project Management: Agent-Based Modeling. In Project Management and Engineering Research (pp. 117-129). Springer, Cham.
  • Naredo, E., Ryan, C., Guevara, I., Margaria, T., Urbano, P., & Trujillo, L. (2020, July). General controllers evolved through grammatical evolution with a divergent search. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 243-244).
  • Nathan, M. J., & Swart, M. I. (2020). Materialist epistemology lends design wings: educational design as an embodied process. Educational Technology Research and Development, 1-30.
  • Naufel, L. R. M. (2020). Complex Systems Approach for Simulation & Analysis of Socio-Technical Infrastructure Systems: An Empirical Demonstration (Doctoral dissertation, Arizona State University).
  • Navarro-Meneses, F. J. (2020). Agile and Value Creation in Agent-Based Social Simulation. Journal of Creating Value, 2394964320961903.
  • Nery, T. H. D. O. (2020). Segregações no curso de licenciatura em Matemática na perspectiva da segregação de Schelling.[HTML]
  • Nguyen, M. L. K. N., Megiddo, D. I., & Howick, P. S. (2020). Hybrid Simulation for Modelling Healthcare-Associated Infections: Promising but Challenging. Clinical Infectious Diseases.
  • Nguyen, T. H., & Jung, J. J. (2020). Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles. Neural Computing and Applications, 1-10.
  • Niazi, M. A., Iantovics, L. B., & Temkin, A. (2020). Review of “The Model Thinker” by Scott Page.
  • Nishi, T., Matsuda, M., Hasegawa, M., Alizadeh, R., Liu, Z., & Terunuma, T. (2020). Automatic Construction of Virtual Supply Chain as Multi-Agent System Using Enterprise E-Catalogues. International Journal of Automation Technology, 14(5), 713-722.
  • Noah, M. S. A. (2020). BUILDING INTELLIGENT AUTONOMOUS AGENTS AND MULTI AGENTS USING THE FETCH. AI DECENTRALISED OPEN ECONOMIC FRAMEWORK.
  • Noemi, G., Samanta, R., Federica, V., Alessandra, B., & Gabriella, B. (2020). Agent-Based Modeling and Simulation of Care Delivery for Patients With Thrombotic and Bleeding Disorders. Studies in health technology and informatics, 270, 1193-1194.
  • Norouziasl, S., & Jafari, A. (2020, November). Comparing Office Layouts Regarding Lighting Energy Saving Potentials Using Agent-Based Real-Time Simulation of Occupancy Behavioral Patterns. In Construction Research Congress 2020: Computer Applications (pp. 972-981). Reston, VA: American Society of Civil Engineers.
  • Norouziasl, S., Jafari, A., & Wang, C. (2020). An agent-based simulation of occupancy schedule in office buildings. Building and Environment, 107352.
  • Nwokoye, C. H., Umeugoji, C., & Umeh, I. (2020). Evaluating Degrees of Differential Infections on Sensor Networks’ Features Using the SEjIjR-V Epidemic Model. Egyptian Computer Science Journal, 44(3).
  • Oh, H., Trinh, M. P., Vang, C., & Becerra, D. (2020). Addressing Barriers to Primary Care Access for Latinos in the US: An Agent-Based Model. Journal of the Society for Social Work and Research, 11(2), 000-000.
  • Olenick, J. D. (2020). Still Learning: Introducing the Learning Transfer Model, a Formal Model of Transfer (Doctoral dissertation, Michigan State University).
  • Oliveira, J. D., de Borba Campos, M., & Paixão-Cortes, V. S. M. (2020, July). Usable and Accessible Robot Programming System for People Who Are Visually Impaired. In International Conference on Human-Computer Interaction (pp. 445-464). Springer, Cham.
  • O'Neill, J. (2020). The Ability to Unite Under Crisis: Ethnic Group Consolidation During Ethnic Conflict in Latin America (Doctoral dissertation, The Ohio State University).
  • Ornelas, N. O. 2020. An Ecosystem: Computational Thinking, Project-Based Learning & Logo.
  • Orsi, F., Scuttari, A., & Marcher, A. (2020). How much traffic is too much? Finding the right vehicle quota for a scenic mountain road in the Italian Alps. Case Studies on Transport Policy, 8, 1270-1284. https://doi.org/10.1016/j.cstp.2020.08.007
  • Oruro, E. M., Pardo, G. V., Lucion, A. B., Calcagnotto, M. E., & Idiart, M. A. (2020). Maturation of pyramidal cells in anterior piriform cortex may be sufficient to explain the end of early olfactory learning in rats. Learning & Memory, 27(12), 20-32.
  • Oruro, E. M., Pardo, G. V., Lucion, A. B., Calcagnotto, M. E., & Idiart, M. A. (2020). The maturational characteristics of the GABA input in the anterior piriform cortex may also contribute to the rapid learning of the maternal odor during the sensitive period. Learning & Memory, 27(12), 493-502.
  • O'Shea, T., Bates, P., & Neal, J. (2020). Testing the impact of direct and indirect flood warnings on population behaviour using an agent-based model. Natural Hazards and Earth System Sciences, 20(8), 2281-2305.
  • Osterritter, L. J., & Carley, K. M. (2020, October). Modeling Interventions for Insider Threat. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 55-64). Springer, Cham.
  • Pala, D., Annovazzi-Lodi, L., Bellazzi, R., Fiscante, N., Franzini, M., Larizza, C., ... & Casella, V. (2020). THE KEY ROLE OF GEOGRAPHIC INFORMATION IN EXPOSOMICS: THE EXAMPLE OF THE H2020 PULSE PROJECT. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 283-289.
  • Palau, A. S., Liang, Z., Lütgehetmann, D., & Parlikad, A. K. (2020). Collaborative Prognostics in Social Asset Networks. In Value Based and Intelligent Asset Management (pp. 329-349). Springer, Cham.
  • Pal, C. V., Leon, F., Paprzycki, M., & Ganzha, M. (2020). A Review of Platforms for the Development of Agent Systems. arXiv preprint arXiv:2007.08961.
  • Panorkou, N., & Germia, E. (2020). Examining Students’ Quantitative Reasoning in a Virtual Ecosystem Simulation of the Water Cycle.
  • Panov, S. (2020). To Derogate (and Notify), or Not to Derogate (and Not to Notify), that is the Question!: An Analysis of the Legal Framework of the COVID-19 State of Emergency in the Republic of Bulgaria and ECHR Practice.
  • Papastamatiou, Y. P., Bodey, T. W., Caselle, J. E., Bradley, D., Freeman, R., Friedlander, A. M., & Jacoby, D. M. (2020). Multiyear social stability and social information use in reef sharks with diel fission–fusion dynamics. Proceedings of the Royal Society B, 287(1932), 20201063.
  • Papparlardo, F., Russo, G., Pennisi, M., Palumbo, G. A. P., Sgroi, G., Motta, S., & Maimone, D. (2020). The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis.
  • Park, H. (2020). Evolutionary Ecological Model of Defense Activation Disorders Via the Marginal Value Theorem. Psychiatry Investigation.
  • Park, S. H. (2020). Does capitalism need a government to be nice: Robert Axelrod and his iterated prisoner's dilemma computer tournament [Original paper]. Frontiers in Education Technology, 3(2).
  • Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. K. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention, 136, 105405.
  • Parthiban, M., & Ram Kumar, P. (2020). Applications of discrete event simulation: A literature review.
  • Pathak, A., Mohan, V. M., & Banerjee, A. (2020). An agent based modelling approach to study lockdown efficacy for infectious disease spreads. medRxiv.
  • Paudel, R. (2020). A Systems Approach to Analyze Household Vulnerability to Food Insecurity in Rural Southern Mali Using a Spatially-Explicit Integrated Social and Biophysical Model (Doctoral dissertation, Michigan State University).
  • Paul, R., Eaton, S. E., Laird, G., Nelson, N., & Brennan, R. (2020). USING AGENT-BASED MODELLING FOR EER EXPERIMENTAL DESIGN: PRELIMINARY VALIDATION BASED ON STUDENT CHEATING BEHAVIOURS. Proceedings of the Canadian Engineering Education Association (CEEA).
  • Pawlowski, T., & van Dinther, C. (2020). Assessing the Impact of Electric Vehicle Charging Behavior on the Distribution Grid.
  • Payette, N. (2020). Collaborating Like Professionals: Integrating NetLogo and GitHub. In Advances in Social Simulation (pp. 343-348). Springer, Cham.
  • Pea, R., Grover, S., & Ventures, L. G. (2020). Weaving the Fabric of Adaptive STEM Learning Environments Across Domains and Settings. The Interdisciplinarity of the Learning Sciences.
  • Peel, A., Dabholkar, S., Anton, G., Wu, S., Wilensky, U., & Horn, M. (2020). A Case Study of Teacher Professional Growth Through Co-design and Implementation of Computationally Enriched Biology Units.
  • Petrasova, A., Gaydos, D. A., Petras, V., Jones, C. M., Mitasova, H., & Meentemeyer, R. K. (2020). Geospatial simulation steering for adaptive management. Environmental Modelling & Software, 104801.
  • Phetheet, J. (2020). Simulating and Analyzing Use of Water and Renewable Energy in Agricultural Areas Using FEWCalc and DSSAT (Doctoral dissertation, University of Kansas).
  • Phetheet, J., Hill, M. C., Barron, R. W., Rossi, M. W., Amanor-Boadu, V., Wu, H., & Kisekka, I. (2020). Consequences of climate change on food-energy-water systems in arid regions without agricultural adaptation, analyzed using FEWCalc and DSSAT. Resources, Conservation and Recycling, 105309.
  • Philips, I. (2020). An Agent Based Model to Estimate Lynx Dispersal if Re-Introduced to Scotland. Applied Spatial Analysis and Policy, 13(1), 161-185.
  • Phuttharak, J., & W Loke, S. (2020). Iterative Spatial Crowdsourcing in Peer-to-Peer Opportunistic Networks. Electronics, 9(7), 1085.
  • Platas-López, A., Guerra-Hernández, A., Cruz-Ramírez, N., Quiroz-Castellanos, M., Grimaldo, F., Paolucci, M., & Cecconi, F. (2020). Towards an Agent-Based Model for the Analysis of Macroeconomic Signals. In Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications (pp. 551-565). Springer, Cham.
  • Pobuda, P. (2020). The Digital Twin of the Economy: Proposed Tool for Policy Design and Evaluation. Real-World Economics Review, issue no. 94, pp. 140-148.
  • Premo, L. S. (2020). Population Size Limits the Coefficient of Variation in Continuous Traits Affected by Proportional Copying Error (and Why This Matters for Studying Cultural Transmission). Journal of Archaeological Method and Theory, 1-23.
  • Proctor, C. C. (2020). Supporting Critical Computational Literacies Through Interactive Storytelling (Doctoral dissertation, Stanford University).
  • Pulley, M., Rodriguez, L., Lewis, M., Kohler, B., & Gordillo, L. (2020). Guiding Students to Understand Functional Responses: Holling's Disc Experiment Revisited. PRIMUS, 1-20.
  • Pumain, D. (Ed.). (2020). Geographical Modelling: Cities and Territories. John Wiley & Sons.
  • Punzalan, A. (2020). Predicting Condor Range Expansion in California to Reduce Development Threats. 2020-CSU Theses and Dissertations.
  • Putriani, D., Ghani, G. M., & Kartiwi, M. (2020). EXPLORATION OF AGENT-BASED SIMULATION: THE MULTIPLIER EFFECT OF ZAKAH ON ECONOMIC GROWTH1. Journal of Islamic Monetary Economics and Finance, 6(3), 641-666.
  • Qian, C., Yu, K., & Gu, H. (2020). Flexibility mechanisms in a dynamic distribution network. Journal of Business & Industrial Marketing.
  • Qin, M., Chen, L., Jing, N., & Chen, Q. (2020, July). Simulation Research on the Formation Behavior of a Compound College Students’ Entrepreneurship Team Based on NetLogo. In 2020 International Conference on Advanced Education, Management and Social Science (AEMSS2020) (pp. 236-239). Atlantis Press.
  • Raczynski, S. (2020). Prey-Predator Models Revisited: Uncertainty, Herd Instinct, Fear, Limited Food, Epidemics, Evolution, and Competition. In Interacting Complexities of Herds and Social Organizations (pp. 107-132). Springer, Singapore.
  • Radzvilas, M., De Pretis, F., Peden, W., Tortoli, D., & Osimani, B. (2020). Double blind vs. open review: an evolutionary game logit-simulating the behavior of authors and reviewers. arXiv preprint arXiv:2011.07797.
  • Rahman, A., Naufal, F., & Partiwi, S. G. (2020, June). Measuring the entropy of organizational culture using agent-based simulation. In Managing Learning Organization in Industry 4.0: Proceedings of the International Seminar and Conference on Learning Organization (ISCLO 2019), Bandung, Indonesia, October 9-10, 2019 (p. 109). Routledge.
  • Rahmoeller, M., & Steinweg, J. M. (2020). Implementation of a New Quantitative Biology Course: Assessment of Students’ Abilities and Confidence. PRIMUS, 1-35.
  • Railsback, S. F., Harvey, B. C., & Ayllón, D. (2020). Contingent trade-off decisions with feedbacks in cyclical environments: testing alternative theories. Behavioral Ecology.
  • Railsback, S. F., Harvey, B. C., & Ayllón, D. (2020). Importance of the Daily Light Cycle in Population‐Habitat Relations: A Simulation Study. Transactions of the American Fisheries Society.
  • Raimbault, J. (2020). An agent-based model of interdisciplinary interactions in science. arXiv preprint arXiv:2006.16399.
  • Rajabi, A., Talebzadehhosseini, S., & Garibay, I. (2020). Resistance of communities against disinformation. arXiv preprint arXiv:2004.00379.
  • Ralph, M. (2020). Emergent patterns in deterministic modelling. International Journal of Mathematical Education in Science and Technology, 1-11.
  • Razakatiana, M., Kolski, C., Mandiau, R., & Mahatody, T. (2020, November). Game Theory-based Human-Assistant Agent Interaction Model: Feasibility Study for a Complex Task. In Proceedings of the 8th International Conference on Human-Agent Interaction (pp. 187-195).
  • Razakatiana, M., Kolski, C., Mandiau, R., & Mahatody, T. (2020, June). Human-agent Interaction based on Game Theory: Case of a road traffic supervision task. In 2020 13th International Conference on Human System Interaction (HSI) (pp. 88-93). IEEE.
  • Recio, M. R., Singer, A., Wabakken, P., & Sand, H. (2020). Agent-based models predict patterns and identify constraints of large carnivore recolonizations, a case study of wolves in Scandinavia. Biological Conservation, 251, 108752.
  • Reddy, A. S., Subha, T. D., Suresh, T., & Subash, T. D. (2020). A DTM Research based on the strategic process. Materials Today: Proceedings.
  • Remiszewski, K. (2020). NUTRIENT CYCLING ALONG MICROBIAL AND LITHOLOGIC GRADIENTS AND FOSTERING STUDENT SELF-CONFIDENCE IN SCIENCE.
  • Reymond, D. (2020). Patents information for humanities research: could there be something?. Iberoamerican Journal of Science Measurement and Communication, 1(1).
  • Ribas-Xirgo, L. (2020, November). Multi-agent System Model of Taxi Fleets. In Workshop of Physical Agents (pp. 123-134). Springer, Cham.
  • Riedel, L., Herdeanu, B., Mack, H., Sevinchan, Y., & Weninger, J. (2020). Utopia: a comprehensive and collaborative modeling framework for complex and evolving systems. Journal of Open Source Software, 5(53), 2165.
  • Rizana, A. F., & Ramadhan, F. (2020). Penerapan Agent-Based Simulation dalam Memprediksi Penggunaan Berkelanjutan Sistem ERP. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 7(2).
  • Roach, A., Scott, I., Macfarlane, G., Jones, G. T., & Macgregor, A. (2020). OP0284 AN AGENT-BASED SIMULATION OF THE EFFECTS OF VARYING TIME TO TREATMENT WITH BIOLOGICAL AGENTS ON PATIENT HEALTH AND COST IN AXIAL SPONDYLOARTHRITIS USING NATIONAL REGISTER DATA.
  • Robertson, J. J., Swannack, T. M., McGarrity, M., & Schwalb, A. N. (2020). Zebra mussel invasion of Texas lakes: estimating dispersal potential via boats. Biological Invasions, 1-31.
  • Robeva, R. S., Jungck, J. R., & Gross, L. J. (2020). Changing the nature of quantitative biology education: data science as a driver. Bulletin of Mathematical Biology, 82(10), 1-30.
  • Rodriguez Recio, M., Singer, A., Wabakken, P., & Sand, H. (2020). Agent-based models predict patterns and identify constraints of large carnivore recolonizations, a case study of wolves in Scandinavia.
  • Rosenbusch, H., Röttger, J., & Rosenbusch, D. (2020). Would Chuck Norris certainly win the Hunger Games?: Simulating the result reliability of Battle Royale games through agent-based models. Simulation and Gaming.
  • Ruiz-Martin, C., Wainer, G., & Lopez-Paredes, A. (2020). Discrete-Event Modeling and Simulation of Diffusion Processes in Multiplex Networks. ACM Transactions on Modeling and Computer Simulation (TOMACS), 31(1), 1-32.
  • Ruscheinski, A., Wilsdorf, P., Zimmermann, J., van Rienen, U., & Uhrmacher, A. M. (2020). An Artifact-based Workflow for Finite-Element Simulation Studies. arXiv preprint arXiv:2010.07625.
  • Russo, G., Reche, P., Pennisi, M., & Pappalardo, F. (2020). The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opinion on Drug Discovery, 1-15.
  • Ryder, E., Ruiz, C., Weaver, S., & Gegear, R. (2020). Choosing Your Own Adventure: Engaging the New Learning Society through Integrative Curriculum Design. EPiC Series in Education Science, 3, 188-199.
  • Sabzian, H., Shafia, M. A., Ghazanfari, M., & Bonyadi Naeini, A. (2020). Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory. Sustainability, 12(7), 2904.
  • Sadler, T. D., Friedrichsen, P., Zangori, L., & Ke, L. (2020). Technology-Supported Professional Development for Collaborative Design of COVID-19 Instructional Materials. Journal of Technology and Teacher Education, 28(2), 171-177.
  • Saito, M., & Hayashi, H. (2020). P2P Human-Resource Sharing and Its Redistribution Strategy of Stable Coin.
  • Sakashita, T., Watanabe, S., Hanaoka, H., Ohshima, Y., Ikoma, Y., Ukon, N., ... & Ishioka, N. S. (2020). Absorbed dose simulation of meta-211 At-astato-benzylguanidine using pharmacokinetics of 131 I-MIBG and a novel dose conversion method, RAP. Annals of Nuclear Medicine, 1-11.
  • Sánchez-Cartas, J. M. (2020, June). Platform competition and consumer’s decisions: An ABM simulation of pricing with different behavioral rules.
  • Santoro, M., Mazzetti, P., & Nativi, S. (2020). The VLab Framework: An Orchestrator Component to Support Data to Knowledge Transition. Remote Sensing, 12(11), 1795.
  • Santos, F., Nunes, I., & Bazzan, A. L. (2020). Quantitatively Assessing the Benefits of Model-driven Development in Agent-based Modeling and Simulation. Simulation Modelling Practice and Theory, 102126.
  • Santos, M., Cajaiba, R., Gonzalez, D., Leote, P., Ferreira, D., Bastos, R., ... & Cabral, J. A. (2020). How accurate are estimates of flower visitation rates by pollinators? Lessons from a spatially explicit agent-based model. Ecological Informatics, 101077.
  • Saoud, M. S., Boubetra, A., & Attia, S. (2020). A Simulation Knowledge Extraction-Based Decision Support System for the Healthcare Emergency. Hospital Management and Emergency Medicine: Breakthroughs in Research and Practice: Breakthroughs in Research and Practice, 192.
  • Sapienza, A., & Falcone, R. (2020). Evaluating agents’ trustworthiness within virtual societies in case of no direct experience. Cognitive Systems Research.
  • Sarmah, D. T., Bairagi, N., & Chatterjee, S. (2020). Tracing the footsteps of autophagy in computational biology. Briefings in Bioinformatics.
  • Sauter, J. A., Bixler, K., Kitchen, S., & Chase, R. (2020, April). RF emitter localization with robotic swarms. In Unmanned Systems Technology XXII (Vol. 11425, p. 114250D). International Society for Optics and Photonics.
  • Savaglio, C., Ganzha, M., Paprzycki, M., Bădică, C., Ivanović, M., & Fortino, G. (2020). Agent-based Internet of Things: State-of-the-art and research challenges. Future Generation Computer Systems, 102, 1038-1053.
  • Saxena, N. (2020). Working'Failure'into your Learning Design. The Emerging Learning Design Journal, 7(1), 2
  • Scataglini, S., & Perez Luque, E. (2020). Closing the Gender Gap in DHM. In 6th International Digital Human Modeling Symposium, August 31–September 2, 2020, Skövde, Sweden (Vol. 11, pp. 408-418). IOS Press.
  • Schaff, F. (2020). Conceptualising Artificial Anasazi with an Explicit Knowledge Representation and Population Model. In Advances in Social Simulation (pp. 399-403). Springer, Cham.
  • Schlaile, M. P. (2020). A Case for Economemetics? Why Evolutionary Economists Should Re-evaluate the (F) utility of Memetics. In Memetics and Evolutionary Economics (pp. 33-68). Springer, Cham.
  • Schleicher, J. (2020). Introduction to In Silico Modeling to Study ROS Dynamics. In Reactive Oxygen Species (pp. 1-32). Humana, New York, NY.
  • Schloesser, D. S., Hollenbeck, D., & Kello, C. T. (2020). Social Foraging in Groups of Search Agents with Human Intervention.
  • Schlüter, J., Bossert, A., Rössy, P., & Kersting, M. (2020). Impact assessment of autonomous demand responsive transport as a link between urban and rural areas. Research in Transportation Business & Management, 100613.
  • Schmolke, A., Abi‐Akar, F., Roy, C., Galic, N., & Hinarejos, S. (2020). Simulating Honey Bee Large‐Scale Colony Feeding Studies Using the BEEHAVE Model. Part I: Model Validation. Environmental Toxicology and Chemistry.
  • Schwarz, M., Auzepy, Q., & Knoeri, C. (2020). Can electricity pricing leverage electric vehicles and battery storage to integrate high shares of solar photovoltaics?. Applied Energy, 277, 115548.
  • Schwarz, N., Dressler, G., Frank, K., Jager, W., Janssen, M., Müller, B., ... & Groeneveld, J. (2020). Formalising theories of human decision-making for agent-based modelling of social-ecological systems: practical lessons learned and ways forward. Socio-Environmental Systems Modelling, 2, 16340-16340.
  • Sedigh, A. H. A., Purvis, M. K., Savarimuthu, B. T. R., Frantz, C. K., & Purvis, M. A. (2020). Impact of different belief facets on agents' decision--a refined cognitive architecture. arXiv preprint arXiv:2004.11858.
  • Sedigh, A. H. A., Purvis, M. K., Savarimuthu, B. T. R., Purvis, M. A., & Frantz, C. K. (2020). Impact of meta-roles on the evolution of organisational institutions. arXiv preprint arXiv:2008.04096.
  • Sellers, M. W., Sayama, H., & Pape, A. D. (2020). Simulating Systems Thinking under Bounded Rationality. Complexity, 2020.
  • Sells, S. N., & Mitchell, M. S. (2020). The economics of territory selection. Ecological Modelling, 438, 109329.
  • Sengupta, A., & Sena, V. (2020). Impact of Open Innovation on Industries and Firms–A Dynamic Complex Systems View. Technological Forecasting and Social Change.
  • Sfa, F. E., Nemiche, M., & Rayd, H. (2020). A generic macroscopic cellular automata model for land use change: The case of the Drâa valley. Ecological Complexity, 43, 100851.
  • Shaaban, M. (2020). The roadmap to energy security in Egypt. In M. Brzoska & J. Scheffran (Eds.), Climate change, security risks, and violent conflicts: Essays from integrated climate research in Hamburg (pp. 83-102). Hamburg University Press.
  • Shafiq, A. (2020). Contemporary debates in Islamic monetary economics. Islamic Monetary Economics: Finance and Banking in Contemporary Muslim Economies, 215.
  • Shanaa, M., & Abdallah, S. (2020, November). Agent-based simulation for COVID-19 outbreak within a semi-closed environment. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) (pp. 231-236). IEEE.
  • Shapiro, R. B. New and Future Coding Paradigms: Interview with R. Benjamin Shapiro. In Holbert, N., Berland, M., & Kafai, Y. B. (eds.), Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs, 369.
  • Shareff, R. (2020). Agricultural Contexts as a Platform for Science and Technology: A Cross-Cultural Examination of Classroom, Community, and Modeling Dynamics (Doctoral dissertation, UC Berkeley).
  • Sharma, A., Tale, E., Hernandez, M., & Phuong, V. (2020). Engaging students with computing and climate change through a course in Scientific Computing. Journal of STEM Education: Innovations and Research, 20(2).
  • Sharma, D., Khandekar, N., & Sachdeva, K. (2020). Exploratory agent-based model to understand migration scenarios: a study from the Indian Himalayan Region, Uttarakhand. Development in Practice, 1-12.
  • Shi, L., Zhang, L., & Lu, Y. (2020). Evaluating social network-based weight loss interventions in Chinese population: An agent-based simulation. PloS one, 15(8), e0236716.
  • Shi, X., Sun, Z., & Zhu, T. (2020). Multi-Agent Traffic Simulation Considering Heterogeneous Driving Behaviors and Collision. In CICTP 2020 (pp. 4646-4659).
  • Shiflet, A. B., Shiflet, G. W., Cannataro, M., Guzzi, P. H., Zucco, C., & Kaplun, D. A. (2020). What Are the Chances?—Hidden Markov Models. In An Introduction to Undergraduate Research in Computational and Mathematical Biology (pp. 353-400). Birkhäuser, Cham.
  • Shinde S.B., Kurhekar M.P. (2020) Agent-Based Modeling of the Adaptive Immune System Using Netlogo Simulation Tool. In: Das K., Bansal J., Deep K., Nagar A., Pathipooranam P., Naidu R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore
  • Shoukat, A., & Moghadas, S. M. (2020). Agent-Based Modelling: An Overview with Application to Disease Dynamics. arXiv preprint arXiv:2007.04192.
  • Shrinidhi, K. R., Sneha, V., Jain, V., & Nair, M. K. (2020). Multi-agent-Based Systems in Machine Learning and Its Practical Case Studies. In Machine Learning for Intelligent Decision Science (pp. 153-189). Springer, Singapore.
  • Sibbel, D. M. (2020). Agent-Based Modelling: How climate policies influence population dynamics (Master's thesis).
  • Sidiropoulos, G., Kiourt, C., & Moussiades, L. (2020). Crowd simulation for crisis management: the outcomes of the last decade. arXiv preprint arXiv:2006.01216.
  • Sikk, K., & Caruso, G. (2020). A spatially explicit agent-based model of central place foraging theory and its explanatory power for hunter-gatherers settlement patterns formation processes. Adaptive Behavior, 1059712320922915.
  • Silva, A., & Oliveira, M. (2020, January). Simulando o Jogo de Negociação Pit Game em um Sistema Multi-Agentes Implementado com o Framework JaCaMo. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional (pp. 938-948). SBC.
  • Silva J., Varela N., Lezama O.B.P. (2020) Optimizing Street Mobility Through a NetLogo Simulation Environment. In: Smys S., Tavares J., Balas V., Iliyasu A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham
  • Silva, T., Araújo, M., Junior, R., Costa, L., Andrade, J., & Campos, G. (2020, October). Classifying Organizational Structures on Targets in the Cooperative Target Observation. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional (pp. 718-729). SBC.
  • Silva, W. T., Harding, K. C., Marques, G. M., Bäcklin, B. M., Sonne, C., Dietz, R., ... & Desforges, J. P. (2020). Life cycle bioenergetics of the gray seal (Halichoerus grypus) in the Baltic Sea: Population response to environmental stress. Environment International, 145, 106145.
  • Singh, R. K., Sardar, M., & Das, D. (2020). Assembling Multi-Robots Along a Boundary of a Region with Obstacles—A Performance Upgradation. In Advances in Computational Intelligence (pp. 201-212). Springer, Singapore.
  • Singley, A., & Callender Highlander, H. (2020). A Mathematical Model for the Effect of Social Distancing on the Spread of COVID-19. Spora: A Journal of Biomathematics, 6(1), 40-51.
  • Sistrunk, A., Cedeno, V., & Biswas, S. (2020). On synthetic data generation for anomaly detection in complex social networks. arXiv preprint arXiv:2010.13026.
  • Smaldino, P. (2020). How to translate a verbal theory into a formal model.
  • Smaldino, P., & O'Connor, C. (2020). Interdisciplinarity Can Aid the Spread of Better Methods Between Scientific Communities.
  • Sneider, T. (2020). UNETHICAL BEHAVIOR IN ORGANIZATIONS–AN AGENT-BASED APPROACH. In Economic and Social Development (Book of Proceedings), 58th International Scientific Conference on Economic and Social (p. 250).
  • Song, W., & Jablonski, P. G. (2020). Evolution of switchable aposematism: insights from individual-based simulations. PeerJ, 8, e8915.
  • Sopamena, P., Andriansyah, R., & Sopamena, K. (2020). Analysis of Understanding of Student Concepts in Solving Absolute Value Problems. Matematika dan Pembelajaran, 7(2), 42-50.
  • Sopha, B. M., Sakti, S., Prasetia, A. C. G., Dwiansarinopa, M. W., & Cullinane, K. (2020). Simulating long-term performance of regional distribution centers in archipelagic logistics systems. Maritime Economics & Logistics, 1-29.
  • Souza, J. K. G. D. (2020). Modelagem baseada em agentes: possibilidades na Educação Matemática e pesquisa ambiental. [a href=http://200.129.179.47/handle/11612/1773]
  • Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., ... & Gilbert, N. (2020). Computational models that matter during a global pandemic outbreak: A call to action. Journal of Artificial Societies and Social Simulation, 23(2).
  • Stevanovic, A., & Mitrovic, N. (2020). Impact of conflict resolution parameters on combined alternate-directions lane assignment and reservation-based intersection control. European Transport Research Review, 12(1), 1-10.
  • Stiner, S., & Chellamuthu, V. (2020). An Agent-Based Model of West Nile Virus: Predicting the Impact of Public Health Agents and Vaccinations on Horses. Curiosity: Interdisciplinary Journal of Research and Innovation, 44-66.
  • Stork, C. (2020). Exploring self-organisation for car-sharing systems: An agent-based approach.
  • Stroup, W. M., Ares, N., Hurford, A. C., & Lesh, R. (2020). Diversity-by-Design. Foundations for the Future in Mathematics Education, 367.
  • Student, J., Kramer, M. R., & Steinmann, P. (2020). Coasting: model description, global sensitivity analysis, and scenario discovery. MethodsX, 101145.
  • Student, J., Kramer, M. R., & Steinmann, P. (2020). Simulating emerging coastal tourism vulnerabilities: an agent-based modelling approach. Annals of Tourism Research, 85, 103034.
  • Su, T. Y. M. (2020). Internal Migration of Foreign-Born in US: Impacts of Population Concentration and Risk Aversion.
  • Suliman, T. (2020). Understanding the dynamics of even-aged stands of Brutia pine (Pinus brutia Ten.) in the coastal region of Syria based on a distance-independent individual-tree growth model.
  • Sulis, E., Terna, P., Di Leva, A., Boella, G., & Boccuzzi, A. (2020). Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare. Journal of Medical Systems, 44(9), 1-7.
  • Sulis, E., & Tambuscio, M. (2020, October). Simulation of misinformation spreading processes in social networks: an application with netlogo. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 614-618). IEEE.
  • Sullivan, G. B. (2020). Understanding Listeria Dynamics in Produce Operations Using Three Approaches: Sampling, Sequencing, and in silico Modeling (Doctoral dissertation, Cornell University).
  • Sun, T., Bu, F., Liu, X., & Fu, Y. (2020, December). Modeling and Simulation of Group Drug-related Incident Evolution. In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (pp. 1247-1252). IEEE.
  • Sundar, S., Battistoni, C., McNulty, R., Morales, F., Gorky, J., Foley, H., & Dhurjati, P. (2020). An agent-based model to investigate microbial initiation of Alzheimer’s via the olfactory system. Theoretical Biology and Medical Modelling, 17(1), 1-15.
  • Suneetha, C., Rao, S. S., & Ramesh, K. S. (2020). Ideal frequency rendezvousing for multiuser communication (IFRMC) over cognitive radio network. International Journal of Speech Technology, 1-11.
  • Surro, C. J. (2020). Computational Methods and Models in Macroeconomics (Doctoral dissertation, UCLA).
  • Susaki, K., & Kaneda, T. (2020). The Potential of Vision-Driven Agent Simulation: The VD-Walker. In Downtown Dynamics (pp. 187-205). Springer, Tokyo.
  • Swain, A., Devereux, M., & Fagan, W. F. (2020). Deciphering trophic interactions in a mid-Cambrian assemblage. bioRxiv.
  • Swanson, Martin, Sherin & Wilensky (2020). Characterizing Student Theory Building in the Context of Block-Based Computational Modeling. ICLS, Nashville, TN.
  • Swanson, H., Martin, K. (*), Jones, B. (*), Hedayati, M. (*), Wu, S. (*), Sherin, B., Wilensky, U. (2020, April).Characterizing the nature of student theory building in the context of computational modeling activities. Paper accepted for presentation at the annual meeting of the American Education Research Association.
  • Szczepanska, T., Priebe, M., & Schröder, T. (2020). Teaching the Complexity of Urban Systems with Participatory Social Simulation. In Advances in Social Simulation (pp. 427-439). Springer, Cham.
  • Tabasi, M., Alesheikh, A. A., Sofizadeh, A., Saeidian, B., Pradhan, B., & AlAmri, A. (2020). A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran. Parasites & Vectors, 13(1), 1-17.
  • Tabrett, A., & Way, A. M. (2020). A new Monte Carlo simulation tool for designing an archaeological landscape sampling strategy. MethodsX, 101124.
  • Tan, M., Hatef, E., Taghipour, D., Vyas, K., Kharrazi, H., Gottlieb, L., & Weiner, J. (2020). Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities. JMIR Medical Informatics, 8(9), e18084.
  • Tang, W., Grimm, V., Tesfatsion, L., Shook, E., Bennett, D., An, L., ... & Ye, X. (2020). Code Reusability and Transparency of Agent-Based Modeling: A Review from a Cyberinfrastructure Perspective. In High Performance Computing for Geospatial Applications (pp. 115-134). Springer, Cham.
  • Tang, Y., Liu, M., & Sun, Z. (2020). Indirect Effects of Grazing on Wind-Dispersed Elm Seeds in Sparse Woodlands of Northern China. Land, 9(12), 490.
  • Tang, Z., & Zhu, H. (2020). Nonlinear Dynamic Analysis of New Product Diffusion considering Consumer Heterogeneity. Complexity, 2020.
  • Tatar, D., Roschelle, J., & Hegedus, S. (2020). Democratizing Access to Advanced Mathematics (1992–Present). Historical Instructional Design Cases: ID Knowledge in Context and Practice, 283.
  • Tekdoğan, Ö. F., & Saraç, M. (2020). The problems with fractional reserve banking and proposing a shariah-compliant full reserve banking model. Islamic Monetary Economics: Finance and Banking in Contemporary Muslim Economies, 133.
  • Tether, V., Malleson, N., Steenbeek, W., & Birks, D. (2020). Using agent-based models to investigate the presence of edge effects around crime generators and attractors. Agent-Based Modelling for Criminological Theory Testing and Development, 45.
  • Tews, A. C. (2020). Predictive Ecological Modeling of Grey Wolf (Canis lupus) Movement using Agent-Based Modeling and GIS (Doctoral dissertation, University of Colorado Colorado Springs).
  • Thiel, D. (2020). A pricing-based location model for deploying a hydrogen fueling station network. International Journal of Hydrogen Energy.
  • Thiriot, S. (2020). Impact of the Interaction Network on the Dynamics of Word-of-Mouth with Information Seeking. arXiv preprint arXiv:2002.02728.
  • Thomas, Y., Razafimahefa, N. R., Ménesguen, A., & Bacher, C. (2020). Multi-scale interaction processes modulate the population response of a benthic species to global warming. Ecological Modelling, 436, 109295.
  • Thongsukdee, P., & Weerawat, W. (2020). Physician workforce planning and allocation model using agent‐based modeling: A case study in Thailand. The International Journal of Health Planning and Management.
  • Tolk, A., Dinh, K., Comer, K., & Scott, S. (2020, May). Exploratory analysis to address deep uncertainty: using calibratable system models for exploratory simulation of complex missions. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-11).
  • Tolk, A., Harper, A., & Mustafee, N. (2020). Hybrid Models as Transdisciplinary Research Enablers. European Journal of Operational Research.
  • Tomasiello, D. B., Giannotti, M., & Feitosa, F. F. (2020). ACCESS: An agent-based model to explore job accessibility inequalities. Computers, Environment and Urban Systems, 81, 101462.
  • Tosselli, L., Bogado, V., & Martínez, E. (2020). A repeated-negotiation game approach to distributed (re) scheduling of multiple projects using decoupled learning. Simulation Modelling Practice and Theory, 98, 101980.
  • Tran, M., Ngo, M., Pham-Hi, D., & Bui, M. (2020, October). Bayesian Calibration of Hyperparameters in Agent-Based Stock Market. In 2020 RIVF International Conference on Computing and Communication Technologies (RIVF) (pp. 1-6). IEEE.
  • Trinh, L. T., Sano, K., & Hatoyama, A. K. (2020). Modelling and Simulating Head-On Conflict-Solving Behaviour of Motorcycles under Heterogeneous Traffic Condition in Developing Countries. Transportmetrica A: Transport Science, 1-48.
  • Tsai, M. H., & Huang, S. R. (2020). Team Efficiency Estimation for Construction Process Considering the Collaborative Behaviors. Journal of Applied Science and Engineering, 23(1), 79г92.
  • Tseng, S. H., & Son Nguyen, T. (2020). Agent-Based Modeling of Rumor Propagation Using Expected Integrated Mean Squared Error Optimal Design. Applied System Innovation, 3(4), 48.
  • Tuesta, E. F., Bolaños-Pizarro, M., Neves, D. P., Fernández, G., & Axel-Berg, J. (2020). Complex networks for benchmarking in global universities rankings. Scientometrics, 1-21.
  • Tyszberowicz, S., & Faitelson, D. (2020). Emergence in cyber-physical systems: potential and risk. Frontiers of Information Technology & Electronic Engineering, 21(11), 1554-1566.
  • Uyar, T., & Özel, M. E. (2020). Agent-based modelling of interstellar contacts using rumour spread models. International Journal of Astrobiology, 1-7.
  • van Doormaal, N., Ruiter, S., & Lemieux, A. M. (2020). Corruption and the shadow of the future. Agent-Based Modelling for Criminological Theory Testing and Development, 167.
  • van Doren, D. (2020). Enabling Innovation Within Public Research Institutes: A Modelling Approach. In Advances in Social Simulation (pp. 441-449). Springer, Cham.
  • van Tol, M. C. M., Moncada, J. A., Lukszo, Z., & Weijnen, M. (2020). Modelling the interaction between policies and international trade flows for liquid biofuels: an agent-based modelling approach. Energy Policy, 112021.
  • Van Voorn, G., Hengeveld, G., & Verhagen, J. (2020). An agent based model representation to assess resilience and efficiency of food supply chains. Plos one, 15(11), e0242323.
  • van Weerden, J. F., Verbrugge, R., & Hemelrijk, C. K. (2020). Modelling non-attentional visual information transmission in groups under predation. Ecological Modelling, 431, 109073.
  • Veldt, N., Benson, A. R., & Kleinberg, J. (2020, August). Minimizing Localized Ratio Cut Objectives in Hypergraphs. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1708-1718).
  • Veloso, P., & Krishnamurti, R. (2020, September). An academy of spatial agents: Generating spatial configurations with deep reinforcement learning. Cognizant Architecture - What if Buildings Could Think?, 2, 191-200.
  • Vendome, C., Rao, D. M., & Giabbanelli, P. J. (2020, May). How do modelers code artificial societies? investigating practices and quality of netlogo codes from large repositories. In Proceedings of the 2020 Spring Simulation Conference (pp. 1-12).
  • Verhagen, P. (2020). NetLogo palaeodemography scripts.
  • Vermeer, W., Hjorth, A., Jenness, S. M., Brown, C. H., & Wilensky, U. (2020). Leveraging modularity during replication of high-fidelity models: Lessons from replicating an agent-based model for HIV prevention. Journal of Artificial Societies and Social Simulation, 23(4), 7. doi.org/10.18564/jasss.4352
  • Vernon-Bido, D., & Collins, A. J. (2020). Finding Core Members of Cooperative Games using Agent-Based Modeling. arXiv preprint arXiv:2009.00519.
  • Vidal-Cordasco, M., & Nuevo-López, A. Resilience and vulnerability to climate change in the Greek Dark Ages. Journal of Anthropological Archaeology, 61, 101239.
  • Vieira, A. A., Dias, L. M., Santos, M. Y., Pereira, G. A., & Oliveira, J. A. (2020). Supply Chain Data Integration: A Literature Review. Journal of Industrial Information Integration, 100161.
  • Vigoda-Gadot, E., & Vashdi, D. R. (2020). Towards a new age of research methods in public administration, public management and public policy. Handbook of Research Methods in Public Administration, Management and Policy, 1.
  • Villanuevaa, S. K. D., & Buhata, C. A. H. Determining the Effectiveness of Practicing Non-Pharmaceutical Interventions in Improving Virus Control in a Pandemic using Agent-Based Modelling.
  • Viloria, A., Arias, Y. A. O., Balaguera, M. I., Lis-Gutiérrez, J. P., Angulo, M. G., & Lis-Gutierrez, M. (2020). Modeling and Simulating Human Occupation: A NetLogo-Agent-Based Toy Model. In Advances in Electrical and Computer Technologies (pp. 135-145). Springer, Singapore.
  • Vlug, J. H. (2020). Impact of Migration and Urbanization on Cities: an Agent-Based Model on the effects of Migration on the city of The Hague.
  • Vodovotz, Y., & An, G. (2020). Agent-Based Modeling of Wound Healing: Examples for Basic and Translational Research. In Complex Systems and Computational Biology Approaches to Acute Inflammation (pp. 223-243). Springer, Cham.
  • Voinov, A., Perez, P., Castilla-Rho, J. C., & Kenny, D. C. (2020). Integrated ecological economic modeling: what is it good for?. In Sustainable Wellbeing Futures. Edward Elgar Publishing.
  • von Briesen, E. M. (2020). Modeling Identity-Based Conflict and Genocide-An Approach Informed by Complexity Theory and Computational Social Science (Doctoral dissertation, The University of North Carolina at Charlotte).
  • Vu, T., Probst, C., Nielsen, A., Bai, H., Buckley, C., Meier, P., ... & Purshouse, R. (2020). A software architecture for mechanism-based social systems modelling in agent-based simulation models. Journal of Artificial Societies and Social Simulation.
  • Walker, N. D., Boyd, R., Watson, J., Kotz, M., Radford, Z., Readdy, L., ... & Hyder, K. (2020). A spatially explicit individual-based model to support management of commercial and recreational fisheries for European sea bass Dicentrarchus labrax. Ecological Modelling, 431, 109179.
  • Waltemath, D., Golebiewski, M., Blinov, M. L., Gleeson, P., Hermjakob, H., Hucka, M., ... & Malik-Sheriff, R. S. (2020). The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE). Journal of Integrative Bioinformatics, 1(ahead-of-print).
  • Wang, A., & Chan, E. H. (2020). The impact of power-geometry in participatory planning on urban greening. Urban Forestry & Urban Greening, 48, 126571.
  • Wang, A., Wang, H., & Chan, E. (2020). The incompatibility in urban green space provision: An agent-based comparative study. Journal of Cleaner Production, 120007.
  • Wang, M., Tsanas, A., Blin, G., & Robertson, D. (2020). Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model. Scientific Reports, 10(1), 1-13.
  • Wang, Q., & Mao, X. (2020). Dynamic Task Allocation Method of Swarm Robots Based on Optimal Mass Transport Theory. Symmetry, 12(10), 1682.
  • Wang, Y., Li, X., Zhang, F., Wang, W., & Xiao, R. (2020). Effects of rapid urbanization on ecological functional vulnerability of the land system in Wuhan, China: A flow and stock perspective. Journal of Cleaner Production, 248, 119284.
  • Wang, Z., & Jia, G. (2020). A novel agent-based model for tsunami evacuation simulation and risk assessment. Natural Hazards, 1-27.
  • Waziri, N. (2020). Education for All? Complex solutions to complex problems in the Nigerian education sector (Doctoral dissertation, University of Cambridge).
  • Webster, K. (2020, June). Negotiating an Inefficient Market: An Agent-Based Model Approach to Property Insurance Claim Negotiations. In Advances in Simulation and Digital Human Modeling: Proceedings of the AHFE 2020 Virtual Conferences on Human Factors and Simulation, and Digital Human Modeling and Applied Optimization, July 16-20, 2020, USA (Vol. 1206, p. 65). Springer Nature.
  • Wheatley, R., Pavlic, T. P., Levy, O., & Wilson, R. S. (2020). Habitat features and performance interact to determine the outcomes of terrestrial predator‐prey pursuits. Journal of Animal Ecology.
  • Widyastuti, K., Imron, M. A., Pradopo, S. T., Suryatmojo, H., Sopha, B. M., Spessa, A., & Berger, U. (2020). PeatFire: an agent-based model to simulate fire ignition and spreading in a tropical peatland ecosystem. International Journal of Wildland Fire.
  • Wijermans, N., Boonstra, W. J., Orach, K., Hentati‐Sundberg, J., & Schlüter, M. (2020). Behavioural diversity in fishing—Towards a next generation of fishery models. Fish and Fisheries.
  • Wilensky, U. J. (2020) New Developments in Restructuration Theory and Understanding Complex Systems Through Agent-Based Restructurations. AERA Annual Meeting San Francisco, CA http://tinyurl.com/rvb86xr (Conference Canceled)
  • Wilkerson, M. H. & Gravel, B. Storytelling as a Support for Collective Constructionist Activity. In Holbert, N., Berland, M., & Kafai, Y. B. (eds.), Designing Constructionist Futures: The Art, Theory, and Practice of Learning Designs, 213.
  • Williams, R. A. (2020). User Experiences using FLAME: A Case Study Modelling Conflict in Large Enterprise System Implementations. Simulation Modelling Practice and Theory, 102196.
  • Williams, T. G., Guikema, S. D., Brown, D. G., & Agrawal, A. (2020). Assessing model equifinality for robust policy analysis in complex socio-environmental systems. Environmental Modelling & Software, 104831.
  • Wilson, K. M., & Hill, M. G. (2020). Synthesis and assessment of the flat-headed peccary record in North America. Quaternary Science Reviews, 248, 106601.
  • Winterrose, M. L., Carter, K. M., Wagner, N., & Streilein, W. W. (2020). Adaptive attacker strategy development against moving target cyber defenses. In Advances in Cyber Security Analytics and Decision Systems (pp. 1-14). Springer, Cham.
  • Wolf, S., Burrows, A. C., Borowczak, M., Johnson, M., Cooley, R., & Mogenson, K. (2020). Integrated Outreach: Increasing Engagement in Computer Science and Cybersecurity. Education Sciences, 10(12), 353.
  • Wong, S. M., & Montalto, F. A. (2020). Exploring the Long‐Term Economic and Social Impact of Green Infrastructure in New York City. Water Resources Research, e2019WR027008.
  • Wozniak, M. (2020). Virtualising Space–New Directions for Applications of Agent-Based Modelling in Spatial Economics. Acta Universitatis Lodziensis. Folia Oeconomica, 1(346), 7-26.
  • Wu, B. (2020). Investor Behavior and Risk Contagion in an Information-Based Artificial Stock Market. IEEE Access, 8, 126725-126732.
  • Wu, S. P. W., Peel, A. M., Bain, C., Anton, G., Horn, M. S. & Wilensky, U. (2020). Workshops and co-design can help teachers integrate computational thinking into their K-12 STEM classes. Proceedings of CTE2020. Hong Kong, China.
  • Wu, X., Lin, Y., & Zhao, L. (2020). Simulation Modeling of Tourists’ Travel Behaviors at the Intra-City Scale. In CICTP 2020 (pp. 4599-4608).
  • Xia, H., Li, L., Cheng, X., Liu, C., & Qiu, T. (2020). A dynamic virus propagation model based on social attributes in city IoTs. IEEE Internet of Things Journal.
  • Xiang, L., Shen, G., & Tan, Y. (2020, December). A Multi-agent Platform to Inform Strategies for Briefing Age-Friendly Communities in Urban China. In International Conference on Resource Sustainability-Sustainable Urbanisation in the BRI Era (pp. 181-193). Springer, Singapore.
  • Yang, Q., Sun, Y., Liu, X., & Wang, J. (2020). MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China. Sustainability, 12(2), 546.
  • Yang, Q., Wang, J., Liu, X., & Xia, J. (2020). MAS-Based Interaction Simulation within Asymmetric Information on Emergency Management of Urban Rainstorm Disaster. Complexity, 2020.
  • Yao, X., Sun, H., & Fan, B. (2020). A novel simulation framework for crowd co-decisions. International Journal of Crowd Science.
  • Yasrebi-Soppa, P., Bartels, J. J., Viefhaus, S., Reuss, P., & Althoff, K. D. (2020). Visualizing the behavior of CBR agents in an FPS Scenario.
  • Ye, T., Ning, Z., Zhang, J., & Xu, M. (2020). Trusted measurement of behaviors for the Internet of Things. Alexandria Engineering Journal.
  • Yıldız, B., & Çağdaş, G. (2020). Fuzzy logic in agent-based modeling of user movement in urban space: Definition and application to a case study of a square. Building and Environment, 169, 106597.
  • Youl, E., Malo, S., & Ouaro, S. (2020, November). An Agent-Based Study of the Impact of Sensitization on the Spread of Covid 19 in Burkina Faso. In Proceedings of the Future Technologies Conference (pp. 64-77). Springer, Cham.
  • Young, E., & Aguirre, B. (2020). PrioritEvac: an Agent-Based Model (ABM) for Examining Social Factors of Building Fire Evacuation. Information Systems Frontiers, 1-14.
  • Yousefi, M., Yousefi, M., & Fogliatto, F. S. (2020). Simulation-based optimization methods applied in hospital emergency departments: A systematic review. Simulation, 0037549720944483.
  • Yue, T., Long, R., Chen, H., Liu, J., Liu, H., & Gu, Y. (2020). Energy-saving behavior of urban residents in China: A multi-agent simulation. Journal of Cleaner Production, 252, 119623.
  • Yust, A. E., & Smyth, D. S. (2020). Simulating Bacterial Growth, Competition, and Resistance with Agent-Based Models and Laboratory Experiments. In An Introduction to Undergraduate Research in Computational and Mathematical Biology (pp. 217-271). Birkhäuser, Cham.
  • Zahmani, M. H., & Atmani, B. (2020). Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation. Journal of Scheduling, 1-22.
  • Zakharova, L., Meyer, K. M., & Seifan, M. (2020). Combining trait-and individual-based modelling to understand desert plant community dynamics. Ecological Modelling, 434, 109260.
  • Zamora-Maldonado, H. C., Avila-Foucat, V. S., Sánchez-Sotomayor, V. G., & Lee, R. Social-ecological Resilience Modeling: Water Stress Effects in the Bighorn Sheep Management System in Baja California Sur, Mexico. Ecological Complexity, 45, 100884.
  • Zapata-Roldan, F., & Sheikh, N. J. (2020). A Design Management Agent-Based Model for New Product Development. IEEE Transactions on Engineering Management.
  • Zarrabi, A. H., Azarbayjani, M., & Tavakoli, M. (2020) Generative Design Tool: Integrated Approach toward Development of Piezoelectric Façade System.
  • Zhang, B. H., & Ahmed, S. A. (2020). Systems Thinking—Ludwig Von Bertalanffy, Peter Senge, and Donella Meadows. In Science Education in Theory and Practice (pp. 419-436). Springer, Cham.
  • Zhang, D. (2020). Teaching Geometry to Students With Learning Disabilities: Introduction to the Special Series. Learning Disability Quarterly, 0731948720959769.
  • Zhang, G., Li, H., & Yan, S. (2020). The Vital Few: Exploring the Role of Expertise in the Process of Team Creativity. The Journal of Creative Behavior.
  • Zhang, H., & Zhang, B. (2020). The unintended impact of carbon trading of China's power sector. Energy Policy, 147, 111876.
  • Zhang, M., Chen, H., Li, X., & Luo, A. (2020). Describing coevolution of business and IS alignment via agent-based modeling.
  • Zhang, M., Chen, H., & Lyytinen, K. (2020). Validating the coevolutionary principles of business and IS alignment via agent-based modeling. European Journal of Information Systems, 1-16.
  • Zhang, R., & Chan, W. K. V. (2020, July). Evaluation of Energy Consumption in Block-Chains with Proof of Work and Proof of Stake. In Journal of Physics: Conference Series (Vol. 1584, No. 1, p. 012023). IOP Publishing.
  • Zhang, R., & Tielbörger, K. (2020). Density-dependence tips the change of plant–plant interactions under environmental stress. Nature Communications, 11(1), 1-9.
  • Zhang, X., Xu, L., & Gao, M. (2020, September). An Efficient Influence Maximization Algorithm Based on Social Relationship Priority in Mobile Social Networks. In International Symposium on Security and Privacy in Social Networks and Big Data (pp. 164-177). Springer, Singapore.
  • Zhang, Y., Gao, J., Cole, S., & Ricci, P. (2020). How the Spread of User-Generated Contents (UGC) Shapes International Tourism Distribution: Using Agent-Based Modeling to Inform Strategic UGC Marketing. Journal of Travel Research, 0047287520951639.
  • Zhao, J., Bai, A., Xi, X., Huang, Y., & Wang, S. (2020). Impacts of malicious attacks on robustness of knowledge networks: a multi-agent-based simulation. Journal of Knowledge Management.
  • Zhao, X., Rivera-Monroy, V. H., Wang, H., Xue, Z. G., Tsai, C. F., Willson, C. S., ... & Twilley, R. R. (2020). Modeling soil porewater salinity in mangrove forests (Everglades, Florida, USA) impacted by hydrological restoration and a warming climate. Ecological Modelling, 436, 109292.
  • Zheng, J., Ma, G., Wei, J., Wei, W., He, Y., Jiao, Y., & Han, X. (2020). Evolutionary process of household waste separation behavior based on social networks. Resources, Conservation and Recycling, 161, 105009.
  • Zheng, Y. (2020, June). The Theme Cooperation Mechanism of Science and Technology Enterprise Incubation Alliance Based on Multi-agent System under Computer Control. In Journal of Physics: Conference Series (Vol. 1574, No. 1, p. 012071). IOP Publishing.
  • Zhou, H., Shen, S., & Liu, J. (2020). Malware propagation model in wireless sensor networks under attack–defense confrontation. Computer Communications.
  • Zhuo, L., & Han, D. (2020). Agent-based modelling and flood risk management: a compendious literature review. Journal of Hydrology, 125600.
  • Ziv, G., Beckmann, M., Bullock, J., Cord, A., Delzeit, R., Domingo, C., ... & Neteler, M. (2020). BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes, 6, e52052.
  • Zoričak, M., Horváth, D., Gazda, V., & Hudec, O. (2020). Spatial evolution of industries modelled by cellular automata. Journal of Business Research.
  • Zou, J., Wang, K., & Sun, H. (2020). An implementation architecture for crowd network simulations. International Journal of Crowd Science.
  • Zukri, N. H. A., Rashid, N. A. M., Awang, N., & Zulkifli, Z. A. (2020). Agent-Based Encryption for Password Management Application. In Charting the Sustainable Future of ASEAN in Science and Technology (pp. 529-541). Springer, Singapore.
  • Zvereva, O. M. (2020). Investigation of Money Turnover in the Computer Agent-Based Model. In Advances in Information Technologies, Telecommunication, and Radioelectronics (pp. 95-105). Springer, Cham.

2019

  • Abdulkareem, S. A., Mustafa, Y. T., Augustijn, E. W., & Filatova, T. (2019). Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models. Geoinformatica, 23(2), 243-268.
  • Abdullah, L. (2019). Model Interaksi Pelaku Hutan Rakyat dalam Perdagangan Kayu: Pendekatan Simulasi Model Berbasis Agen (The Interaction Model of Community Forest Behavior in Wood Trade: Agent Based Modelling Approach). Jurnal Penelitian Hutan Tanaman, 16(1), 21-34.
  • Abrahamson, D. (2019). A new world: Educational research on the sensorimotor roots of mathematical reasoning. In A. Shvarts (Ed.), Proceedings of the annual meeting of the Russian chapter of the International Group for the Psychology of Mathematics Education (PME) & Yandex (pp. 48–68). Moscow: Yandex
  • Abrahamson, D., Flood, V. J., Miele, J. A., & Siu, Y.-T. (2019). Enactivism and ethnomethodological conversation analysis as tools for expanding Universal Design for Learning: The case of visually impaired mathematics students. ZDM Mathematics Education, 51(2), 291-303. doi:10.1007/s11858-018-0998-1
  • Abrahamson, D., & Shulman, A. (2019). Co-constructing movement in mathematics and dance: An interdisciplinary pedagogical dialogue on subjectivity and awareness. Feldenkrais Research Journal, 6, 1-24. Retrieved from
  • Adeel, M., Khalid, M., Asif, M., & Faisal, M. N. (2019). Simulation Models for Comparison of Toxicities of Anticancer Drugs. Annals of Punjab Medical College (APMC), 13(3), 216-222.
  • Aghaie, A., & Hajian Heidary, M. (2019). Simulation-based optimization of a stochastic supply chain considering supplier disruption: Agent-based modeling and reinforcement learning. Scientia Iranica, 26(6), 3780-3795.
  • Ahmed, S. H., Bashir, A. K., & Guibene, W. (2019). Introduction to the special section on emerging technologies for connected vehicles and ITS networks. Computers & Electrical Engineering, 75, 309-311.
  • Aji, W. S. (2019). Simulation with Multi Agent Flood Prediction Based on Rain Intensity Using Particle Swarm Otimization. Jurnal Teknologi Informasi, 5(2), 93-98.
  • Al-Najjar, A. A. M., & Chasib, H. S. (2019). Design and implementation weights equation for optimization DSR protocol in MANETs environment. Int. J. Adv. Sci. Technol, 28(8), 457-470.
  • Alves, F., Varela, M. L. R., Rocha, A. M. A., Pereira, A. I., & Leitão, P. (2019). A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment.
  • Amelia, P., & Lathifah, A. (2019). Dynamics analysis of container needs and availability in surabaya container terminal with agent-based modeling and simulation. Procedia Computer Science, 161, 910-918.
  • Anderson, Sven, and Siv Disa Anderson. "Coding and Music Creation in a Multi-Agent Environment." Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. 2020.
  • Antelmi, A., Cordasco, G., D’Auria, M., De Vinco, D., Negro, A., & Spagnuolo, C. (2019, October). On Evaluating Rust as a Programming Language for the Future of Massive Agent-Based Simulations. In Asian Simulation Conference (pp. 15-28). Springer, Singapore.
  • Anton, G. & Wilensky, U. (2019). One size fits all: Designing for socialization in physical computing. In Proceedings of the 50th ACM technical symposium on computer science education (pp. 825 - 831). ACM
  • Arastoopour Irgens, G., Chandra, S., Dabholkar, S., Horn, M., & Wilensky, U. (2019). Classifying Emergent Student Learning in a High School Computational Chemistry Unit. Paper presented at the American Education Research Association (AERA) Conference. Toronto, CA
  • Arastoopour Irgens, G., Dabholkar, S., Bain, C., Woods, P., Hall, K., Swanson, H., Horn, M., & Wilensky, U. (2019). Modeling and Measuring Students' Computational Thinking Practices in Science. Journal of Science Education and Technology.
  • Ashley, D. R., Chockalingam, V., Kuzma, B., & Bulitko, V. (2019, July). Learning to select mates in artificial life. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 103-104).
  • Ashraf, R., Zafar, B., Jabbar, S., Ahmad, M., & Ahmed, S. H. (2019). Modeling and Simulation of Resource-Constrained Vaccination Strategies and Epidemic Outbreaks. In Applications of Intelligent Technologies in Healthcare (pp. 131-141). Springer, Cham.
  • Aslan, U., Anton, G., & Wilensky, U. (2019). Bringing Powerful Ideas to Middle School Students' Lives Through Agent-Based Modeling.a Paper presented at the Annual Meeting of the American Educational Research Association (AERA) 2019. Toronto, CA
  • Azarov, I., Peskov, K., Helmlinger, G., & Kosinsky, Y. (2019). Role of T cell-to-dendritic cell chemoattraction in T cell priming initiation in the lymph node: An agent-based modeling study. Frontiers in immunology, 10, 1289.
  • Bain, C. & Wilensky U. (2019). Sorting Out Algorithms: Learning about Complexity through Participatory Simulations. In E. K. Hawthorne, M. A. Pérez-Quiñones, S. Heckman, & J. Zhang (Eds.). Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE '19), February 27-March 2, 2019, Minneapolis, MN, USA.
  • Bain, C., & Anton, G. (2019, February). Integrating Agent-based Modeling in STEM Classes: From Blocks to Text and Back?. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 1238-1238).
  • Bain, C., Anton, G., Horn, M., & Wilensky, U. (2019, October). Position: Building Blocks for Agent-based Modeling Can Scaffold Computational Thinking Engagement in STEM Classrooms. In 2019 IEEE Blocks and Beyond Workshop (B&B) (pp. 1-4).
  • Basu, D. (2019). Examining Students’ Covariational Reasoning Through Mathematical Modeling Activities Embedded in the Context of the Greenhouse Effect.
  • Basu, D., & Panorkou, N. (2019). Integrating Covariational Reasoning and Technology into the Teaching and Learning of the Greenhouse Effect. Journal of Mathematics Education, 12(1), 6-23.
  • Barbuto, A., Lopolito, A., Santeramo, F.G. (2019) Improving diffusion in agriculture: an agent-based model to find the predictors for efficient early adopters Agricultural and Food Economics. [PDF]
  • Bauduin, S., McIntire, E. J., & Chubaty, A. M. (2019). NetLogoR: a package to build and run spatially explicit agent‐based models in R. Ecography, 42(11), 1841-1849.
  • Bayo, M. (2019). Agend-Based-Modelling. Pond eutrophication in agroecosystems and the influence of combinations of pesticides and fertilizers on aquatic productivity. GRIN Verlag.
  • Benhadi-Marín, J., Pereira, J. A., Sousa, J. P., & Santos, S. A. (2019). EcoPred: an educational individual based model to explain biological control, a case study within an arable land. Journal of Biological Education, 1-16.
  • Bipasha, T., Azucena, J., Alkhaleel, B., Liao, H., & Nachtmann, H. (2019, December). Hybrid simulation to support interdependence modeling of a multimodal transportation network. In 2019 Winter Simulation Conference (WSC) (pp. 1390-1401). IEEE.
  • Bithell, M. Creating a Model of the Earth System (MOTES): Some Experiences with Parallel ABM. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 459). Springer Nature.
  • Blikstein, P., & Moghadam, S. H. (2019). 3 Computing Education. The Cambridge handbook of computing education research, 56.
  • Bo, Y. (2019). The data clustering based dynamic risk identification of biological immune system: mechanism, method and simulation. Cluster Computing, 22(3), 6253-6266.
  • Borong, N., & Galdo, M. (2019). NET LINGO Initialism: An Agent-Based Model on Language. Journal of Educational and Human Resource Development, 7, 150-155.
  • Borowczak, M., & Burrows, A. C. (2019). Ants Go Marching—Integrating Computer Science into Teacher Professional Development with NetLogo. Education Sciences, 9(1), 66.
  • Bortz, W. W., Gautam, A., Lipscomb, K., & Tatar, D. (2019). Integration Computational Thinking into Middle School Science: A search for Synergistic Pedagogy. In ASEE Southeastern Section Conference.
  • Boukehila, A., & Taleb, N. (2019, November). Case-Based Approach to Detect Emergence. In Proceedings of the 2019 3rd International Conference on Big Data Research (pp. 98-102).
  • Brennan, R. W., Hermanson, G., Nelson, N., Paul, R., & Sullivan, M. (2019). Using agent-based modelling for preliminary EER experimental design. Proceedings of the Canadian Engineering Education Association (CEEA).
  • Browning, F., Moore K., Campos, J. (2019) Exploring Negative Absolute Temperature Using NetLogo. The Physics Journal, 57(26), 26-27. [PDF]
  • Bulitko, V., Doucet, K., Evans, D., Docking, H., Walters, M., Oliver, M., ... & Kendal-Freedman, N. (2019, July). A-life Evolution with Human Proxies. In Artificial Life Conference Proceedings (pp. 465-466). One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press.
  • Burbach, L., Belavadi, P., Halbach, P., Plettenberg, N., Nakayama, J., Ziefle, M., & Valdez, A. C. Towards An Understanding of Opinion Formation on the Internet. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 133). Springer Nature.
  • Buss, A., Shepherd, C. E., & Smith, S. M. (2019). Learning from Failure: Growing Roses of Success.
  • Butler, G., Rudge, J., & Dash, P. R. (2019). Mathematical modelling of cell migration. Essays in biochemistry, 63(5), 631-637.
  • Calabrò, G., Torrisi, V., Inturri, G., & Ignaccolo, M. (2020). Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization. European Transport Research Review, 12(1), 1-11.
  • Cardinot, M., O’Riordan, C., Griffith, J., & Perc, M. (2019). Evoplex: A platform for agent-based modeling on networks. SoftwareX, 9, 199-204.
  • Cascalho, J., Trigo, P., Cruz, M. J., Mendes, A., Giacomello, E., Ressurreiçao, A., ... & Morato, T. (2019). SIMSEA: A Multiagent Architecture for Fishing Activity in a Simulated Environment.
  • Castañeda-Martínez R.A., Flores DL., Castro C., Benítez B.(2019). Agent-Based Model of Resistant Bacterial Evolution in an Heterogeneous Medium. In: Sanchez M., Aguilar L., Castañón-Puga M., Rodríguez A. (eds) Applied Decision-Making. Studies in Systems, Decision and Control, vol 209. Springer, Cham
  • Castro, C., Flores, D. L., Cervantes-Vásquez, D., Vargas-Viveros, E., Gutiérrez-López, E., & Muñoz-Muñoz, F. (2019). An agent-based model of the fission yeast cell cycle. Current genetics, 65(1), 193-200.
  • Castro, C., Flores, D. L., Vargas, E., Cervantes, D., & Delgado, E. (2019). Agent-Based Model of the Budding Yeast Cell Cycle Regulatory Network. In World Congress on Medical Physics and Biomedical Engineering 2018 (pp. 531-534). Springer, Singapore.
  • Ceja, A. Y., & Kane, S. (2019, August). An Astroecological Model for Characterizing Exoplanet Habitability. In AAS/Division for Extreme Solar Systems Abstracts (Vol. 4).
  • Chao, D., Hashimoto, H., & Kondo, N. (2019). Social influence of e-cigarette smoking prevalence on smoking behaviours among high-school teenagers: Microsimulation experiments. PloS one, 14(8), e0221557.
  • Chappin, E., Bouwmans, I., & Deijkers, E. EMLab-Consumer—Simulating Energy Efficiency Adoption Decisions of European Households. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 485). Springer Nature.
  • Chaudhari, K. S. (2019).Agent-based modelling of electric vehicle charging for optimized charging station operation. Doctoral thesis, Nanyang Technological University, Singapore
  • Chen, P., Wu, X., & Miao, D. (2019, June). Agent-Based Modeling in a Simple Circular Economy. In International Conference on Applications and Techniques in Cyber Security and Intelligence (pp. 487-497). Springer, Cham.
  • Chen, Z. (2019). An agent-based model for information diffusion over online social networks. Papers in Applied Geography, 5(1-2), 77-97.
  • Chen, Z., Spana, S., Yin, Y., & Du, Y. (2019). An advanced parking navigation system for downtown parking. Networks and Spatial Economics, 19(3), 953-968.
  • Chennoufi, M., & Bendella, F. (2019, April). Decision Making in Complex System. In 2019 5th International Conference on Optimization and Applications (ICOA) (pp. 1-7). IEEE.
  • Chiew, L. S., & Amerudin, S. (2019, June). ANALYSIS OF BURGLARY CRIME PATTERNS THROUGH THE INTEGRATION OF SPATIAL STATISTICS AND AGENT-BASED MODELLING.
  • Chliaoutakis, A., & Chalkiadakis, G. (2019, June). AncientS-ABM: A Novel Tool for Simulating Ancient Societies. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 237-241). Springer, Cham.
  • Chu, H., Yu, J., Wen, J., Yi, M., & Chen, Y. (2019). Emergency evacuation simulation and management optimization in urban residential communities. Sustainability, 11(3), 795.
  • Chumachenko, D., Meniailov, I., Bazilevych, K., & Chumachenko, T. (2019, September). On Intelligent Decision Making in Multiagent Systems in Conditions of Uncertainty. In 2019 XIth International Scientific and Practical Conference on Electronics and Information Technologies (ELIT) (pp. 150-153). IEEE.
  • Chumachenko, D., & Yakovlev, S. (2019, February). On intelligent agent-based simulation of network worms propagation. In 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM) (pp. 11-14). IEEE.
  • Cimino, M. G., Lega, M., Monaco, M., & Vaglini, G. (2019, February). Adaptive Exploration of a UAVs Swarm for Distributed Targets Detection and Tracking. In ICPRAM (pp. 837-844).
  • Cockrell, C., Teague, J., & Axelrod, D. E. (2020). Prevention of Colon Cancer Recurrence From Minimal Residual Disease: Computer Optimized Dose Schedules of Intermittent Apoptotic Adjuvant Therapy. JCO Clinical Cancer Informatics, 4, 514-520.
  • Coronel, A. R., & Alatriste, F. R. (2019, March). Turning caregivers into informed agents as a strategy to disseminate scientific information about cancer. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 938-944). Springer, Cham.
  • Cortier, O., Boutouil, M., & Maquaire, O. (2019). Quantifying Benefits of Permeable Pavement on Surface Runoff, An Agent-Based-Model with NetLogo. In New Trends in Urban Drainage Modelling: UDM 2018 11 (pp. 729-733). Springer International Publishing.
  • Cotfas, L. A., Delcea, C., Milne, R. J., Salari, M., Crăciun, L., & Molănescu, A. G. (2019). Testing new methods for boarding a partially occupied airplane using apron buses. Symmetry, 11(8), 1044.
  • Crabtree, S., Harris, K., Davies, B., & Romanowska, I. (2019, July 6). Outreach in Archaeology with Agent-Based Modeling: Part 3 of 3. https://doi.org/10.1017/aap.2019.4
  • Cruz, A., Carneiro, E., Fontes, X., Kokkinogenis, Z., & Rossetti, R. J. (2019, October). Hermes: a tool for mesoscopic simulation of advanced traveller information systems. In 2019 IEEE International Smart Cities Conference (ISC2) (pp. 638-643). IEEE.
  • Cruz, E. G. A. (2019). Modeling Social Learning: An Agent-Based Approach (Doctoral dissertation, Old Dominion University).
  • Da Costa, L., & Rajotte, J. F. (2019, May). Crowd Prediction Under Uncertainty. In Canadian Conference on Artificial Intelligence (pp. 308-319). Springer, Cham.
  • Dabholkar, S. & Wilensky, U. (2019). Designing ESM-mediated collaborative activity systems for science learning. Poster to be presented at International Conference of Computer Supported Collaborative Learning 2019, Lyon, France.
  • Dabholkar, S., Wilensky, U., & Horn, M. (2019) Supporting a teacher’s integration of Computational Thinking (CT) in a biology class by co-designing an ESM- (Emergent Systems Microworlds) based curricular unit, Poster presented at Inaugural symposium on Computer Science and Learning Science, Evanston, USA
  • Dabholkar, S. (2019) Designing Emergent Systems Microworlds to learn computational thinking in the context of synthetic biology. Poster presented at Learn.Design.Compute with Bio 2019, Philadelphia, USA
  • Dabholkar, S., Swanson, H., & Wilensky, U. (2019). Epistemic considerations for modeling: Understanding the usefulness and limitations of models with Emergent Systems Microworlds. In a Related Paper Set, Using Technology to Promote Students’ Modeling Practice and Complex Systems Thinking. The Annual Meeting of the National Association of Research in Science Teaching (NARST), Baltimore, MD, USA.
  • Dalle Nogare, D., & Chitnis, A. B. (2019, December). NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium. In Seminars in Cell & Developmental Biology. Academic Press.
  • Davies, B., Romanowska, I., Harris, K., & Crabtree, S. A. (2019). Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3. Advances in Archaeological Practice, 7(2), 185-193.
  • Davis, P., O'Mahony, A. & Pfautz, J. (2019).Social-Behavioral Modeling for Complex Systems. John Wiley & Sons.
  • Davydenko, I. Y., & Fransen, R. W. (2019). Conceptual agent based model simulation for the Port Nautical Services. IFAC-PapersOnLine, 52(3), 19-24.
  • DeMarco, K., Squires, E., Day, M., & Pippin, C. (2019). Simulating collaborative robots in a massive multi-agent game environment (scrimmage). In Distributed Autonomous Robotic Systems (pp. 283-297). Springer, Cham.
  • Delcea, C., & Cotfas, L. A. (2019). Increasing awareness in classroom evacuation situations using agent-based modeling. Physica A: Statistical Mechanics and its Applications, 523, 1400-1418.
  • Delcea, C., Milne, R. J., Cotfas, L. A., Crăciun, L., & Molănescu, A. G. (2019). Methods for Accelerating the Airplane Boarding Process in the Presence of Apron Buses. IEEE Access, 7, 134372-134387.
  • DeLuca, C. (2019). Keyword Response: Out of Step. In Key Concepts in Curriculum Studies (pp. 47-49). Routledge.
  • Dhou, K. (2019). An innovative design of a hybrid chain coding algorithm for bi-level image compression using an agent-based modeling approach. Applied Soft Computing, 79, 94-110.
  • Dhou, K., & Cruzen, C. (2019). An innovative chain coding technique for compression based on the concept of biological reproduction: an agent-based modeling approach. IEEE Internet of Things Journal, 6(6), 9308-9315.
  • Dickes, A.C., Kamarainen, A., Metcalf, S.J., Gün‐Yildiz, S., Brennan, K., Grotzer, T., & Dede, C. (2019). Scaffolding ecosystems science practice by blending immersive environments and computational modeling. British Journal of Educational Technology, 50(5), 2181-2202. https://doi.org/10.1111/bjet.12806
  • Ding, F., & Pan, W. (2019). Simulation Research on Large Passenger Flow Guidance of Urban Rail Transit Based on Multi-Agent. Academic Journal of Computing & Information Science, 2(1).
  • Ding, Feng, and Wenjie Pan. "Simulation Research on Large Passenger Flow Guidance of Urban Rail Transit Based on Multi-Agent." Academic Journal of Computing & Information Science 2.1 (2019).
  • Dobaria, R., & Chilka, A. (2019). IoT Smart Waste Monitoring and Collection Framework. International Journal of Distributed Computing and Technology, 5(1), 27-32.
  • Dragoni, A. F. (2019). An Agent-Swarm Simulator for Dynamic Vehicle Routing Problem Empirical Analysis. In Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection: 17th International Conference, PAAMS 2019, Ávila, Spain, June 26-28, 2019, Proceedings (Vol. 11523, p. 246). Springer.
  • Dragotă, V., & Delcea, C. (2019). How long does it last to systematically make bad decisions? An agent-based application for dividend policy. Journal of Risk and Financial Management, 12(4), 167.
  • D’Souza, M., & Kashi, R. N. (2019, January). Avionics Self-adaptive Software: Towards Formal Verification and Validation. In International Conference on Distributed Computing and Internet Technology (pp. 3-23). Springer, Cham.
  • Egbert, M. (2019, July). Real-Time Visualization and Interaction with Computational Artefacts. In MethAL workshop with The Conference on Artificial Life 2019.
  • Elfakir, A., Tkiouat, M., & Allam, K. (2019). Entrepreneurial financing under uncertainty: Performance comparison between ROMCA and conventional microloans using agent based simulation.
  • Elfakir, A., & Tkiouat, M. (2019). Profit and loss Sharing Negotiations involving a VC and an entrepreneur: A Game Theoretic Approach with Agent Based Simulation.
  • El-dosuky, M. (2019). Taming the Sharing Economy Flood: Modelling the Imposing of Sharing Economy Regulation. Available at SSRN 3372792.
  • Esmaeili Bidhendi, M. (2019). The Study of CO Symptoms' Impacts on Individuals, Using GIS and Agent-based Modeling (ABM). Pollution, 5(3), 463-471.
  • Falcionelli, N., et al. (2019)."An Agent-Swarm Simulator for Dynamic Vehicle Routing Problem Empirical Analysis." Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection Lecture Notes in Computer Science, 26 June 2019, pp. 246–250., doi:10.1007/978-3-030-24209-1_23.
  • Falletta, J., & Mukheibir, P. (2019). Demand Forecasting: Review of processes and methodologies [prepared for the Water Corporation].a
  • Farris, A. V. (2019, June). The Sensing Bridge Between Perceptuomotor Experience and Scientific Investigation. In Proceedings of the 18th ACM International Conference on Interaction Design and Children (pp. 648-651).
  • Febriandini, I. F., Sutopo, W., & Hisjam, M. (2019, May). Analysis daily newspaper distribution in Solo by Agent Based Simulation. In IOP Conference Series: Materials Science and Engineering (Vol. 528, No. 1, p. 012033). IOP Publishing.
  • França da Silva, T., Alves Leite, J. L., Campos Ferro Junior, R. J., Ferreira da Costa, L., Pinheiro de Souza, R., Bernardino Andrade, J. P., & Lima de Campos, G. A. (2019, May). Smart targets to avoid observation in cto problem. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1958-1960).
  • Frahm, E., Kandel, A.W. & Gasparyan, B. (2019). Upper Palaeolithic Settlement and Mobility in the Armenian Highlands: Agent-Based Modeling, Obsidian Sourcing, and Lithic Analysis at Aghitu-3 Cave. Journal of Paleolithic Archaeology 2, 418–465. https://doi.org/10.1007/s41982-019-00025-5
  • Freelan, D., Spagnuolo, C., Scarano, V., Cordasco, G., & Cioffi-Revilla, C. (2019, June). The MASON Simulation Toolkit: Past, Present, and Future. In Multi-Agent-Based Simulation XIX: 19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers (Vol. 11463, p. 75). Springer.
  • Fuchs, M., & Neumayr, R. (2019, September). Agent-Based Semiology for Simulation and Prediction of Contemporary Spatial Occupation Patterns. In Design Modelling Symposium Berlin (pp. 648-661). Springer, Cham.
  • Fulop, S. A., & Scott, H. (2019). Vowel System Sandbox: Complex System Modelling of Language Change. Journal of Open Research Software, 7(1).
  • Gama, C. A. F., & Vivacqua, A. S. (2019, October). The cooperative dynamics of Brazilian Oil and Gas Innovations Systems a Research Proposal. In Anais do XV Simpósio Brasileiro de Sistemas Colaborativos (pp. 24-29). SBC.
  • Gao, S., Song, X., & Ding, R. (2019). Dynamic Agent-Based Simulation of Information Transfer in Collaborative Project Network. In Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation (pp. 602-610). Reston, VA: American Society of Civil Engineers.
  • García-Peña, C., Gutiérrez-Robledo, L. M., Cabrera-Becerril, A., & Fajardo-Ortiz, D. (2019). Team Assembly Mechanisms and the Knowledge Produced in the Mexico’s National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach. Scientifica, 2019.
  • Garzón, M., & Rojas-Galeano, S. (2019, November). An Agent-Based Model of Urban Pigeon Swarm Optimisation. In 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-6). IEEE.
  • Georgescu, A., Gheorghe, A. V., Piso, M. I., & Katina, P. F. (2019). Governance by Emerging Technologies—The Case for Sand and Blockchain Technology. In Critical Space Infrastructures (pp. 237-247). Springer, Cham.
  • Giabbanelli, P., Fattoruso, M., & Norman, M. L. (2019, May). Cofluences: simulating the spread of social influences via a hybrid agent-based/fuzzy cognitive maps architecture. In Proceedings of the 2019 ACM SIGSIM conference on principles of advanced discrete simulation (pp. 71-82).
  • Gibson, J. B., Page, J., & Mukhlish, F. (2019, September). Simulation of an Unmanned Aerial Vehicle Search and Rescue Swarm for Observation of Emergent Behaviour. In Australasian Simulation Congress (pp. 95-105). Springer, Singapore.
  • Giner Sanz, J. J., García Gabaldón, M., Ortega Navarro, E. M., Shao Horn, Y., & Pérez Herranz, V. (2019, September). A NetLogo® model for introducing students to genetic algorithms. In IN-RED 2019. V Congreso de Innovación Educativa y Docencia en Red (pp. 88-101). Editorial Universitat Politècnica de València.
  • Ginovart Gisbert, M., & Font Marques, M. (2019). Flaix de ciència. Massa petits per poder ser modelitzats? Models basats en l’individu per representar i investigar poblacions microbianes amb creixement no planctònic. Treballs de la Societat Catalana de Biologia, 68, 50-53.
  • Ginovart Gisbert, M., & Font Marques, M. (2019). Flaix de ciència. Massa petits per poder ser modelitzats? Models basats en l’individu per representar i investigar poblacions microbianes amb creixement no planctònic. Treballs de la Societat Catalana de Biologia, 68, 50-53.
  • Golovnev, G. (2019, May). Modeling of Multi-agent Voltage Control in Distribution Electric Networks of Railways. In International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies EMMFT 2018: Volume 1 (Vol. 982, p. 300). Springer.
  • Gooding, T. (2019). Agent-based model history and development. In Economics for a Fairer Society (pp. 25-36). Palgrave Pivot, Cham.
  • Gooding, T. (2019). Evolutionary Price Robustness. In Economics for a Fairer Society (pp. 105-114). Palgrave Pivot, Cham.
  • Gooding, T., & Gooding, T. (2019). Evolution in Netlogo. Economics for a Fairer Society: Going Back to Basics using Agent-Based Models, 45-51.
  • Graham, S., Gupta, N., Smith, J., Angourakis, A., Reinhard, A., Ellenberger, K., ... & Nobles, G. (2019). The Open Digital Archaeology Textbook.
  • Greasley, A. (2019). Case Study: Agent-Based Modeling in Discrete-Event Simulation. In Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics (pp. 325-335). De Gruyter.
  • Greco, A., Cannizzaro, F., & Pluchino, A. (2019). Automatic evaluation of plastic collapse conditions for planar frames with vertical irregularities. Engineering with Computers, 35(1), 57-73.
  • Gružauskas, V., Gimžauskienė, E., & Navickas, V. (2019). Forecasting accuracy influence on logistics clusters activities: The case of the food industry. Journal of Cleaner Production, 240, 118225.
  • Gullichsen, F. (2019). Simulation of social media networks with agent-based modeling: The research and evaluation of using Netlogo, an agent-based modeling software.
  • Guo, Y. & Wilensky, U. (2019). Changing High School Students’ Perceptions of Wealth Inequality in the U.S. through Agent-based Participatory Simulations. Poster presented at the annual meeting of the American Educational Research Association (Special Interest Group: Learning Sciences), Toronto, Canada, April 5-9./li>
  • Gurkan, C., Rasmussen, L. & Wilensky, U. (2019). Effects of Visual Sensory Range on the Emergence of Cognition in Early Terrestrial Vertebrates: An Agent-Based Modeling Approach. The 2019 Conference on Artificial Life, Newcastle upon Tyne, UK. No. 31, 475-476.
  • Haire, M., Xu, X., Alboul, L., Penders, J., & Zhang, H. (2019, September). Ship hull repair using a swarm of autonomous underwater robots: A self-assembly algorithm. In 2019 European Conference on Mobile Robots (ECMR) (pp. 1-6). IEEE.
  • Hameed, B., Othman, W. A. F. W., Noor, N. M., Bakar, E. A., & Hawary, A. F. (2019). DECENTRALIZED PATH FORMATION TECHNIQUE FOR SWARM ROBOTS USING BATMAN APPROACH. ROBOTIKA, 1(1), 9-15.
  • Hammouda, M., Kaya, C. S., & Yücesoy, C. A. (2019, October). Development of an Agent-Based Model to Study the Mechanism of Effects of Botulinum Toxin on Muscle Tissue Adaptation. In 2019 Medical Technologies Congress (TIPTEKNO) (pp. 1-3). IEEE.
  • Hannum, C. (2019). Market concentration in real estate brokerage in economic downturns: evidence from spatial agent-based models. Applied Economics Letters, 26(19), 1567-1571.
  • Harmsma, W H. (2019). "The Effects of Stocking Configurations in Industrial Symbiotic Networks : an Agent-Based Simulation Study.” The Effects of Stocking Configurations in Industrial Symbiotic Networks : an Agent-Based Simulation Study[HTML].
  • Haryadi, F. N., Imron, M. A., Indrawan, H., & Triani, M. (2019, October). Predicting Rooftop Photovoltaic Adoption In The Residential Consumers of PLN Using Agent-Based Modeling. In 2019 International Conference on Technologies and Policies in Electric Power & Energy (pp. 1-5). IEEE.
  • Hasani, M. F., & Utama, N. P. (2019, September). Analysis of the Effect of the Number of Human Populations on the Spread of Dengue Fever. In 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (pp. 1-5). IEEE.
  • He, Z., Yuan, H., Li, Z., Gao, L., Zhang, E., Yao, Y., & Zhang, X. (2019, December). Automatic Route Guidance Method based on VANETs. In 2019 6th International Conference on Information Science and Control Engineering (ICISCE) (pp. 1009-1012). IEEE.
  • Hernandez-Betancur, J. E., Montoya-Restrepo, L. A., & Montoya-Restrepo, I. (2019). Deliberate Strategy Deconstructing Event for the Arising of the Emergent Strategy. Journal of Engineering and Applied Sciences, 14(22), 8452-8463.
  • Hilbert, M., Barnett, G., Blumenstock, J., Contractor, N., Diesner, J., Frey, S., ... & Zhu, J. J. (2019). Computational communication science: A methodological catalyzer for a maturing discipline.
  • Hilljegerdes, M., & Augustijn-Beckers, E. W. (2019, May). Evaluating the effects of consecutive hurricane hits on evacuation patterns in Dominica. In ISCRAM.
  • Hjorth, A. & Wilensky, U. (2019). Urban Planning-in-Pieces: A Computational Approach to Understanding Conceptual Change and Causal Reasoning about Urban Planning "Constructivist Foundations"
  • Hoffmann, B., Chalmers, K., Urquhart, N., & Guckert, M. (2019, February). Athos-A Model Driven Approach to Describe and Solve Optimisation Problems: An Application to the Vehicle Routing Problem with Time Windows. In Proceedings of the 4th ACM International Workshop on Real World Domain Specific Languages (pp. 1-10).
  • Hofstede, G. J., & Chappin, E. Archetypical Patterns in Agent-Based Models. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 313). Springer Nature.
  • Hofstede, G. J., & Chappin, E. (2019, September). Archetypical Patterns in Agent-Based Models. In Conference of the European Social Simulation Association (pp. 313-332). Springer, Cham.
  • Hofstede, G. J., Franco, E., Damen, F., & Fogliano, V. Healthy Snacks from Mom? An Agent-Based Model of Snackification in Three Countries. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 429). Springer Nature.
  • Holbert, N., & Wilensky, U. (2019). Designing Educational Video Games to Be Objects-to-Think-With. Journal of the Learning Sciences, 28(1), 32–72. https://doi.org/10.1080/10508406.2018.1487302 .
  • Hoyles, C. (2019). Micromundos, Construccionismo y Matemáticas1. Richard Noss y Celia Hoyles, Inglaterra❑ Significados, representaciones y lenguaje: las fracciones en tres generaciones de libros de texto para primaria Alicia Avila, México❑ Cómo trabajar la orientación espacial de modo significativo en Educación, 31(2), 7.
  • Huang, W., Cui, Y., & Xiao, X. (2019). Two-Way Mutual-Structure-Based Public Opinion Communication System: An Analysis with Simulation. Tehnički vjesnik, 26(1), 201-207.
  • Huang, W. D., & Cui, Y. (2019). Effect of Individual Cognitive Behavior Model on Public Opinion Communication Mechanism based on Social Ecosystem. Ekoloji Dergisi, (107).
  • Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., ... & McElhaney, K. (2019). C2STEM: a System for Synergistic Learning of Physics and Computational Thinking. Journal of Science Education and Technology, 1-18.
  • Hwang, Y. S., Lin, H. H., Pai, S. H., & Tu, C. H. (2019). Gpublocks: Gui programming tool for cuda and opencl. Journal of Signal Processing Systems, 91(3), 235-245.
  • Iapăscurtă, V. (2019). Detection of movement toward randomness by applying theblock decomposition method to a simple model of the circulatory system. Complex Systems, 28(1), 59-76.
  • Ilyinsky, A., & Goroshnikova, T. (2019, October). Navigation in NSR as Large-Scale System: Ship Path Analysis in Non-Severe Ice Condition. In 2019 Twelfth International Conference" Management of large-scale system development"(MLSD) (pp. 1-4). IEEE.[HTML]
  • Innocenti, E., Detotto, C., Idda, C., & Prunetti, D. (2019, April). Urban, agricultural and touristic land use patterns: combining spatial econometrics and ABM/LUCC. In 2019 4th World Conference on Complex Systems (WCCS) (pp. 1-6). IEEE.
  • Izquierdo, L. R., Izquierdo, S. S., & Sandholm, W. H. (2019). 0.4. The fundamentals of NetLogo. Agent-Based Evolutionary Game Dynamics.
  • Izquierdo, L. R., Izquierdo, S. S., & Sandholm, W. H. (2019). 2.1. Robustness and fragility. Agent-Based Evolutionary Game Dynamics.
  • Jacildo, A. J., Rabajante, J. F., & Alcantara, E. P. (2019). Agent-based Modeling of Asian Corn Borer Resistance to BT Corn. bioRxiv, 795724.
  • Jalali, S. H., Vafaeinejad, A. R., Aghamohammadi, H., & Esmaeili Bidhendi, M. (2019). The Study of CO Symptoms' Impacts on Individuals, Using GIS and Agent-based Modeling (ABM). Pollution, 5(3), 463-471.
  • Javed, A. (2019). Understanding Malware Behaviour in Online Social Networks and Predicting Cyber Attacks (Doctoral dissertation, Cardiff University).
  • Jaxa-Rozen, M., Kwakkel.H.J, Bloemendal, M. (2019) A coupled simulation architecture for agent-based/geohydrological modelling with NetLogo and MODFLOW. Environmental Modelling and Software 115 (2019)19-37 [PDF]
  • Kabeer, M., Riaz, F., Jabbar, S., Aloqaily, M., & Abid, S. (2019, June). Real World Modeling and Design of Novel Simulator for Affective Computing Inspired Autonomous Vehicle. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 1923-1928). IEEE.
  • Kamarainen, A., Metcalf, S., Dickes, A., Gun-Yildiz, S., Brennan, K., Grotzer, T., & Dede, C. (2019, April). Impact of blended immersive virtual world and programming curriculum on student perspectives about scientific modeling. In Annual meeting program American Educational Research Association.
  • Kamimura, K., Gardiner, B., Dupont, S., Finnigan, J. (2019) Agent-based modelling of wind damage proceses and patterns in forests. Agricultural and Forest Meteorology 268 (2019) 279-288. [PDF]
  • Kaminski, Y., & Malinouski, I. (2019). MODELING OF TRAFFIC FLOW USING THE NETLOGO ENVIRONMENT. In Progress through Innovations (pp. 38-39).
  • Kapat, S. K., & Tripathy, S. N. (2019). Malware Architectural View with Performance Analysis in Network at Its Activation State. In Cognitive Informatics and Soft Computing (pp. 207-216). Springer, Singapore.
  • Kardas-sloma, L., Perozziello, A., Zahar, J. R., Lescure, X., Yazdanpanah, Y., & Lucet, J. (2019). Transmission d’Escherichia coli résistant aux β-lactamines (E. coli BLSE) dans la communauté: modélisation et évaluation de l’impact des interventions. Médecine et Maladies Infectieuses, 49(4), S30-S31.
  • Kaur, H., & Sharma, A. (2019). Sanction Enforcement for Norm Violation in Multi-agent Systems: A Cafe Case Study. In Proceedings of the 2nd International Conference on Data Engineering and Communication Technology (pp. 325-335). Springer, Singapore.
  • Khan, M. Y. A., Nasir, G., Ahmed, S., Tahir, M., & Ahmad, M. (2019, January). MLA based protocol for monitoring electrical parameters in Smart Gird using WSN. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-7). IEEE.
  • Khan, O., Din, A., Khalid, A., Khan, A., Ahmed, A., & Zia, K. (2019, December). Online Adversarial Coverage for Multi-Agent Systems. In 2019 15th International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE.
  • Kim, C., Jin, Y. G., Park, J., & Kang, D. (2019). A case study of a last-mile solution in a high-density residential neighborhood. Procedia Computer Science, 151, 132-138.
  • Kim, C., Jin, Y. G., Park, J., & Kang, D. (2019). The influence of an autonomous driving car operation on commuters’ departure times. Procedia Computer Science, 151, 85-91.
  • Kim, Y., Son, J., Lee, Y. S., Lee, M., Hong, J., & Cho, K. (2019). Integration of an individual-oriented model into a system dynamics model: an application to a multi-species system. Environmental Modelling & Software, 112, 23-35.
  • Kleiner, G., Rybachuk, M., & Ushakov, D. (2019, September). An investigation of social-behavioral phenomena in the peer-review processes of scientific foundations. In International Conference on Modelling and Simulation of Social-Behavioural Phenomena in Creative Societies (pp. 68-81). Springer, Cham.
  • Kocheril, G., Krebs, F., Nacken, L., & Holzhauer, S. Open and Integrative Modelling in Energy System Transitions—Conceptual Discussion About Model Reusability, Framework Requirements and Features. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 499). Springer Nature.
  • Komendant-Brodowska, A., Jager, W., Abramczuk, K., Baczko-Dombi, A., Fecher, B., Sokolovska, N., & Spits, T. Peek Over the Fence—How to Introduce Students to Computational Social Sciences. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 279). Springer Nature.
  • Kopeček, I., & Daňa, J. (2019). Modeling and Simulating Communication, Stress, and Productivity in Socio-Economic Structures. Slavonic Natural Language Processing in the 21st Century, 151.
  • Koralewski, T. E., Westbrook, J. K., Grant, W. E., & Wang, H. H. (2019). Coupling general physical environmental process models with specific question-driven ecological simulation models. Ecological modelling, 405, 102-105.
  • Kostylenko, O., Rodrigues, H., Torres, D. (2019) The spread of a financial virus through Europe and beyond. AIMS Mathematics, 86-98. [PDF]
  • Kponyo, J. J., Coker, K., Agyemang, J. O., & Der, J. (2019). An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach.
  • Lahav, O., Hagab, N., Levy, S. T., & Talis, V. (2019). Computer-model-based audio and its influence on science learning by people who are blind. Interactive Learning Environments, 27(5-6), 856-868.
  • Lahav, O., Kittany, J., Levy, S. T., & Furst, M. (2019). Perception of sonified representations of complex systems by people who are blind. Assistive Technology, 1-9.
  • Lee, J. S., & Wolf-Branigin, M. (2019) Innovations in Modeling Social Good: A Demonstration With Juvenile Justice Intervention Research on Social Work Practice. [HTML]
  • Legaspi, J., Canfield, C. I., Gill, K. S., Wyglinski, A. M., & Bhadai, S. V. (2020, May). Integrated Agent-Based Model for Broadband Resource Allocation Analysis. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (pp. 1-5). IEEE.
  • Li, L., Xia, H., Zhang, R., & Li, Y. (2019, June). DDSEIR: A dynamic rumor spreading model in online social networks. In International Conference on Wireless Algorithms, Systems, and Applications (pp. 596-604). Springer, Cham.
  • Li, J. (2019, October). Simulation Research on Marketing Effect of Enterprise in Social Network Based on SIR Model. In 4th International Conference on Modern Management, Education Technology and Social Science (MMETSS 2019). Atlantis Press.
  • Linares, S. (2019). Modelos del crecimiento urbano.
  • Line Have Musaeus, P. M. (2019).Computational Thinking in the Danish High School: Learning Coding, Modeling, and Content Knowledge with NetLogo. Proceedings of the SIGCSE '19 Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 913-919). Minneapolis, MN, USA [PDF]
  • Lines, T., & Basiri, A. (2019, November). Simulating and modeling the signal attenuation of wireless local area network for indoor positioning. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation (pp. 9-15).
  • Lippe, M., Bithell, M., Gotts, N. Natalie, D., Barbrook, P., Giupponi, C., Hallier, M., Hofsted, G., Le Page, C., Matthews, R., Schluter, M., Smith, P., Teglio, A. & Thellman, K (2019). Using agent-based modelling to simulate social-ecological systems across scales. GeoInformatica 23.2, 269-298.
  • Liu, G., Casazza, M., & Lega, M. (2019). Simulation of coupled impact-management response scenarios for distributed wastewater environmental discharges at basin scale through urban environmental risk network transmission mechanism. Journal of environmental management, 236, 182-194.
  • Liu, G., Ye, J., & Argyres, C. (2019). Modeling and simulation of the knowledge growth process among new energy technology firms in the distributed innovation network. DYNA-Ingeniería e Industria, 95(1).
  • Liu, Y., Li, F. & Su, Y.(2019) Critical Factors Influencing the Evolution of Companies’ Environmental Behavior: An Agent-Based Computational Economic Approach SAGE Open. [PDF]
  • Lorenz, W., & Wurzer, G. (2019). Visual Representation of Adjacencies-A NetLogo application to turn functional matrices into bubble diagrams.
  • Lorenz, W., & Wurzer, G. (2019). Visual Representation of Adjacencies. In Architecture in the Age of the 4th Industrial Revolution Proceedings of the 37th eCAADe and 23rd SIGraDi Conference Volume (Vol. 2, pp. 11-20).
  • Lorig, F. (2019). Hypothesis-Driven Simulation Studies. In Hypothesis-Driven Simulation Studies (pp. 137-170). Springer Vieweg, Wiesbaden.
  • Lovellette, E., Hexmoor, H., & Rodriguez, K. (2019). Automated argumentation for collaboration among cyber-physical system actors at the edge of the Internet of Things. Internet of Things, 5, 84-96.
  • Lu, P. (2019). Heterogeneity, judgment, and social trust of agents in rumor spreading. Applied Mathematics and Computation, 350, 447-461.
  • Lu, P., Deng, L., & Liao, H. (2019). Conditional effects of individual judgment heterogeneity in information dissemination. Physica A: Statistical Mechanics and its Applications, 523, 335-344.
  • Luanda, A. (2019, June). A Gift-Exchange Model for the Maintenance of Group Cohesion in a Telecommunications Scenario. In Distributed Computing and Artificial Intelligence, 16th International Conference (Vol. 1003, p. 189). Springer.
  • Lumbreras Sancho, S., Wogrin, S., Navarro Llevat, G., Bertazzi, I., & Pereda García, M. (2019). A decentralized solution for transmission expansion planning: getting inspiration from nature.
  • MA Mahmoud, R., M Abdel Karim, N., & MA Youssef, A. (2019). Comparative analyses of computational implementations for healthcare building design. JES. Journal of Engineering Sciences, 47(5), 627-643.
  • MacCarthy, E. A. (2019). Modeling the Effect of Contact Rates on Infectious Diseases in Contact Networks. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 6(8), 10556-10560.
  • Mahdizadeh Gharakhanlou, N., & Mesgari, M. S. (2019). DEVELOPING A CELLULAR AUTOMATA MODEL FOR SIMULATING RAINFALL-RUNOFF PROCESS (CASE STUDY: BABOL CATCHMENT). International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
  • Mailleret, L., Davtian, D., & Grognard, F. (2018, July). An individual based model to optimize natural enemies deployment in augmentative biological control. In 11th European Conference on Mathematical and Theoretical Biology (ECMTB).
  • Malaina, A.(2019) The Paradigm of Complexity in Sociology: Epistemological and Methodological Implications, Complexity Applications.Language and Communication Sciences, 31-42. [HTML]
  • Malinowski, A., & Czarnul, P. (2019). Multi-agent large-scale parallel crowd simulation with nvram-based distributed cache. Journal of Computational Science, 33, 83-94.
  • Marcum-Dietrich, N., Bruozas, M., & Staudt, S. (2019). Precipitating Change: Integrating Meteorology, Mathematics, and Computational Thinking: Research on Students' Learning and Use of Data, Modeling, and Prediction Practices for Weather Forecasting. In International Society for Technology in Education (ISTE) Conference, Philadelphia, PA.
  • Martin, K., Horn, M., & Wilensky, U. (2019). Emergent Schema Learning in Short Museum Interactions. Paper Accepted to the Journal of Education Informatics, Vilnius, Lithuania.
  • Martin, K., Horn, M., & Wilensky, U. (2019). Constructivist Dialogue Mapping of learning in Museums and Informal Spaces. Paper Accepted to the Journal of Education Informatics, Vilnius, Lithuania
  • MARTIN, K., HORN, M., & WILENSKY, U. (2019). Prevalence of Direct and Emergent Schema and Change after Play. Informatics in Education, 18(1), 183-212.
  • Martin (2019) Future Visions for the study of Group Learning with Complex Systems Models: The role of multimodal data in group learning analysis. Computer Supported Collaborative Learning, Lyon, France.
  • Martin, K., Horn, M., & Wilensky, U. (2019). Learning Complexity through Open-Ended Agent Based Modeling. Paper Submitted to IDC 2019 Annual International conference, Boise, Idaho.
  • Martin, K., Wang, E. Q., Bain, C., & Worsley, M. (2019, October). Computationally augmented ethnography: Emotion tracking and learning in museum games. In International Conference on Quantitative Ethnography (pp. 141-153). Springer, Cham.
  • Mayes, R. (2019). Quantitative reasoning and its rôle in interdisciplinarity. In Interdisciplinary Mathematics Education (pp. 113-133). Springer, Cham.
  • Mayfield, J., & Mayfield, M. (2019). The diffusion process of strategic motivating language: An examination of the internal organizational environment and emergent properties. International Journal of Business Communication, 56(3), 368-392.
  • McGowan, L., & Westgren, R. A Study of Group Formation Using Agent-Based Modeling. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 115). Springer Nature.
  • McNulty, M., Smith, J.D., Villamar, J., Burnett-Zeigler, I., Vermeer, W., Benbow, N., Gallo, C., Wilensky, U., Hjorth, A., Mustanski, B., Schneider, C. & Brown, H. (2019). Implementation Research Methodologies for Achieving Scientific Equity and Health Equity. Ethnicity & Disease, 29(1), 83-92
  • Mertens, A., Feliciani, T., Heidari, S., Siebers, P. O., & Dignum, F. Are We Done Yet? or When is Our Model Perfect (Enough)?. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 285). Springer Nature.
  • Meskini, F. Z., & Aboulaich, R. (2019, October). Multi-agent based simulation of a smart insurance using blockchain technology. In 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS) (pp. 1-6). IEEE.
  • Milne, R. J., Cotfas, L. A., Delcea, C., Salari, M., Craciun, L., & Molanescu, A. G. (2019). Greedy method for boarding a partially occupied airplane using apron buses. Symmetry, 11(10), 1221.
  • Milne, R. J., Delcea, C., Cotfas, L. A., & Salari, M. (2019). New methods for two-door airplane boarding using apron buses. Journal of Air Transport Management, 80, 101705.
  • Mittal, A., Gibson, N. O., & Krejci, C. C. (2019, December). An agent-based model of surplus food rescue using crowd-shipping. In 2019 Winter Simulation Conference (WSC) (pp. 854-865). IEEE.
  • Mohammed, R., Kennedy-Clark, S., & Reimann, P. (2019). Using immersive and modelling environments to build scientific capacity in primary preservice teacher education. Journal of Computers in Education, 6(4), 451-481.
  • Montes, D. O., Suppi, R., De Giusti, L. C., Leporace, M., Micieli, M. V., Santini, M. S., & Naiouf, M. (2019). Simulación de altas prestaciones (GPU) para la reproducción del mosquito Aedes aegypti en el cementerio de Santo Tomé, Corrientes. In XXV Congreso Argentino de Ciencias de la Computación (CACIC)(Universidad Nacional de Río Cuarto, Córdoba, 14 al 18 de octubre de 2019).
  • Morelle, K., Buchecker, M., Kienast, F., & Tobias, S. (2019). Nearby outdoor recreation modelling: An agent-based approach. Urban Forestry & Urban Greening, 40, 286-298.
  • Mostafizi, A., Wang, H., & Dong, S. (2019). Understanding the multimodal evacuation behavior for a near-field tsunami. Transportation research record, 2673(11), 480-492.
  • Mozahem, N. A. (2019). Always negotiate, sometimes cooperate: an agent-based model. International Journal of Organization Theory & Behavior.
  • Muraru, A., Lile, R., Boșcoianu, E. C., Boșcoianu, M., Vladareanu, L. (2019). The UAV control approach by using multi agent systems. Periodicals of Engineering and Natural Science, vol. 7, no. 1.
  • Musaeus, L. H., & Musaeus, P. (2019, February). Computational Thinking in the Danish High School: Learning Coding, Modeling, and Content Knowledge with NetLogo. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 913-919).
  • Naili, M., Bourahla, M., & Naili, M. (2019). Stability-based model for evacuation system using agent-based social simulation and Monte Carlo method. International Journal of Simulation and Process Modelling, 14(1), 1-16.
  • Ng, H., Othman, W., Bakar, E., Mat Noor, N., & Hawary, A. (2019).[HTML] MEERKATS BEHAVIOR MODELLING USING NETLOGO. ROBOTIKA, 1(1), 16-21.
  • Nie, X. X., Bai, C., & Zhang, J. (2019). Simulation research on the effectiveness of a multiagent mine safety supervision system and its verification. Mathematical Problems in Engineering, 2019.
  • Norouziasl, S., Jafari, A., & Wang, C. (2019). Analysis of lighting occupancy sensor installation in building renovation using agent-based modeling of occupant behavior. In Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation (pp. 593-601). Reston, VA: American Society of Civil Engineers.
  • Noss, R., & Hoyles, C. (2019). Microworlds, Constructionism and Maths. Educación matemática, 31(2), 7-21.
  • O’Connor, J. An Analysis of the Various Factors Involved in the Evolution of Mimicry Using an Agent Based Programming Language and of the Body Massesand Reproduction Rates of Existing Mimetic Species.
  • Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A. A., Burgess, K. J., & Guo, L. (2019). Emerging technologies as pedagogical tools for teaching and learning science: A literature review. Human Behavior and Emerging Technologies, 1(2), 149-160.
  • Opiyo, N. N. (2019). Impacts of neighbourhood influence on social acceptance of small solar home systems in rural western Kenya. Energy Research & Social Science, 52, 91-98.
  • Opiyo, N. (2019, October). Neighbourhood influence and social acceptance of PV systems in rural developing communities. In 36th European Photovoltaic Solar Energy Conference (pp. 2006-2012). International Solar Energy Society.
  • Owusu, P. A., Leonenko, V. N., Mamchik, N. A., & Skorb, E. V. (2019). Modeling the growth of dendritic electroless silver colonies using hexagonal cellular automata. Procedia Computer Science, 156, 43-48.
  • Ozawa, S., Haynie, D., Bessias, S., Laing, S., Ladi, E. (2019) Modeling the Economic Impact of Substandard and Fƒalsified Antimalarials in the Democratic Republic of the Congo. The American Journal of Tropical Medicine and Hygiene.
  • Palau, A. S., Dhada, M. H., & Parlikad, A. K. (2019). Multi-agent system architectures for collaborative prognostics. Journal of Intelligent Manufacturing, 30(8), 2999-3013.
  • Park, J., Redwine, J., Hill, T. D., & Kotun, K. (2019). Water resource and ecotone transformation in coastal ecosystems. Ecological Modelling, 405, 69-85.
  • Park, J. W., & Arteaga, C. (2019). Human Responses of Emergency Evacuation Using Agent-Based Modeling. In International Conference on Smart Cities, Seoul, Korea (pp. 1-6).
  • Párraga-Álava, J., Garzón, G. M., & Valarezo, R. V. (2019). Multi-Objective Genetic Algorithms: are they useful for tuning parameters in Agent-Based Simulation?. Revista Ibérica de Sistemas e Tecnologias de Informação, (E19), 172-184.
  • Peel, A., Dabholkar, S., Anton, G., Horn, M., & Wilensky, U. (2019) Teachers’ professional growth through co-design and implementation of computational thinking (CT) integrated biology units. Annual Meeting of the Association of Science Teacher Education (ASTE) 2019. San Antonio, TX.
  • Peel, A., Dabholkar, S., Granito, T. (2019) Teaching Experimental Design with Computational Thinking. Poster presented at the National Association of Biology Teachers (NABT). 2019 November 14-17; Chicago, IL.
  • Pereira, A. I., Barbosa, J., & Leitao, P. (2019). Hybrid System for Simultaneous Job Shop Scheduling and Layout Optimization Based on Multi-agents and Genetic Algorithm. In Hybrid Intelligent Systems: 18th International Conference on Hybrid Intelligent Systems (HIS 2018) Held in Porto, Portugal, December 13-15, 2018 (Vol. 923, p. 387). Springer.
  • Perez, L., Dragicevic, S., & Gaudreau, J. (2019). A geospatial agent-based model of the spatial urban dynamics of immigrant population: A study of the island of Montreal, Canada. PloS one, 14(7).
  • Petrosino, T., Sherard, M., & Brady, C. (2019). Using Collaborative Agent-based Modeling to Explore Complex Phenomena with Elementary Preservice Science Teachers. Poster session presented at Computer Supported Collaborative Learning, Lyon, France.
  • Petrosino, A. J., Sherard, M. K., Harron, J. R., Brady, C. E., Stroup, W. M., & Wilensky, U. J. (April, 2019). Developing preservice teachers’ conceptualization of models and simulations through group-based cloud computing. Poster presented at the American Education Research Association Annual Meeting, Toronto, Canada
  • Phetheet, J., Heger, W., & Hill, M. C. (2019, December). Evaluating Use of Water and Renewable Energy in Agricultural Areas: A Coupled Simulation of DSSAT and Agent-Based Modeling. In AGU Fall Meeting 2019. AGU.
  • Piccione, A., et al. (2019). An Agent-Based Simulation API for Speculative PDES Runtime Environments. 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Chicago, Il, USA, June 03-05. New York, NY, USA: ACM.
  • Plikynas, D., Laužikas, R., Sakalauskas, L., Miliauskas, A., & Dulskis, V. (2019, September). Agent-based simulation of cultural events impact on social capital dynamics. In Proceedings of SAI Intelligent Systems Conference (pp. 1138-1154). Springer, Cham.
  • Plikynas, D., Miliauskas, A., & Laužikas, R. (2019, December). Simulation of Social Capital Dynamics: Impact of Cultural Events. In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence (pp. 315-319).
  • Ponsiglione, C., Primario, S., & Zollo, G. (2019). Does natural language perform better than formal systems? Results from a fuzzy agent-based model. International Journal of Technology, Policy and Management, 19(2), 171-195.
  • Portocarrero Sarmento, R. (2019). Inventory Management-A Case Study with NetLogo. arXiv preprint arXiv:1905.08041.
  • Putra, H. C., Andrews, C. J., & Senick, J. A. Modeling building occupant behavior during load shedding. Management, 23(15), 3267-95.
  • Quan, J., & Liu, Y. (2019, January). Construction and Simulation Analysis of Cooperative Game Model of Hospital Group in Medical Waste Stream System. In 2018 International Conference on Mathematics, Modeling, Simulation and Statistics Application (MMSSA 2018). Atlantis Press
  • Rai, S., Carter, T., & Sharma, B. (2019). Using NetLogo to simulate building occupancy of a university building. ASEE 2019 Annual Conference.
  • Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: a practical introduction. Princeton university press.
  • Raimbault, J., & Pumain, D. (2019). Methods for exploring simulation models. Geographical Modeling: Cities and Territories, 2, 125-150.
  • Raglin, A., & Metu, S. (2019, May). Agent based simulation of decision making with uncertainty. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications (Vol. 11006, p. 110060M). International Society for Optics and Photonics.
  • Ramazanov, R. (2019). Agent-based modeling the distribution of authorities between the levels of the state. Artificial societies, 14(3).
  • Ramazanov, R. (2019). Simulation analysis of China fiscal models. Artificial Societies, 14(4).
  • Ramos, A., Calado, M., & Antunes, L. (2019, June). A Gift-Exchange Model for the Maintenance of Group Cohesion in a Telecommunications Scenario. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 189-196). Springer, Cham.
  • Rao, V. A. (2019). Distributed data-gathering protocols in AD-HOC wireless networks (Doctoral dissertation, IIT Delhi).
  • Rengifo, M. G. H., & Díaz-Ambrona, C. G. H. (2019). Comportamiento demográfico: Dinámico–Probabilístico de los pueblos indígenas en aislamiento de la amazonía ecuatoriana. Revista Científica Axioma, (20), 25-34.
  • Reuillon, R., Leclaire, M., Raimbault, J., Arduin, H., Chapron, P., Chérel, G., ... & Perret, J. (2019, September). Fostering the use of methods for geosimulation models sensitivity analysis and validation
  • Rey-Coyrehourcq, S., Banos, A., & Raimbault, J. (2019, October). Le calcul intensif en géographie: une tradition bien ancrée. In JCAD Journées Calcul et Données 2019.
  • Reynolds, E. R., Himmelwright, R., Sanginiti, C., & Pfaffmann, J. O. (2019). An agent-based model of the Notch signaling pathway elucidates three levels of complexity in the determination of developmental patterning. BMC systems biology, 13(1), 1-16.
  • R Izquierdo, L., S Izquierdo, S., & H Sandholm, W. (2019). Agent-Based Evolutionary Game Dynamics. Independent.
  • Roach, A. R., Dennison, E. M., Hyrich, K. L., & MacGregor, A. J. (2019). O23 Using big data in the design and validation of a simulation of the healthcare system for patients with inflammatory rheumatic disease: results from the SiMSK study. Rheumatology, 58(Supplement_3), kez105-022.
  • Roach, A. R., Dennison, E. M., Hyrich, K. L., & MacGregor, A. J. (2019). O24 The impact of early referral and lowering clinical thresholds of biologic access on the disease course and costs in RA: results from the SiMSK study. Rheumatology, 58(Supplement_3), kez105-023.
  • Rodemann, T., Eckhardt, T., Unger, R., & Schwan, T. (2019). Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems. Energies, 12(15), 2858.
  • Romanowska, I., Crabtree, S., Harris, K., & Davies, B. (2019). Agent-Based Modeling for Archaeologists: Part 1 of 3. Advances in Archaeological Practice, 7(2), 178-184. [HTML]
  • Rozo, K. R., Arellana, J., Santander-Mercado, A., & Jubiz-Diaz, M. (2019). Modelling building emergency evacuation plans considering the dynamic behaviour of pedestrians using agent-based simulation. Safety science, 113, 276-284.
  • Sahnoun, M. H., Baudry, D., Mustafee, N., Louis, A., Smart, P. A., Godsiff, P., & Mazari, B. (2019). Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system. Journal of Intelligent Manufacturing, 30(8), 2981-2997.
  • Salecker, J., Sciaini, M., Meyer, K. M., & Wiegand, K. (2019). The nlrx R package: A next‐generation framework for reproducible NetLogo model analyses. Methods in Ecology and Evolution.[PDF]
  • Salt, D., & Polhill, G. A NetLogo Extension to Secure Data Using GNUs Pretty Good Privacy Software Suite. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 299). Springer Nature.
  • Sanginiti, C., Pfaffmann, J. O., Reynolds, E. R., & Himmelwright, R. An agent-based model of the Notch signaling pathway elucidates three levels of complexity in the determination of developmental patterning.
  • Santos, F., Nunes, I., & Bazzan, A. L. (2020). Quantitatively assessing the benefits of model-driven development in agent-based modeling and simulation. Simulation Modelling Practice and Theory, 104, 102126.
  • Sapienza, A., & Falcone, R. (2019, June). Social Recommendations: Have We Done Something Wrong?. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 281-284). Springer, Cham.
  • Saputro, N. (2019, December). Game-Theoretic and Genetic-Based Approach for Cooperative Mission-Oriented Swarms of Drones. In 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE) (pp. 163-168). IEEE.
  • Sarmento, R. P. (2019). Inventory Management-A Case Study with NetLogo. arXiv preprint arXiv:1905.08041
  • Saxena, N. (2019). A Designer’s Reflections on Designing for ‘Productive Failure’.
  • Saxena, N. (2019). Pallas Advanced Learning Systems–A research-informed virtual learning kit. Journal of Applied Learning and Teaching, 2(1), 65-68.
  • Scheller, S. (2019). Steven F. Railsback and volker grimm, agent-based and individual-based modeling. a practical introduction. Œconomia. History, Methodology, Philosophy, (9-2), 407-413.
  • Scherjon, F., Romanowska, I., & Lambers, K. (2019). Digitally Teaching Digital Skills: Lessons Drawn from a Small Private Online Course (SPOC) on ‘Modelling and Simulation in Archaeology’ at Leiden University. Journal of Computer Applications in Archaeology, 2(1), 79–88. doi: 10.5334/jcaa.26
  • Scheller, S. (2019). Steven F. Railsback and Volker Grimm, Agent-Based and Individual-Based Modeling. A Practical Introduction. Œconomia. History, Methodology, Philosophy, (9-2), 407-413.
  • Sciullo, A., Vallino, E., Iori, M., & Fontana, M. (2019). Paths and processes in complex electricity markets: The agent-based perspective. In Routledge Handbook of Energy Economics (pp. 522-533). Routledge.
  • Schmidt, A. H., & Zhang, K. (2019). Agent-Based Modelling: A New Tool for Legal Requirements Engineering: Introduction and Use Case (KEI). European Quarterly of Political Attitudes and Mentalities, 8(1), 1-21.
  • Schneider, R., & Kouros, J. (2019). Effiziente Programmierung sozialwissenschaftlicher Modelle. In Simulieren und Entscheiden (pp. 231-256). Springer VS, Wiesbaden.
  • Schwan, T., Unger, R., & Eckhardt, T. FMI-Based Co-Simulation of Multi-Agent Occupants Models, with Modelica Building and HVAC System Models.
  • Sengupta, P., Kim, B., & Shanahan, M. C. (2019). Playfully coding science: Views from preservice science teacher education. In Critical, Transdisciplinary and Embodied Approaches in STEM Education (pp. 177-195). Springer, Cham.
  • Serrano, E., & Satoh, K. (2019, November). An agent-based model for exploring pension law and social security policies. In JSAI International Symposium on Artificial Intelligence (pp. 50-63). Springer, Cham.
  • Sharma, A. K., & Asirwatham, L. (2019). Learning by Computing: A First Year Honors Chemistry Curriculum. In Using Computational Methods To Teach Chemical Principles (pp. 127-138). American Chemical Society.
  • Shen, Y., Guo, Y., & Chen, W. (2019). Safety analysis of China’s marine energy channel based on Multi-Agent simulation. Energy Procedia, 158, 3259-3264.
  • Sherin, B. (2019). Machine Learning and the Perils of Prolific Pattern Finding. Constructivist Foundations, 14(3), 285-287.
  • Shirazi, E. & Jadid, S.(2019) A multiagent design of self-healing in eletric power distribution systems. Electric Power Systems Research. [PDF]
  • Shirkhodaie, G., & Rahman, A. (2019). Investigating the Effects of the Spread of Contagious Disease and Immunization of Population on Social Welfare Using Agent Based Modeling. Social Welfare Quarterly, 18(70), 181-208.
  • Shvarts, A., & Abrahamson, D. (2019). Dual-eye-tracking Vygotsky: A microgenetic account of a teaching/learning collaboration in an embodied-interaction technological tutorial for mathematics. Learning, Culture, and Social Interaction, 22, 100316. https://doi.org/10.1016/j.lcsi.2019.05.003
  • Silva, J., Varela, N., & Lezama, O. B. P. (2019, September). Optimizing Street Mobility Through a NetLogo Simulation Environment. In International Conference On Computational Vision and Bio Inspired Computing (pp. 48-55). Springer, Cham.
  • Slate, J. E., Adler, R. F., & Hibdon, J. E. (2019). Scott T. Mayle, Hanna Kim, and Sudha Srinivas Northeastern Illinois University. Integrating Digital Technology in Education: School-University-Community Collaboration, 55.
  • Smart, H. (2019). Operationalizing a conceptual model of colorism in local policing. Social justice research, 32(1), 72-115.
  • Smarzhevskiy, I. (2019). Behaviour in Hierarchy NetLogo Model. Description, ODD Documentation, Result. Description, ODD Documentation, Result (September 9, 2019).
  • Smarzhevskiy, I. A. (2019, November). The Distribution Model of Social and Psychological Properties on a Dynamic Set of Agents. In The International Scientific and Practical Forum “Industry. Science. Competence. Integration” (pp. 376-382). Springer, Cham.
  • Spitznagel, B., Weigal, J., & Rodriguez, J. (2019). Visualizing Viscous Flow and Diffusion in the Circulatory System. The Physics Teacher, 57(8), 529-532.
  • Sukarno, S. A. (2019). Méthodes d'approximation au problème de routage de véhicule pour une gestion de flotte de drones (Doctoral dissertation, Valenciennes, Université Polytechnique Hauts-de-France).
  • Sulis, E., Amantea, I. A., & Fornero, G. (2019, December). Risk-aware business process modeling: a comparison of discrete event and agent-based approaches. In 2019 Winter Simulation Conference (WSC) (pp. 3152-3159). IEEE.
  • Sultanov, M., & Crape, B. L. (2019). PIN135 MODELING HUMAN PAPILLOMAVIRUS TRANSMISSION FOR VACCINE EVALUATIONS: A PRELIMINARY AGENT-BASED MODEL. Value in Health, 22, S661.
  • Sun, B. (2019). Technology Innovation Diffusion Mechanisms of Agricultural Machinery in an Innovation Ecosystem. Revista de la Facultad de Agronomia de la Universidad del Zulia, 36(5).
  • Sun, J., Zheng, M., Skitmore, M., Xia, B., & Wang, X. (2019). Industry effect of job hopping: an agent-based simulation of Chinese construction workers. Frontiers of Engineering Management, 6(2), 249-261.
  • Swanson H., Anton G., Bain C., Horn M., Wilensky U. (2019) Introducing and Assessing Computational Thinking in the Secondary Science Classroom. In: Kong SC., Abelson H. (eds) Computational Thinking Education. Springer, Singapore
  • Swanson, H., & Wilensky, U. (2019, August). Engaging students in theory building in the science classroom. Poster to be presented at the biennial conference of the European Association for Research on Learning and Instruction, Aachen, Germany.
  • Taillandier, P., Gaudou, B., Grignard, A., Huynh, Q. N., Marilleau, N., Caillou, P., ... & Drogoul, A. (2019). Building, composing and experimenting complex spatial models with the GAMA platform. GeoInformatica, 23(2), 299-322.
  • Taillandier, P., Grignard, A., Marilleau, N., Philippon, D., Huynh, Q. N., Gaudou, B., & Drogoul, A. (2019). Participatory modeling and simulation with the gama platform. Journal of Artificial Societies and Social Simulation, 22(2).
  • Tan, Z., Othman, W. A. F. W., Wahab, A. A. A., & Alhady, S. S. N. (2019). CROWD DYNAMICS ANALISYS: SIMULATING HETEREOGENEOUS CROWDS WITH PANIC EFFECT STOCHASTICS BEHAVIOUR. Journal of Fundamental and Applied Sciences, 11(2), 838-856.
  • Tashakor, G., & Suppi, R. (2019). Agent-based model for tumour-analysis using Python+ Mesa. arXiv preprint arXiv:1909.01885.
  • Tashakor, G., & Suppi, R. (2019, July). Simulation and computational analysis of multiscale graph agent-based tumor model. In 2019 International Conference on High Performance Computing & Simulation (HPCS) (pp. 298-304). IEEE.
  • Taves, A. (2019). Modeling Theories and Modeling Phenomena: A Humanist’s Initiation. In Human Simulation: Perspectives, Insights, and Applications (pp. 83-94). Springer, Cham.
  • Thiel, S. (2019). An Agent-Based Ecological Model of West Nile Virus for Classroom Use.
  • Todorova, V. (2019). Playing with Information Source. In At the Intersection of Language, Logic, and Information: ESSLLI 2018 Student Session, Sofia, Bulgaria, August 6–17, 2018, Selected Papers 30 (pp. 171-184). Springer Berlin Heidelberg.
  • Tofah, D. K. W. (2019). Production and Logistics Systems Improvements-Biim Ultrasound AS (Master's thesis, UiT Norges arktiske universitet).
  • Triastanto, A. N. D., & Utama, N. P. (2019, September). Model Study of Traffic Congestion Impacted by Incidents. In 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (pp. 1-6). IEEE.
  • Tubadji, A., Angelis, V., & Nijkamp, P. (2019). Micro-cultural preferences and macro-percolation of new ideas: A NetLogo simulation. Journal of the Knowledge Economy, 10(1), 168-185
  • Urbanová, P. (2019). Agent based modeling of fish shoal behaviour.
  • Vahdati, A. R. (2019). Agents. jl: agent-based modeling framework in Julia. Journal of Open Source Software, 4(42), 1611.
  • Valdez, A. C., & Ziefle, M. (2019). Predicting acceptance of novel technology from social network data-an agent-based simulation-approach. In International Conference on Competitive Manufacturing (COMA), CIRP. Stellenbosch, South Africa.
  • Valente, J. A. (2019). The Role of Debugging in Knowledge Construction. Constructivist Foundations, 14(3).
  • Varughese, J. C., Moser, D., Thenius, R., Wotawa, F., & Schmickl, T. (2019). swarmfstaxis: Borrowing a swarm communication mechanism from fireflies and slime mold. In Complex Adaptive Systems (pp. 213-222). Springer, Cham.
  • Verwaart, T., van Wassenaer, L., & Hofstede, G. J. Agent-Based Simulation of Policies to Reconnect a City and the Countryside. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 101). Springer Nature.
  • Vogelstein, L., & Brady, C. (2019). Taking the Patch Perspective: A Comparative Analysis of a Patch Based Participatory Simulation.
  • Wagner, M., & de Vries, W. T. (2019). Comparative review of methods supporting decision-making in urban development and land management. Land, 8(8), 123.
  • Waldherr, A., & Wettstein, M. (2019). Bridging the gaps: using agent-based modeling to reconcile data and theory in computational communication science. International Journal of Communication, 13, 3976-3999.
  • Walker, B., & Johnson, T.V.(2019) NetLogo and GIS: A Powerful Combination EPiC Series in Computing, vol 58, 257-264. [PDF]
  • Wang, H. H., Grant, W. E., Elliott, N. C., Brewer, M. J., Koralewski, T. E., Westbrook, J. K., ... & Sword, G. A. (2019). Integrated modelling of the life cycle and aeroecology of wind-borne pests in temporally-variable spatially-heterogeneous environment. Ecological modelling, 399, 23-38.
  • Warnke, T., & Uhrmacher, A. M. (2019, October). Reproducible parallel simulation experiments via pure functional programming. In 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT) (pp. 1-8). IEEE.
  • Warren, A., & Sattenspiel, L. (2020). Artificial Long House Valley v1. 0.0. CoMSES Computational Model Library.
  • Weintrop, D., Bau, D., & Wilensky U. (2019). The Cloud is the Limit: A case study of programming on the web, with the web. International Jounal of Child-Computer Interaction. [PDF]
  • Weintrop, D., & Wilensky, U. (2019). Transitioning from introductory block-based and text-based environments to professional programming languages in high school computer science classrooms. Computers & Education, 142.
  • Wijermans, N., Verhagen, H., & Lytter, A. An Online Implementation of a Virtual Agent-Based Experiment Tool—An Exploration. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 291). Springer Nature.
  • Wells, L., Bednarz, T., & Au, A. (2019). Applying reinforcement learning techniques to operations-level wargaming scenarios. In 23rd International Congress on Modelling and Simulation-Supporting evidence-based decision making: the role of modelling and simulation (pp. 74-74).
  • Wen, G., Huang, N., & Zhu, J. (2019, November). A node-centric network congestion estimation method considering average spatio-temporal scale. In Journal of Physics: Conference Series (Vol. 1345, No. 4, p. 042062). IOP Publishing.[HTML]
  • Witwytzkyj, J., Valle Filho, A., & Santana, A. L. M. (2019, November). Modelagem Bifocal Aplicada à Engenharia Mecânica: Desenvolvimento de um modelo computacional de condução térmica para uso educacional no ensino superior. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação (Vol. 8, No. 1, p. 1084).
  • Wozniak, M. Conceptual Framework for Modeling Complex Urban Systems—From Theoretical Assumptions to Empirical Basis. In Advances in Social Simulation: Proceedings of the 15th Social Simulation Conference: 23–27 September 2019 (p. 509). Springer Nature.
  • Wyeld, T., Shen, H., & Bednarz, T. (2019, November). Containerisation as a method for supporting multiple VR visualisation platforms from a single data source. In The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry (pp. 1-3).
  • Xanthopoulou, T. D., Prinz, A., & Shults, F. L. (2019, September). Generating Executable Code from High-Level Social or Socio-Ecological Model Descriptions. In International Conference on System Analysis and Modeling (pp. 150-162). Springer, Cham.
  • Xi, J. Y. S., & Chan, W. K. V. (2019, December). Simulation of knife attack and gun attack on university campus using agent-based model and GIS. In 2019 Winter Simulation Conference (WSC) (pp. 263-272). IEEE.
  • Xia, H., Zhang, S., Li, Y., Pan, Z., Peng, X., & Cheng, X. (2019) "An Attack-Resistant Trust Inference Model for Securing Routing in Vehicular Ad Hoc Networks," in IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 7108-7120, July 2019. doi: 10.1109/TVT.2019.2919681
  • Xiang, L., & Mitchell, A. (2019). Investigating bark beetle outbreaks. Science Scope, 42(6), 65-76.
  • Yang, B. (2019). Machine learning-based evolution model and the simulation of a profit model of agricultural products logistics financing. Neural Computing and Applications, 31(9), 4733-4759.
  • Yang, B., & Chen, Y. A. (2019). Evolution model and simulation of logistics outsourcing for manufacturing enterprises based on multi-agent modeling. Cluster Computing, 22(3), 6807-6815.
  • Yang, T., Gao, W., & Zhang, F. (2019, September). Summary of Research on Power Boosting Technology of Distributed Mobile Energy Storage Charging Piles. In 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC) (pp. 1770-1776). IEEE.
  • Ye, X., Chen, B., Li, P., Jing, L., & Zeng, G. (2019). A simulation-based multi-agent particle swarm optimization approach for supporting dynamic decision making in marine oil spill responses. Ocean & Coastal Management, 172, 128-136.
  • Yee, G. Q. M. (2019). Self-assembly for supply chains.
  • Yongchen, G., & Yang, S. (2019). Multi-Agent Modeling and Simulation on China's Marine Energy Channel Security. Journal of System Simulation, 31(4), 655.
  • Young, E. (2019). Prioritevac, An Adaptive Model for Evacuation: Agent Based Simulation of the Station Nightclub Fire (Doctoral dissertation, University of Delaware).
  • Yousefi, M., & Yousefi, M. (2019). Human resource allocation in an emergency department: A metamodel-based simulation optimization. Kybernetes.
  • Yu, B., Guo, Z., Peng, Z., Wang, H., Ma, X., & Wang, Y. (2019). Agent-based simulation optimization model for road surface maintenance scheme. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04018065.
  • Yue, T., Long, R., Chen, H., Liu, J., Liu, H., & Gu, Y. (2019). Energy-Saving Behavior of Urban Residents in China: A Multi-Agent Simulation. Journal of Cleaner Production, 119623.[HTML]
  • Yuhong, Z. H. A. O., & Jie, C. H. E. N. (2019, October). A P2P trust model based on trust factor and feedback aggregation. In 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE) (pp. 214-219). IEEE.
  • Zhang, E. Z., & Zhang, X. (2019, July). Road traffic congestion detecting by VANETs. In Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019).
  • Zhang, N., & Zheng, X. (2019). Agent-based simulation of consumer purchase behaviour based on quality, price and promotion. Enterprise Information Systems, 13(10), 1427-1441.
  • Zhao, G., Zhao, R., & Yang, X. (2019). Fostering Fifth-Grade Students’ Ability to Design Comparative Experiments Using an Online Model. Journal of Education and Development, 3(1), 1.
  • Zhou, Y., & Wu, H. (2019). Dynamic analysis and simulation study of knowledge flow under the perspective of industrial transfer. Expert Systems, 36(5), e12345.
  • Zhu, H. (2019, May). A Platoon-based Rolling Optimization Algorithm at Isolated Intersections in A Connected and Autonomous Vehicle Environment. In Proceedings of the 2019 5th International Conference on Computing and Data Engineering (pp. 41-46).
  • Zia, K., Saini, D. K., & Muhammad, A. (2019). Efficient evacuation in a multi-exit environment: an agent-based decision support model. International Journal of Information and Decision Sciences, 11(4), 355-375.
  • Zoto, E., Kianpour, M., Kowalski, S. J., & Lopez-Rojas, E. A. (2019). A socio-technical systems approach to design and support systems thinking in cybersecurity and risk management education. Complex Systems Informatics and Modeling Quarterly, (18), 65-75.
  • Zou, J., Sun, H., Fan, B., & Zhao, Y. (2019, October). A General Simulation Framework for Crowd Network Simulations. In Proceedings of the 4th International Conference on Crowd Science and Engineering (pp. 12-19).
  • Zu, C., Zeng, H. & Zhou, X.(2019) Computational Simulation of Team Creativity: The Benefit of Member Flow. Frontiers in Psychology. [PDF]
  • Zulkarnay, I. (2019). Simulation modeling in the study of the optimal placement of systemically important universities across the country. Artificial Societies, 14(4).

2018

  • Adelt, F., Weyer, J., Hoffmann, S., & Ihrig, A. (2018). Simulations of the governance of complex systems (SimCo): Basic concepts and experiments on urban transportation. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3654
  • Agarwal, A., Hartman, T., & Goel, A. K. (2018). From Middle School to Graduate School: Combining Conceptual and Simulation Modeling for Making Science Learning Easier. In CogSci.
  • Almagor, J., Benenson, I., & Czamanski, D. (2018). The Evolution of the Land Development Industry: An Agent-Based Simulation Model. In Trends in Spatial Analysis and Modelling (pp. 93-120). Springer, Cham.[PDF]
  • Alvarado, A., Mata, A., Segovia, M., & Vargas, E. (2018). Emergencia De Ideas Matemáticas En Secundaria Con Simulaciones Participativas En Netlogo. REVISTA ELECTRÓNICA AMIUTEM, 2(1), 88-94.
  • Alves, F., Pereira, A. I., Barbosa, J., & Leitão, P. (2018, June). Scheduling of home health care services based on multi-agent systems. In International conference on practical applications of agents and multi-agent systems (pp. 12-23). Springer, Cham.
  • Alves, F., Varela, M. L. R., Rocha, A. M. A., Pereira, A. I., Barbosa, J., & Leitão, P. (2018, December). Hybrid system for simultaneous job shop scheduling and layout optimization based on multi-agents and genetic algorithm. In International Conference on Hybrid Intelligent Systems (pp. 387-397). Springer, Cham.
  • Amin, E., Abouelela, M., & Soliman, A. (2018). The role of heterogeneity and the dynamics of voluntary contributions to public goods: An experimental and agent-based simulation analysis. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3585
  • Anderson, A. (2018). Costs and Benefits of Copulatory Silk Wrapping in the Nursery Web Spider, Pisaurina mira Walckenaer, 1837 (Araneae, Pisauridae). The University of Nebraska - Lincoln, ProQuest Dissertations Publishing. [PDF]
  • Anderson, T., & Dragićević, S. (2018). Deconstructing Geospatial Agent-Based Model: Sensitivity Analysis of Forest Insect Infestation Model. In Agent-Based Models and Complexity Science in the Age of Geospatial Big Data (pp. 31-44). Springer, Cham.[PDF]
  • Anderson, W., Kobold, K., & Yakimenko, O. (2018). Employing Systems Engineering Tools to Analyze Green Microgrids for Remote Islands.World Academy of Science, Engineering and Technology, International Journal of Energy and Power Engineering, 5(6).[PDF]
  • Andre, R. (2018). Simulating the Emergence of Social Complexity Using Agent-Based Modelling. In MEi: CogSci Conference 2018 (p. 64).
  • Antoniou, V., & Schlieder, C. (2018). Addressing Uneven Participation Patterns in VGI Through Gamification Mechanisms. In Geogames and Geoplay (pp. 91-110). Springer, Cham.[PDF]
  • Aurambout, J. P., & Endress, A. G. (2018). A model to simulate the spread and management cost of kudzu (Pueraria montana var. lobata) at landscape scale. Ecological Informatics, 43, 146-156.[PDF]
  • Avvenuti, M., Cimino, M. G. C., Cola, G., & Vaglini, G. (2018, December). Detection and mapping of a toxic cloud using uavs and emergent techniques. In International Conference on Mining Intelligence and Knowledge Exploration (pp. 215-224). Springer, Cham.
  • Baeza, A., & Janssen, M. A. (2018). Modeling the decline of labor-sharing in the semi-desert region of Chile. Regional Environmental Change, 18(4), 1161-1172.[PDF]
  • Bassett, K. (2018). Agent-Based Modeling of Pollen Competition.
  • Beauchemin, C. A., Liao, L. E., & Blahut, K. (2018). Tutorial on agent-based models in NetLogo applied to immunology and virology. arXiv preprint arXiv:1808.09499.
  • Berea, A. (2018). Emergence of Communication in Socio-Biological Networks. Springer International Publishing.[PDF]
  • Berea, A. (2018). Constructed Language Versus Bio-chemical Communication: An Agent-Based Model and Applications. In Emergence of Communication in Socio-Biological Networks (pp. 31-49). Springer, Cham.[PDF]
  • Bourgais, M., Taillandier, P., Vercouter, L., & Adam, C. (2018). Emotion modeling in social simulation: A survey. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3681
  • Boyd, R., Roy, S., Sibly, R., Thorpe, R., & Hyder, K. (2018). A general approach to incorporating spatial and temporal variation in individual-based models of fish populations with application to Atlantic mackerel. Ecological Modelling, 382, 9-17.[PDF]
  • Bozuyla, M., Tola, A. T., & Murat, Y. S. (2018). A Novel Safe Merging Algorithm for Connected Vehicles Using NetLogo. Elektronika ir Elektrotechnika, 24(3), 3-7.
  • Brady, C., Petrosino, A., Stroup, W., & Wilensky, U. (2018). Group-based cloud computing: Technological supports for social constructionism. In Dagiene, V. & Jasute, E. (Eds.) Proceedings of the Constructionism 2018 conference. Vilnius, Lithuania.
  • Brilliantova, A., Pletenev, A., Doronina, L., & Hosseini, H.(2018). An agent-based model of an endangered population of the Arctic fox from Mednyi Island. Moscow University, University of Munster, Rochester Institute of Technology [PDF]
  • Broniatowski, D. A., & Moses, J. (2018). The Flexibility of Generic Architectures: Lessons from the Human Nervous System. In Disciplinary Convergence in Systems Engineering Research (pp. 585-598). Springer, Cham.[PDF]
  • Burrows, A., French, D. (2018). Evidence of Science and Engineering Practices in Preservice Secondary Science Teachers’ Instructional Planning Journal of Science Education and Technology, (p. 1-14). [PDF]
  • Castillo, E. A., & Trinh, M. P. (2018). In search of missing time: A review of the study of time in leadership research. The Leadership Quarterly, 29(1), 165-178.[PDF]
  • Carbo, J., Sanchez-Pi, N., & Molina, J. M. (2018). Agent-based simulation with NetLogo to evaluate ambient intelligence scenarios. Journal of Simulation, 12(1), 42-52.
  • Carillo, M., Cordasco, G., Serrapica, F., Scarano, V., Spagnuolo, C., & Szufel, P. (2018). Distributed simulation optimization and parameter exploration framework for the cloud. Simulation Modelling Practice and Theory, 83, 108-123.[PDF]
  • Chaudhari, K. S., Kandasamy, N. K., Krishnan, A., Ukil, A., & Gooi, H. B. (2018). Agent Based Aggregated Behavior Modelling For Electric Vehicle Charging Load. IEEE Transactions on Industrial Informatics.[PDF]
  • Chen, W., Liu, H., & Xu, D. (2018). Dynamic pricing strategies for perishable product in a competitive multi-agent retailers market. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3710
  • Chennoufi, M., Bendella, F., & Bouzid, M. (2018). Multi-agent simulation collision avoidance of complex system: application to evacuation crowd behavior. International Journal of Ambient Computing and Intelligence (IJACI), 9(1), 43-59.
  • Chi, Y. (2018). Self-Organized Bike Redistribution in Urban City. American Journal of Operations Research, 8(5), 386-394.
  • Chumachenko, D., Dobriak, V., Mazorchuk, M., Meniailov, I., & Bazilevych, K. (2018, February). On agent-based approach to influenza and acute respiratory virus infection simulation. In Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), 2018 14th International Conference on (pp. 192-195). IEEE.[PDF]
  • Clapp, J. (2018). The Promise of Systems Science in Health Behavior Research: The Example of Studying Drinking Events. Health Behavior Research 1(2) [PDF]
  • Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High Performance Computer Simulations
  • Condro A., Pawitan H., Risdyanto I. (2018). Predicting drought propagation within peat layers using a three dimensionally explicit voxel based model (pp. 149). IOP Conference: earth Envionmental Science.[PDF]
  • Cortier, O., Boutouil, M., & Maquaire, O. (2018, September). Quantifying Benefits of Permeable Pavement on Surface Runoff, An Agent-Based-Model with NetLogo. In International Conference on Urban Drainage Modelling (pp. 729-733). Springer, Cham.
  • Crittenden, J., Fujimoto, R., Lu, Z., Pecher, P. (2018). Granular Cloning: Intra-Object Parallelism in Ensemble Studies Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Pages 165-176.[PDF]
  • Dabholkar, S., Anton, G., & Wilensky, U. (2018). Developing mathetic content knowledge using an emergent systems microworld. Proceedings of Constructionism.
  • Daeichian, A., & Haghani, A. (2018). Fuzzy Q-Learning-Based Multi-agent System for Intelligent Traffic Control by a Game Theory Approach. Arabian Journal for Science and Engineering, 1-7.[PDF]
  • Daňa, J., & Ráček, J. (2018). Modeling and Simulating Cooperation in Organizations. IT for Practice 2018, 79.
  • Davis, B. (2018). Complexity as a Discourse on School Mathematics Reform. In Transdisciplinarity in Mathematics Education (pp. 75-88). Springer, Cham.[PDF]
  • Delcea, C., Cotfas, L. A., Chiriță, N., & Nica, I. (2018). A two-door airplane boarding approach when using apron buses. Sustainability, 10(10), 3619.
  • Delcea, C., Cotfas, L. A., & Paun, R. (2018). Agent-based evaluation of the airplane boarding strategies’ efficiency and sustainability. Sustainability, 10(6), 1879.
  • Delcea, C., Cotfas, L. A., & Paun, R. (2018, September). Agent-based optimization of the emergency exits and desks placement in classrooms. In International Conference on Computational Collective Intelligence (pp. 340-348). Springer, Cham.
  • Delcea, C., Cotfas, L. A., & Paun, R. (2018, September). Airplane boarding strategies using agent-based modeling and grey analysis. In International Conference on Computational Collective Intelligence (pp. 329-339). Springer, Cham.
  • Deshmukh, V. (2018). Modeling Human Migration Dynamics in Netlogo.
  • de Aguiar, P. V., de Sá, C. C., de Lima, H. G. G., Amaral, A. R., & Parpinelli, R. S. (2018, October). Application of the NetLogo Tool in Ischemic Stroke Simulation. In Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (pp. 596-607). SBC.
  • DeSoto, K. A. (2018). Modeling Evacuation of Population Centers Using NetLogo
  • Djerroud, H., & Cherif, A. A. (2018, November). Visualization tool for jade platform (jex). In Proceedings of the Future Technologies Conference (pp. 481-489). Springer, Cham.
  • Dobbie, S., Schreckenberg, K., Dyke, J. G., Schaafsma, M., & Balbi, S. (2018). Agent-based modelling to assess community food security and sustainable livelihoods. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3639
  • Dos Santos, H. D. R., Galafassi, C., de Souza, D. C., & Russini, A. (2018). MICROSSIMULAÇÃO DO TRAJETO DE UMA MÁQUINA AGRÍCOLA UTILIZANDO O SOFTWARE NETLOGO. Anais do Salão Internacional de Ensino, Pesquisa e Extensão, 9(3).
  • Dubovi, I., Dagan, E., Mazbar, O. S., Nassar, L., & Levy, S. T. (2018). Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study. Nurse education today, 61, 175-181.[PDF]
  • Du, x., Chen, Y., Bouferguene, A., Al-Hussein, M.(2018). Multi-Agent based simulation of elderly egress process and fall accident in senior partment buildings. 2018 Winter Simulation Conference [PDF]
  • Chareunsy, A. K. (2018). Diffusion of development initiatives in a southern Lao community: An agent based evaluation. Journal of Asian Economics, 54, 53-68.[PDF]
  • Cucart-Mora, C., Lozano, S., & de Pablo, J. F. L. (2018). Bio-cultural interactions and demography during the Middle to Upper Palaeolithic transition in Iberia: An agent-based modelling approach. Journal of Archaeological Science, 89, 14-24.[PDF]
  • Di Pietrantonio, J. (2018). A Computational Model of Team-Based Dynamics in the Workplace: Assessing the Impact of Incentive-Based Motivation on Productivity (Doctoral dissertation, Duquesne University).[PDF]
  • Dorsey, J. W., & Hardy, L. C. (2018). Sustainability factors in dynamical systems modeling: Simulating the non-linear aspects of multiple equilibria. Ecological Modelling, 368, 69-77.[PDF]
  • Drezewski, R. (2018). The Agent-Based Model and Simulation of Sexual Selection and Pair Formation Mechanisms. Entropy 20(5), p. 342. [PDF]
  • Easter, C. (2018). An agent-based simulation for studying the effect of various parameters on the evolution of teaching.
  • Edali, M. & Yücel, G. (2018). Automated analysis of regularities between model parameters and output using support vector regression in conjunction with decision trees. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3876
  • El Hachami, K., & Tkiouat, M. (2018, April). An approach for modeling the economy as a complex system using agent-based theory. In Intelligent Systems and Computer Vision (ISCV), 2018 International Conference on (pp. 1-6). IEEE.[PDF]
  • Ene, N., Fernandez, M., Pinaud, B. (2018). A Graph Transformation Approach to the Modelling of Capital Markets. [PDF]
  • Enokela, J. A. (2018). An Analysis of the Consequences of the Herdsmen-Farmers Crisis in North Central Nigeria using Agent–Based Modelling. The Pacific Journal of Science and Technology, 19(2), 326-336.
  • Flores-Parra, J. M., Castañón-Puga, M., Gaxiola-Pacheco, C., Palafox-Maestre, L. E., Rosales, R., & Tirado-Ramos, A. (2018). A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo. In Computer Science and Engineering—Theory and Applications (pp. 127-149). Springer, Cham.
  • Furtado, B. (2018). Policy space: agent based modeling. Rio de Janerio: Ipea.[PDF]
  • Feldman, T. (2018). Unwinding ZIRP: A simulation analysis. Finance Research Letters, 24, 278-288.[PDF]
  • Galán, S. F., & Mengshoel, O. J. (2018). Neighborhood beautification: Graph layout through message passing. Journal of Visual Languages & Computing, 44, 72-88.[PDF]
  • Gao, X., Li, K., & Chen, B. (2018). Invulnerability Measure of a Military Heterogeneous Network Based on Network Structure Entropy. IEEE Access, 6, 6700-6708.[PDF]
  • García-Magariño, I., Gray, G., Lacuesta, R., & Lloret, J. (2018). Survivability strategies for emerging wireless networks with data mining techniques: a case study with NetLogo and RapidMiner.IEEE Access.[PDF]
  • Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., Narasimhan, K. P., & Wilkinson, H. (2018). Computational modelling of public policy: Reflections on practice. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3669
  • Ginovart, M. (2018). How a multi-agent programmable modelling environment like NetLogo can help to deal with communities or assemblages of bacteria on surfaces?. Exploring Microorganisms: Recent Advances in Applied Microbiology, 256.
  • Gómez-Cruz, N. A., Loaiza Saa, I., & Ortega Hurtado, F. F. (2018). Agent-based simulation in management and organizational studies: a survey. European Journal of Management and Business Economics, 26(3), 313-328.[PDF]
  • Gonzales, G. V., dos Santos, E. D., Adamatti, D. F., & Neto, A. J. S. (2018). A Netlogo and Matlab Hybrid Approach for Constructal Design of the Double-T Shaped Cavity by Means of Simulated Annealing. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, 6(1).
  • GOROSHNIKOVA, T., & SMAKHTIN, E. (2018). EMOTIONS IN DECISION-MAKING WITHIN SIMULATION MODELING. In System analysis in economics-2018 (pp. 135-138).
  • Greco, A., & Pluchino, A. (2018). On the use of immune algorithms to determine seismic collapse conditions for frame structures. Computer Software and Media Applications.
  • Gunaratne, C., & Garibay, I. (2018). NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces. arXiv preprint arXiv:1808.03292.
  • Gündel, M., Hoyt, C. T., & Hofmann-Apitius, M. (2018). BEL2ABM: agent-based simulation of static models in Biological Expression Language. Bioinformatics, 34(13), 2316-2318.
  • Guo, Y. and Wilensky (2018a). NetLogo MTG 1 Equal Opportunities HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Guo, Y. and Wilensky (2018b). NetLogo MTG 2 Random Assignment HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Guo, Y. and Wilensky (2018c). NetLogo MTG 3 Feedback Loop HubNet Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.[PDF]
  • Haddad, T. A. (2018). An IoT-Based Adaptive Traffic Light Control Algorithm for Isolated Intersection. In Advances in Computing Systems and Applications: Proceedings of the 4th Conference on Computing Systems and Applications (p. 107). Springer Nature.
  • Haeme, J., McCaw, K., Nguyen, T., & May, T. (2018). Project Whirligig: Modeling the Swarming Behavior of Whirligig Beetles.
  • Han, S., Huang, H., Luo, Z., & Foropon, C. (2018). Harnessing the power of crowdsourcing and Internet of Things in disaster response. Annals of Operations Research, 1-16.[PDF]
  • Haydari, S. (2018). Copyright Law and Knowledge Creation: A Study of Copyright Term Length Impact on Knowledge Creation and Learning. Northeastern University, ProQuest Dissertations Publishing[PDF]
  • Heath, K. N., Ryder, E. F., & Gegear, R. J. (2018). Investigating the effect of memory loss on pollinator-plant interactions through agent-based modeling. Gordon Research Conference: Unifying Ecology Across Scales, Biddeford, ME, United States.
  • Hébert, G. A., Perez, L., & Harati, S. (2018). An Agent-Based Model to Identify Migration Pathways of Refugees: The Case of Syria. In Agent-Based Models and Complexity Science in the Age of Geospatial Big Data (pp. 45-58). Springer, Cham.[PDF]
  • Head, B., & Wilensky, U. (2018). Agent cognition through micro-simulations: Adaptive and tunable intelligence with NetLogo LevelSpace. In: Morales A., Gershenson C., Braha D., Minai A., Bar-Yam Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham.[PDF]
  • Hokamp, S., Gulyas, L., Koehler, M., & Wijesinghe, S. (2018). Agent-Based Modeling of Tax Evasion. Wiley.
  • Hollar, D. W. (2018). Simulations, Applications, and the Challenge for Public Health. In Trajectory Analysis in Health Care (pp. 231-246). Springer, Cham.
  • Huang, W. (2018). Exploring households’ weatherization adoptions: an agent-based approach (Doctoral dissertation, Iowa State University).[PDF]
  • Huber, L., Bahro, N., Leitinger, G., Tappeiner, U., & Strasser, U. (2018, April). Aqua. MORE: Socio-hydrological Modelling of Water Resources in an Alpine Catchment. In EGU General Assembly Conference Abstracts (p. 6426).
  • Humann, J., Jin, Y., & Madni, A. M. (2018). Scalability in Self-Organizing Systems: An Experimental Case Study on Foraging Systems. In Disciplinary Convergence in Systems Engineering Research (pp. 543-557). Springer, Cham.[PDF]
  • Hunter, E., Mac Namee, B., & Kelleher, J. D. (2018). Using a socioeconomic segregation burn-in model to initialise an agent-based model for infectious diseases. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3870
  • Izquierdo, S. S. & Izquierdo, L. R. (2018). Mamdani fuzzy systems for modelling and simulation: A critical assessment. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3660
  • Jaxa-Rozen, M., & Kwakkel, J. H. (2018). PyNetLogo: Linking NetLogo with Python. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3668
  • Jianyu, Z., Baizhou, L., Xi, X., Guangdong, W., & Tienan, W. (2018). Research on the characteristics of evolution in knowledge flow networks of strategic alliance under different resource allocation. Expert Systems with Applications, 98, 242-256.[PDF]
  • JIN, Z., & ZOU, H. (2018). Research on joint distribution based on Web semantics and Agent technology. Modern Electronics Technique, 2018, 14.
  • Jing, L., Chen, B., Zhang, B., & Ye, X. (2018). Modeling marine oily wastewater treatment by a probabilistic agent-based approach. Marine pollution bulletin, 127, 217-224.[PDF]
  • Karanci, A., Velásquez-Montoya, L., Paniagua-Arroyave, J. F., Adams, P. N., & Overton, M. F. (2018). Beach Management Practices and Occupation Dynamics: An Agent-Based Modeling Study for the Coastal Town of Nags Head, NC, USA. In Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 373-395). Springer, Cham.[PDF]
  • Kaziyeva, D., Wallentin, G., Loidl, M., Mohr, S., & Neuwirth, C. (2018). Reviewing software for agent-based bicycle flow models. GI_Forum, 6, 291-296.
  • Klein, I., Levy, N., & Ben-Elia, E. (2018). An agent-based model of the emergence of cooperation and a fair and stable system optimum using ATIS on a simple road network. Transportation research part C: emerging technologies, 86, 183-201.[PDF]
  • Koch, A. (2018). Dynamic Relationships Between Human Decision Making and Socio-natural Systems. In Trends in Spatial Analysis and Modelling (pp. 121-141). Springer, Cham.[PDF]
  • Köhler, J., de Haan, F., Holtz, G., Kubeczko, K., Moallemi, E., Papachristos, G., & Chappin, E. (2018). Modelling sustainability transitions: An assessment of approaches and challenges. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3629
  • Koli, V.N., Mirza, F., Baig, M.M., & Ullah, E.(2018). An Agent-Based Modelling Approach for Scheduling and Management of Elective Surgeries. SM Journal of Health and Medical Informatics[PDF]
  • Kotnik, K. (2018). Exploring Marine Population Dynamics with Agent Based Models.
  • Kurahashi, S. (2018, June). Agent-Based Gaming Approach for Electricity Markets. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 311-320). Springer, Cham.[HTML]
  • Laatabi, A., Marilleau, N., Nguyen-Huu, T., Hbid, H., & Ait Babram, M. (2018). ODD+2D: An ODD based protocol for mapping data to empirical ABMs. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3646
  • Lawall, M. L., & Graham, S. (2018). NETLOGO SIMULATIONS AND THE USE OF TRANSPORT AMPHORAS IN ANTIQUITY. Maritime Networks in the Ancient Mediterranean World, 163.
  • Lawlor, F., Collier, R., Nallur, V. (2018).Towards a Programmable Framework for Agent Game Playing. Adaptive Learning Agents Workshop.[HTML]
  • Lawson, T., Rogerson, R., & Barnacle, M. (2018). A comparison between the cost effectiveness of CCTV and improved street lighting as a means of crime reduction. Computers, Environment and Urban Systems, 68, 17-25.
  • Lee, T. E. (2018). The thin blue line between protesters and their counter-protesters. Journal of Artificial Societies and Social Simulation, 21(2). doi.org/10.18564/jasss.3676
  • Lemos, C. M. (2018). Model Exploration and Computer Experiments. In Agent-Based Modeling of Social Conflict (pp. 65-111). Springer, Cham.[HTML]
  • Lemos, C. M. (2018). ABM of Civil Violence: ODD Description. In Agent-Based Modeling of Social Conflict (pp. 51-63). Springer, Cham.[HTML]
  • Liew, C. W., Phuong, T., Jones, C. B., Evans, S., Hoot, J., Weedling, K., ... & Kurt, R. A. (2018). A computational approach to unraveling TLR signaling in murine mammary carcinoma. Computers in biology and medicine, 93, 56-65.[PDF]
  • Liukkonen, L., Ayllón, D., Kunnasranta, M., Niemi, M., Nabe-Nielsen, J., Grimm, V., & Nyman, A. M. (2018). Modelling movements of Saimaa ringed seals using an individual-based approach. Ecological Modelling, 368, 321-335.[PDF]
  • Lopez-Parez, A., Ruiz-Martin, C., Wainer, G. (2018). Formal Abstract Modeling of Dynamic Multiplex Networks. Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pp.61-72. Rome, Italy.[PDF]
  • Luna-Ramirez, W. A., & Fasli, M. (2018). Bridging the gap between abm and mas: A disaster-rescue simulation using jason and netlogo. Computers, 7(2), 24.
  • Maciel, M.V. (2018). Emergência de distribuições de posicionamentos ideológicos: uma abordagem computacional. Diss. Universidade de São Paulo.
  • Mailleret, L., Davtian, D., & Grognard, F. (2018). An individual based model to optimize natural enemies deployment in augmentative biological control.
  • Makarov, S., & Belianov, A. (2018). Reasons to create a Python framework for agent-based simulation models development. Herald of CEMI, (1).
  • Malishev, M., Bull, C. M., & Kearney, M. R. (2018). An individual‐based model of ectotherm movement integrating metabolic and microclimatic constraints. Methods in Ecology and Evolution, 9(3), 472-489.[PDF]
  • Mao, W., Wu, H., Pan, L., & Zhou, J. (2018, September). Multi-layer Simulation Methods of Temperature Controlled Loads. In 2018 China International Conference on Electricity Distribution (CICED) (pp. 2002-2006). IEEE.
  • Martin, K. (2018, October). Multitouch NetLogo for museum interactive game. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 5-8).
  • Mayangsari, L., & Pasaribu, N. (2018). Understanding Unlawful Behavior in Music Industry Using Agent-Based Modelling and Simulation: A Complementary of Misconception in Value Co-Creation. World Academy of Science, Engineering and Technology, International Journal of Industrial and Manufacturing Engineering, 5(4).[HTML]
  • Menárguez, F. J. M., Hortal, J. C., Salazar, S. S., & Alarcón, A. A. (2018). Anatomizing NetLogo. Some advices on how to consider a programmable environment for designing inhabited landscapes. In EURAU18 Alicante: Retroactive Research: Congress Proceedings (pp. 423-428). Universitat d´ Alacant/Universidad de Alicante.
  • Modu, B., Polovina, N., Lan, Y., & Konur, S. (2018). Machine learning analysis and agent-based modelling of malaria transmission. In Fuzzy Systems and Data Mining IV (pp. 465-472). IOS Press.
  • Moglia, M., Podkalicka, A., & McGregor, J. (2018). An agent-based model of residential energy efficiency adoption. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3729
  • Muelder, H. & Filatova, T. (2018). One theory - many formalizations: Testing different code implementations of the theory of planned behavior in energy agent-based models. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3855
  • Mueller, C., Klein, U., & Hof, A. (2018). An easy-to-use spatial simulation for urban planning in smaller municipalities. Computers, Environment and Urban Systems.[PDF]
  • Muhammad, A., Kashif, Z., & Saini, D. (2018). Agent-based Simulation of Socially-inspired Model of Resistance against Unpopular Norms Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) - Volume 1, pages 133-139.[PDF]
  • Nabe-Nielsen, J., van Beest, FM., Grimm, V., Sibly, PM.(2018). TRACE document: Disturbances and marine populations.[PDF]
  • Pei, C., Weintrop, D., & Wilensky, W. (in press). Cultivating computational thinking practices and mathematical habits of mind in Lattice Land. Mathematical Thinking and Learning.
  • Nie, J., Hu, H., Wang, X., & Xu, C. (2018). Simulation Study on Network Stability of Short Cycle Product Supply Chain Based on Netlogo. In CICTP 2018: Intelligence, Connectivity, and Mobility (pp. 409-418). Reston, VA: American Society of Civil Engineers.
  • Olugboji, O., Camorlinga, S. G., de Faria, R. L., & Kaushal, A. (2018). Understanding the Emergency Department Ecosystem Using Agent-Based Modeling: A Study of the Seven Oaks General Hospital Emergency Department. In Putting Systems and Complexity Sciences Into Practice (pp. 199-214). Springer, Cham.
  • Opiyo, N. (2018). Impacts of socio-economic policies on temporal diffusion of PV-based communal grids in a rural developing community. In Proceedings of the 35th EU PVSEC 2018 (pp. 2182-2187).
  • Opiyo, N. (2018). Modelling different PV-based communal grids architectures for rural developing communities. In Proceedings of the 35th EU PVSEC 2018 (pp. 1859-1864).
  • Opiyo, N. (2018, September). How Subsidies Impact on Temporal Diffusion of PV-Based Minigrids. In 35th European Photovoltaic Solar Energy Conference.
  • Opiyo, N. (2018, September). Modelling Different PV-Based Minigrids Architectures. In 35th European Photovoltaic Solar Energy Conference.
  • Ornelas, N. O. An Ecosystem: Computational Thinking, Project-Based Learning [PDF]Logo
  • Paris, T., Ciarletta, L., & Chevrier, V. (2018, October). Co-simulation à base d'outils multi-agents: un cas d'étude avec NetLogo.
  • Pele, M., Deneubourg, J. L., & Sueur, C. (2018). Decision-making processes underlying pedestrian behaviours at signalised crossings: Part 2. Do pedestrians show cultural herding behaviour?. arXiv preprint arXiv:1805.11834.[PDF]
  • Peng, Y., Li, Q. X., & Bao, H. J. (2018). Conflict Analysis of Concentrated Rural Settlement Development During Post-disaster Reconstruction in China: A Multi-agent Simulation. In Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate (pp. 491-502). Springer, Singapore.[PDF]
  • Petrosino, A., Sherard, M., Harron, J., & Kohl, M. (2018, March). Using Collaborative Agent-based Modeling to Explore Complex Phenomena in Pre-and In-service Teacher Education. In Society for Information Technology & Teacher Education International Conference (pp. 1669-1671). Association for the Advancement of Computing in Education (AACE).
  • Polater, A. (2018). Managing airports in non-aviation related disasters: A systematic literature review. International Journal of Disaster Risk Reduction.[PDF]
  • PONZIANI, F., TINABURRI, A., & RICCI, V. (2018). A Multi Agent Approach To Analyse Shift In People Behaviour Under Critical Conditions. International Journal of Safety and Security Engineering, 8(1), 1-9.[PDF]
  • Ponziani, F. A., Tinaburri, A., & Ricci, V. (2018). A multi agent approach to analyse shift in people behavior under critical conditions. International Journal of Safety and Security Engineering, 8(1), 1-9.
  • Pour, F. S. A., Tatar, U., & Gheorghe, A. (2018, April). Agent-based model of sand supply governance employing blockchain technology. In Proceedings of the Annual Simulation Symposium (p. 14). Society for Computer Simulation International.[PDF]
  • Proctor, C., Blikstein, P.(2018). Unfold.studio: Supporting critical literacies of text and code Stanford Graduate School of Education, (p. 1-35).[PDF]
  • Proietti, C. & Franco, A. Social norms and the dominance of low-doers. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3524
  • Quan-en, M. A., & Juan, Z. H. A. N. G. (2018). Research on Propagation Principle of Wechat Official Account in Complex Network by Modeling Based on SIR and Simulation. Information Science.
  • Raglin, A., Metu, S., & Howard, C. (2018, April). Understanding theoretical human information interaction, the development of a standard model using an agent based modeling framework. In Next-Generation Analyst VI (Vol. 10653, p. 1065302). International Society for Optics and Photonics.
  • Raimbault, J. An Urban Morphogenesis Model Capturing Interactions between Networks and Territories.[PDF]
  • Rajib, Md. Design Considerations for Intermittently Connected Energy Harvesting Wireless Sensor Networks. ProQuest.[PDF]
  • Ramírez-Ávila, G. M., Kurths, J., & Deneubourg, J. L. (2018). Fireflies: a paradigm in synchronization. In Chaotic, Fractional, and Complex Dynamics: New Insights and Perspectives (pp. 35-64). Springer, Cham.[PDF]
  • Rashid, K. I., Nan, D., Tahir, M., & Ahmed, A. An Adaptive Cruise Control Model based on PDLCA for Efficient Lane Selection and Collision Avoidance. International Journal of Advanced Computer Science and Applications (9).[PDF]
  • Raya, K., Gaxiola, C. G., & Castanon, M. (2018). Agent-based model for self management of network flows using negotiation. IEEE Latin America Transactions, 16(1), 210-215.
  • Reinhardt, O., Hilton, J., Warnke, T., Bijak, J., & Uhrmacher, A. M. (2018). Streamlining simulation experiments with agent-based models in demography. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3784
  • Rosales, C., Whipple, J. M., & Blackhurst, J. (2018). The Impact of Out-of-Stocks and Supain Design on Manufacturers: Insights from an Agent-Based Model. Transportation Journal, 57(2), 137-162.[PDF]
  • Rossetti, G., Milli, L., Rinzivillo, S., Sîrbu, A., Pedreschi, D., & Giannotti, F. (2018). NDlib: a python library to model and analyze diffusion processes over complex networks. International Journal of Data Science and Analytics, 5(1), 61-79.[PDF]
  • Salman, M. A., & Al Essa, H. A. (2018). A Distributed Approach for Disk Defragmentation. Journal of University of Babylon for Pure and Applied Sciences, 26(3), 1-5.
  • Santos, F., Nunes, I., & Bazzan, A. L. (2018). Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain. Simulation Modelling Practice and Theory, 83, 162-187.[PDF]
  • Saputra, G. W., Irawan, B., & Kusuma, P. D. (2018). Pemodelan Dan Simulasi Penyebaran Penyakit Tuberkulosis Berbasis Sistem Agen. eProceedings of Engineering, 5(3).
  • Schools, S. (2018). A Genetic Algorithm for Sorting Lists Implemented in the Programming Language Net Logo (Doctoral dissertation, Morgan State University)
  • Secchi, D. & Cowley, S. J. (2018). Modeling organizational cognition: The case of impact factor. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3628
  • SHEN, Y. W., & LU, Z. J. (2018). Research on the Evolution of Entrepreneurial Ecosystem Based on Innovation Network. In Economic Forum.
  • Simpkins, C. E., Dennis, T. E., Etherington, T. R., & Perry, G. L. (2018). Assessing the performance of common landscape connectivity metrics using a virtual ecologist approach. Ecological Modelling, 367, 13-23.[PDF]
  • Snitker, G. (2018). Identifying natural and anthropogenic drivers of prehistoric fire regimes through simulated charcoal records. Journal of Archaeological Science, 95, 1-15.[PDF]
  • Soheilypour, M., & Mofrad, M. R. (2018). Agent‐based modeling in molecular systems biology. BioEssays, 40(7), 1800020.
  • Stamatovic, B. (2018, February). Implementation of CA algorithm for labeling of 26-connected components in 3D binary lattices. In 2018 23rd International Scientific-Professional Conference on Information Technology (IT) (pp. 1-3). IEEE.
  • Sturley, C., Newing, A., & Heppenstall, A. (2018). Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours. The Ionternatinal Review of Retail, Distribution and Consumer Research, 28(1), 27-46.[HTML]
  • Sukarno, S. A., Atitallah, R. B., & Djemai, M. (2018, October). Approximation Algorithm for 3-Dimensional Vehicle Routing Problem for Fleet of Multi-Agents. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT) (pp. 1-6). IEEE
  • Sullivan, A., An, L., & York, A. (2018). Which perspective of institutional change best fits empirical data? An agent-based model comparison of rational choice and cultural diffusion in invasive plant management. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3611
  • Suyamto, D., Prasetyo, L., & Setiawan, Y. (2018, August). A voxel-based model of LiDAR point cloud for estimating forest canopy closure. In Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018) (Vol. 10773, p. 107730Q). International Society for Optics and Photonics.
  • Syuhada, K. E. Pengaruh Formasi Penyimpanan Metode Volume Based Terhadap Unjuk Kerja Strategi Routing Order Picking Menggunakan Strategi Midpoint dan Largesgap. Jurnal TIN Universitas Tanjungpura, 2(2).
  • Taherian, M., Mousavi, S. M., & Chamani, H. (2018). An agent-based simulation with NetLogo platform to evaluate forward osmosis process (PRO Mode). Chinese journal of chemical engineering, 26(12), 2487-2494.
  • Thakur, S., Bhautik, P., Sangore, V., Singh, K. (2018).A Review on Treatment of Sewage Water & Biogas Purification by Algae. Internatinal Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 1, Page No pp.639-642, January 2018, [PDF]
  • Thiriot, S. (2018). Word-of-mouth dynamics with information seeking: Information is not (only) epidemics. Physica A: Statistical Mechanics and its Applications, 492, 418-430.[PDF]
  • Trivedi, A., & Pandey, M. (2018). Agent-based modelling and simulation of religious crowd gatherings in India. In Advanced Computational and Communication Paradigms (pp. 465-472). Springer, Singapore.
  • Tufféry, C., Fernandes, P., Delvigne, V., & Morala, A. (2018). Combinaison d’un SMA et d’un SIG pour aider à la prospection pétroarchéologique. Exploration d’une approche multi-agents dans la modélisation des parcours naturels du silex. Archéologies numériques, 2(1).
  • Tyson, M. (2018). Managing Distributed Information: Implications for Energy Infrastructure Co-production (Doctoral dissertation, Arizona State University).[PDF]
  • Vanhée, L. & Dignum, F. (2018). Explaining the emerging influence of culture, from individual influences to collective phenomena. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3881
  • Vidaković, M., Ivanović, M., Stantić, D., & Vidaković, J. (2018, June). How Research Achievements Can Influence Delivering of a Course-Siebog Agent Middleware. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 110-120). Springer, Cham.[HTML]
  • Voinov, A., Jenni, K., Gray, S., Kolagani, N., Glynn, P. D., Bommel, P., ... & Smajgl, A. (2018). Tools and methods in participatory modeling: Selecting the right tool for the job. Environmental Modelling & Software, 109, 232-255.
  • Wagh, A., & Wilensky, U. (2018). EvoBuild: A quickstart toolkit for programming agent-based models of evolutionary processes. Journal of Science Education and Technology, 27(2), 131-146.
  • Waight, N., & Abd-El-Khalick, F. (2018). Technology, Culture, and Values: Implications for Enactment of Technological Tools in Precollege Science Classrooms. In Cognition, Metacognition, and Culture in STEM Education (pp. 139-165). Springer, Cham.[PDF]
  • Walbert, H. J., Caton, J. L., & Norgaard, J. R. (2018). Countries as agents in a global-scale computational model. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3717
  • Wang, X. M., He, C. C., & Li, X. K. (2018). Interaction Optimization Among Multi-agent of Green Dwelling Market. Journal of Civil Engineering and Management, 35(2), 1-7.
  • Wang, Y., Wen, S., Farnon Ellwood, M. D., Miller, A. D., & Chu, C. (2018). Temporal effects of disturbance on community composition in simulated stage‐structured plant communities. Ecology and evolution, 8(1), 120-127.[PDF]
  • Wang, Z., Zhang, H., Hu, M., Qiu, Q., & Liu, H. (2018). Analysis of safety characteristics of flight situation in complex low-altitude airspace. Advances in Mechanical Engineering, 10(5), 1687814018774656.[PDF]
  • Wilkerson, M. H., Shareff, R., Laina, V., & Gravel, B. (2018). Epistemic gameplay and discovery in computational model-based inquiry activities. Instructional Science, 1-26.[PDF]
  • Weintrop, D., & Wilensky, U. (2018). How block-based, text-based, and hybrid block/text modalities shape novice programming practices. International Journal of Child-Computer Interaction.[PDF]
  • Weisbuch, G. (2018). Lattice dynamics of inequality. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3635
  • West, T. A., Grogan, K. A., Swisher, M. E., Caviglia-Harris, J. L., Sills, E., Harris, D., ... & Putz, F. E. (2018). A hybrid optimization-agent-based model of REDD+ payments to households on an old deforestation frontier in the Brazilian Amazon. Environmental Modelling & Software, 100, 159-174.[PDF]
  • Yarbrough, B., & Wagner, N. (2018, April). Assessing security risk for wireless sensor networks under cyber attack. In Proceedings of the Annual Simulation Symposium (p. 1). Society for Computer Simulation International.[PDF]
  • Xu, X., Sahnoun, M., Abdelaziz, F., Baudry, D., Louis, A.(2018). Multi-objective Flexible Job Shop Scheduling Problem: Simulation Approach [PDF]
  • YACHOU, N., & ABOULAICH, R. (2018). Agent Based Modeling and Simulation for Home Financing. Application in Netlogo Platform. Journal of Applied Economic Sciences, 13(3).
  • Yahyaoui, F., & Tkiouat, M. (2018). Agent-based co-modeling of information society and wealth distribution. International Journal of Advanced Computer Science and Applications, 9(11), 201-206.
  • Yan, J., Liu, R., & Zhang, G. (2018). Task structure, individual bounded rationality and crowdsourcing performance: An agent-based simulation approach. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3854
  • Yaşar, O. (2018). A new perspective on computational thinking. Communications of the ACM, 61(7), 33-39.
  • YIN, G., LI, G., ZHU, T., & CHEN, J. (2018). Study on Multi-Agent System of Energy Managements for 4WD Electric Vehicles. China Mechanical Engineering, 29(15), 1765.
  • Yousefi, M., Yousefi, M., Ferreira, R. P. M., Kim, J. H., & Fogliatto, F. S. (2018). Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artificial intelligence in medicine.[PDF]
  • Yoo, E. (2018). Dynamics of Information Distribution on Social Media Platforms during Disasters (Doctoral dissertation, Arizona State University).[PDF]
  • Yoon, S. A., Goh, S. E., & Park, M. (2018). Teaching and Learning About Complex Systems in K–12 Science Education: A Review of Empirical Studies 1995–2015. Review of Educational Research, 88(2), 285-325.[PDF]
  • Yunming, W., Si, C., Chengsheng, P., & Bo, C. (2018). Measure of invulnerability for command and control network based on mission link. Information Sciences, 426, 148-159.[PDF]
  • Zeppini, P. & Frenken, K. (2018). Networks, percolation, and consumer demand. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3658
  • Zhang, X., He, Junhui.(2018). Nature-Inspired Computational Model of Population Desegregation Under Group Leaders Influence. Proceedings of the Fifth International Forum on Decision Sciences.[PDF]
  • Zhao, C., Li, S., Wang, W., Li, X., & Du, Y. (2018). Advanced parking space management strategy design: an agent-based simulation optimization approach. Transportation Research Record, 2672(8), 901-910.
  • ZHENG, Y., ZHANG, G., MA, R., & SHU, H. (2018). Research on Influence of Mobile Social Network on College Students' Campus Life: from Perspective of Multi-Agent Evolution Simulation. China Educational Technology & Equipment, 04.
  • ZHOU, X. N., WANG, J. S., & Yin, Z. H. U. (2018). Simulation Research of Vehicle Lane-changing Behavior Evolutionary Game Model Based on NetLogo. DEStech Transactions on Engineering and Technology Research, (ecar).
  • Zhu, M., Panorkou, N., Lal, P., Etikyala, S., Germia, E., Iranah, P., ... & Basu, D. (2018, March). Integrating interactive computer simulations into K-12 earth and environmental science. In 2018 IEEE Integrated STEM Education Conference (ISEC) (pp. 220-223). IEEE.
  • Zia, K., Saini, D. K., Muhammad, A., & Ferscha, A. (2018). Customer Participation in the Internet of Things: A Bayesian Game Model. IEEE Transactions on Computational Social Systems.[PDF]
  • Zinelli Jr, M. (2018, October). Multi-agent Simulation of a Real Evacuation Scenario: Kiss Nightclub and the Panic Factor. In Multi-Agent Systems and Agreement Technologies: 15th European Conference, EUMAS 2017, and 5th International Conference, AT 2017, Evry, France, December 14-15, 2017, Revised Selected Papers (Vol. 10767, p. 268). Springer.
  • Zoto, E., Kowalski, S., Lopez-Rojas, E. A., & Kianpour, M. Using a socio-technical systems approach to design and support systems thinking in cyber security education.[PDF]

2017

  • Abbott, R., & Hadžikadić, M. (2017).Complex Adaptive Systems, Systems Thinking, and Agent-Based Modeling. In Advanced Technologies, Systems, and Applications (pp. 1-8). Springer International Publishing. [PDF]
  • ACOSTA, C., BORGESIUS, F., & VAN HATTUM, J. E. S. S. I. E. Facilitating Collective Action for an Integrated Community Energy System.[PDF]
  • Alzaeemi, S. A. S., Sathasivam, S., & Adebayo, S. A. (2017). Analysis of Performance of Various Activation Functions for doing the logic programming in Hopfield Network. International Journal of Computational Bioinformatics and In Silico Modeling, 6(2), 911-921. [ PDF]
  • Alzahrani, E., Richmond, P., & Simons, A. J. (2017, August). A formula-driven scalable benchmark model for ABM, applied to FLAME GPU. In European Conference on Parallel Processing (pp. 703-714). Springer, Cham.[ PDF]
  • Ampatzidis, G., & Ergazaki, M. (2017). Toward an “Anti-Balance of Nature” Learning Environment for Non-Biology Major Students: Learning Objectives and Design Criteria. Natural Sciences Education, 46(1).[HTML]
  • Anderson, J. H., Downs, J. A., Loraamm, R., & Reader, S. (2017). Agent-based simulation of Muscovy duck movements using observed habitat transition and distance frequencies. Computers, Environment and Urban Systems, 61, 49-55. [PDF]
  • Aydin, M. E., & Fellows, R. (2017). A reinforcement learning algorithm for building collaboration in multi-agent systems. arXiv preprint arXiv:1711.10574.[PDF]
  • Auerbach, S., & Dix, R. (2017). Competition and spatial efficiency.[PDF]
  • Badham, J., Jansen, C., Shardlow, N., & French, T. (2017). Calibrating with multiple criteria: A demonstration of dominance. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3212
  • Balev, S., Dutot, A., & Olivier, D. (2017). Networking, Networks and Dynamic Graphs. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 85-116).[HTML]
  • Ballet, P., Rivière, J., Pothet, A., Theron, M., Pichavant, K., Abautret, F., ... & Rodin, V. (2017).Modelling and Simulating Complex Systems in Biology: Introducing NetBioDyn–A Pedagogical and Intuitive Agent-Based Software. In Multi-Agent-Based Simulations Applied to Biological and Environmental Systems (pp. 128-158). IGI Global. [ HTML]
  • Banos, A., Corson, N., Daudé, É., Gaudou, B., & Coyrehourcq, S. R. (2017). Macro Models, Micro Models and Network-based Coupling. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 63-84).[ HTML]
  • Banati, H., Bhattacharyya, S., Mani, A., & Köppen, M. (Eds.). (2017). Hybrid Intelligence for Social Networks. Springer International Publishing.[PDF]
  • Barker, A. K., Alagoz, O., & Safdar, N. (2017). Interventions to reduce the incidence of hospital-onset Clostridium difficile infection: An agent-based modeling approach to evaluate clinical effectiveness in adult acute care hospitals. Clinical Infectious Diseases.[PDF]
  • Barrientos, A. H. (2017). The Evolutionary Dynamics of the Mixe Language. In Sociolinguistics-Interdisciplinary Perspectives. InTech.[PDF]
  • Barrientos, A. H., & Andrade, Y. D. (2017). Modelling and Simulation of Complex Adaptive System: The Diffusion of Socio-Environmental Innovation in the RENDRUS Network. In Cvetkovic, D (Ed.)Computer Simulation. InTech. [HTML]
  • Bastien-Olvera, B., Bautista-Gonzalez, E., & Gay-Garcia, C. An agent-based model of food-borne diseases under climate change scenarios in Mexico City.[PDF]
  • Belete, G.F., Voinov, A., Morales, J. (2017). Environmental Modelling & Software. Volume 94, (pp 112–126). [HTML]
  • Bent, J. (2017). Autonomous UAV Path Planning for Wildfire Data Collection. Computer Science Thesis. [PDF]
  • Beutler, J. D. Online Waiting Time Information.
  • Bezzout, H., Hsaini, S., Azzouzi, S., & El Faylali, H. (2017). Simulation of electromagnetic waves propagation in free space using Netlogo multi-agent approach. In Proceedings of the 2nd international Conference on Big Data, Cloud and Applications (p. 112). ACM.[PDF