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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 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., Lu, X., Du, Y., Rejtig, M., Bagley, R., Horn, M. S., & Wilensky, U. J. (In Review). Learning Computational 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.
  • Chen, J., Zhao, L., Li, Y., Xie, Z., Wilensky, U. J., & Horn, M. S. (In Review). “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.
  • 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.

2024

  • 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.
  • 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.
  • 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.
  • 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
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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)).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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, Y., Du, T., Zhang, Q., & Zhang, N. (2024). Analysis and Evaluation of Species Invasion. Advances in Engineering Technology Research, 9(1), 687-687.
  • 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.
  • Collard, P. (2024). Processionary Caterpillars at the Edge of Complexity. Artificial Life, 1-22.
  • 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.
  • 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.
  • 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).
  • Dehkordi, M. A. E. (2024). Simulating Dynamics of Institutions (Doctoral dissertation, Delft University of Technology).
  • 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.
  • 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.
  • 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.
  • Ehret, M., Johnston, W. J., & Ritter, T. (2024). From buying centers to buying ecosystems: Advancing the B2B research journey. Industrial Marketing Management.
  • Ekström, H., Droste, N., & Brady, M. (2024). Modelling forests as social-ecological systems: A systematic comparison of agent-based approaches. Environmental Modelling & Software, 105998.
  • 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.
  • Engel, A. (2024). Systems Science for Engineers and Scholars. John Wiley & Sons.
  • 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.
  • 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.
  • 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.
  • Fonseca, L. L., Böttcher, L., Mehrad, B., & Laubenbacher, R. C. (2024). Metamodeling and Control of Medical Digital Twins. arXiv preprint arXiv:2402.05750.
  • 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).
  • Gervasi, V., & Guberti, V. (2024). The Effect of Partial and Temporary Vaccination on African Swine Fever Eradication Rates. Transboundary and Emerging Diseases, 2024.
  • 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.
  • 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.
  • 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., Gonzalez, V. A., Zou, Y., Liu, J., & Cabrera-Guerrero, G. (2024). Application of an Agent-Based Post-Earthquake Evacuation Simulation to Enhance Early-Stage Design of Non-Structural and Architectural Layout. Available at SSRN 4743453.
  • Hahn, U., Merdes, C., & von Sydow, M. (2024). Knowledge through social networks: Accuracy, error, and polarisation. Plos one, 19(1), e0294815.
  • 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.
  • 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.
  • 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.
  • Imanian Ardabily, M., Noghani Dokht Bahmani, M., & Asgharpour Masouleh, A. R. (2024). science production. Ferdowsi University of Mashhad Journal of Social Sciences.
  • 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.
  • 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.
  • Kappenberger, J., & Stuckenschmidt, H. (2024). A framework for human-centered AI-based public policies. Human-Centered AI, 287.
  • 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.
  • Kooijman, S. A. L. M. (2024). Ways to reduce or avoid juvenile-driven cycles in individual-based population models. Ecological Modelling, 490, 110649.
  • 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.
  • Khazaei, S., & Najafiani, M. (2024). Evaluation of Emergency Evacuation in Residential High-Rise Buildings Communities (Case Study: Punak Town of Zanjan). Emergency Management.
  • 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.
  • 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.
  • 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.
  • 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.
  • Liang, L., Phan, H., & Giabbanelli, P. J. (2024). Experimental evaluation of a machine learning approach to improve the reproducibility of network simulations. SIMULATION, 00375497241229753.
  • Lin, J. H., Quan, Y. J., & Han, B. P. (2024). Metaibm: A Python-Based Library for Individual-Based Modelling in the Simulation of Eco-Evolutionary Dynamics in a Spatial-Explicit Metacommunity. Available at SSRN 4743316.
  • 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.
  • 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, 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Nurulloyev, F. (2024). МЕТОДИКА ОБУЧЕНИЯ ШКОЛЬНИКОВ СОВРЕМЕННЫХ ПРОГРАММНЫХ СРЕДСТВ. ЦЕНТР НАУЧНЫХ ПУБЛИКАЦИЙ, 45(45).
  • 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.
  • 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.
  • 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.
  • Rajasekaran, U., Malini, A., & Murugan, M. (2024). Artificial Intelligence in Autonomous Vehicles—A Survey of Trends and Challenges. Scrivener Publishing LLC.
  • 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).
  • 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.
  • 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.
  • 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.
  • Sapienza, A., & Falcone, R. (2024). Flood Risk and Preventive Choices: A Framework for Studying Human Behaviors. Behavioral Sciences, 14(1), 74.
  • 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.
  • 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.
  • 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.
  • 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.
  • Steinmann, P. (2024). Quantifying resilience under deep uncertainty (Doctoral dissertation, Wageningen University).
  • 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.
  • Thomas, A. (2024). Grasp planning techniques for harvesting in a novel greenhouse simulation environment (Doctoral dissertation, University of Guelph).
  • 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.
  • 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 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.
  • 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).
  • 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, 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.
  • 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, 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.
  • 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).
  • Yu, Y., Lu, Q., & Fu, Y. (2024). Dynamic Trust Management for the Edge Devices in Industrial Internet. IEEE Internet of Things Journal.
  • 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.
  • 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., 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.
  • Zhou, B. (2024). A Cautionary Note on the Application of GIS in Spatial Optimization Modeling. Journal of Geographic Information System, 16(01), 89-113.
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  • 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.

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.
  • 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).
  • 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.
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  • 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.
  • 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).
  • 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).
  • 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.
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2021

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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.
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  • 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.
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  • 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.
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  • 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.
  • 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.
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  • 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]
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  • 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).
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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]
  • 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).
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  • 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
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  • 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]
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  • 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]
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  • 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
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  • 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]
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  • 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]
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  • 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]
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  • 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.
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  • 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]
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  • Schulze, J., Müller, B., Groeneveld, J., & Grimm, V. (2017). Agent-based modelling of social-ecological systems: Achievements, challenges, and a way forward. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3423
  • Shevchuk, G. K., Zvereva, O. M., & Medvedev, M. A. (2017, November). Imbalance detection in a manufacturing system: An agent-based model usage. In AIP Conference Proceedings (Vol. 1906, No. 1, p. 070013). AIP Publishing.[HTML]
  • Shim, J., & Bliemel, M. (2017). Ignition of new product diffusion in entrepreneurship: An agent-based approach. Entrepreneurship Research Journal.[PDF]
  • Scotti, M., Hartvig, M., Winemiller, K. O., Li, Y., Jauker, F., Jordán, F., & Dormann, C. F. (2017). 15 Trait-Based and Process-Oriented Modeling in Ecological Network Dynamics. Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems, 228.[HTML]
  • Seri, R., & Secchi, D. (2017). How Many Times Should One Run a Computational Simulation? In Simulating Social Complexity (pp. 229-251). Springer, Cham.[ PDF]
  • Singleton, A. D., Spielman, S., & Folch, D. (2017). Urban analytics. Sage.[HTML]
  • SJOBERG, C., KARDUNI, A., BEORKREM, C., & ELLINGER, J. Animal: An Agent-Based Model of Circulation logic for Dynamo.[ PDF]
  • Sobkowicz, P. (2017). Utility, impact, fashion and lobbying: An agent-based model of the funding and epistemic landscape of research. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3399
  • Stefanelli, A. & Seidl, R. (2017). Opinion communication on contested topics: How empirics and arguments can improve social simulation. Journal of Artificial Societies and Social Simulation, 20(4). doi.org/10.18564/jasss.3492
  • Stöckelhuber, K.W., Wießner, S., Das, A., & Heinrich, G. (2017).Filler flocculation in polymers - a simplified model derived from thermodynamics and game theory. Soft Matters, 12(20). [PDF]
  • Suescún, C. G. P., Aragón, C. J. E., Gómez, M. A. J., & Moreno, R. J. (2017, September). Obstacle Evasion Algorithm for Clustering Tasks with Mobile Robot. In Workshop on Engineering Applications (pp. 84-95). Springer, Cham.[PDF]
  • Sun, X., Zhao, X., & Robaldo, L. (2017). Ali Baba and the thief, convention emergence in games. Journal of Artificial Societies and Social Simulation, 20(3). doi.org/10.18564/jasss.3421
  • Swanson, H., Anton, G., Bain, C., Horn, M., Wilensky, U.(2017).Computational Thinking in the Science Classroom. Proceedings of the 1st International Conference on Computational Thinking Education.[PDF]
  • Taherian, M., & Mousavi, S. M. (2017).Modeling and simulation of forward osmosis process using agent-based model system. Computers & Chemical Engineering, 100, 104-118. [PDF]
  • Thomas, D., Lin, S. (2017).Inquiry-Based Science and Mathematics Using Dynamic Modeling. SCIREA Journal of Mathematics, 2(2). [PDF]
  • Sanchez-Cartas, J. M., & Leon, G. (2017). A Game-Theory-Based Price-Optimization Algorithm for the Simulation of Markets Using Agent-Based Modelling. World Academy of Science, Engineering and Technology, International Journal of Economics and Management Engineering, 4(6).
  • Troitzsch, K. G. (2017). Axiomatic Theory and Simulation: A Philosophy of Science Perspective on Schelling's Segregation Model. Journal of Artificial Societies and Social Simulation, 20(1). [HTML]
  • Tubadji, A., Angelis, V., Nijkamp, P. (2017).Micro-Cultural Preferences and Macro-Percolaton of New Ideas: Netlogo Simulation. Journal of Knowledge Economy, 1-18. [ HTML]
  • van der Veen, R. A. C., Kisjes, K. H., & Nikolic, I. (2017). Exploring policy impacts for servicising in product-based markets: A generic agent-based model. [PDF] Journal of Cleaner Production.
  • van der Wal, C. N., Formolo, D., Robinson, M. A., Minkov, M., & Bosse, T. (2017). Simulating crowd evacuation with socio-cultural, cognitive, and emotional elements. In Transactions on Computational Collective Intelligence XXVII (pp. 139-177). Springer, Cham.[PDF]
  • Varughese, J. C., Thenius, R., Schmickl, T., & Wotawa, F. (2017). Quantification and analysis of the resilience of two swarm intelligent algorithms. C. Benzmuller, C. Lisetti, M. Theobald (eds.) GCAI, 148-161.[PDF]
  • Varughese, J. C., Thenius, R., Wotawa, F., & Schmickl, T. (2017). swarmfstaxis: Borrowing a swarm communication mechanism from fireflies and slime mold. In Proceedings of the Twenty First Annual Meeting on Agent Based Modeling and Simulation. Springer.[PDF]
  • Vasiljevska, J., Douw, J., Mengolini, A., & Nikolic, I. (2017). An agent-based model of electricity consumer: Smart metering policy implications in Europe. Journal of Artificial Societies and Social Simulation, 20(1). doi.org/10.18564/jasss.3150
  • Vila Guilera, J. (2017). Analysis and individual-based modelling of the tuberculosis epidemiology in Barcelona. The role of age, gender and origin (Bachelor's thesis, Universitat Politècnica de Catalunya).[PDF]
  • Velazquez, J. J., Su, E., Cahan, P., & Ebrahimkhani, M. R. (2017). Programming Morphogenesis through Systems and Synthetic Biology. Trends in biotechnology.[PDF]
  • Vermeer, W., Head, B., & Wilensky, U. (2017). “The Effects of Local Network Structure on Disease Spread in Coupled Networks.” In Complex Networks & Their Applications V, 487–98. Studies in Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_39.
  • Wagh, A., Cook‐Whitt, K., & Wilensky, U. (2017). Bridging inquiry‐based science and constructionism: Exploring the alignment between students tinkering with code of computational models and goals of inquiry. Journal of Research in Science Teaching. [PDF]
  • Wagh, A., & Wilensky, U. (2017). EvoBuild: A Quickstart Toolkit for Programming Agent-Based Models of Evolutionary Processes. Journal of Science Education and Technology, 1-16.[PDF]
  • Wallentin, G., & Neuwirth, C. (2017).Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model. Ecological Modelling, 345, 165-175. [PDF]
  • Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education (TOCE), 18(1), 3.[PDF]
  • Weiss, M. B., Krishnamurthy, P., & Gomez, M. M. (2017, March). How can polycentric governance of spectrum work? In Dynamic Spectrum Access Networks (DySPAN), 2017 IEEE International Symposium on (pp. 1-10). IEEE.[PDF]
  • Winkler, R. P., & Metu, S. (2017). An Extensible NetLogo Model for Visualizing Message Routing Protocols (No. ARL-SR-0380). US Army Research Laboratory Aberdeen Proving Ground United States.[PDF]
  • Yahyaoui, F., Tkiouat, M. 2017. A multi-level agent-based model of reinsurance. Journal of Applied Economic Sciences, Volume XII, Summer 3(49): 746– 752.[PDF]
  • Yasar, O., Maliekal, J., Veronesi, P., & Little, L. (2017). The essence of scientific and engineering thinking and tools to promote it. In Proceedings of the American Society of Engineering Education Annual Conference.
  • Ye, P., & Wang, F. Y. (2017). Hybrid calibration of agent-based travel model using traffic counts and AVI data. In Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on (pp. 457-462). IEEE.[PDF]
  • Yeager, M. E. (2017). Structural Complexity, Seascape Patchiness, and Body Size Interactively Mediate Seagrass Habitat Value for a Fish Mesopredator (Doctoral dissertation, San Diego State University).[PDF]
  • Yu, S., Hong, L., & Tian, W. (2017). An Integrated Spatial Analysis Computer Environment for Building Energy Efficiency at City Scale. UIA 2017 Seoul World Architects Congress.[PDF]
  • Yu, B., Ren, S., Wu, E., Zhou, Y., & Wang, Y. (2017). Optimization of urban bus operation frequency under common route condition with rail transit. Frontiers of Engineering Management, 4(4), 451-462.
  • Zamzami, N., & Schiffauerova, A. (2017).The impact of individual collaborative activities on knowledge creation and transmission. Scientometrics, 111(3), 1385-1413. [PDF]
  • Zandi, M. (2017).Simulation of Ascorbic Acid Release from Alginate-Whey Protein Concentrates Microspheres at the Simulated Gastrointestinal Condition Using Netlogo Platform. Journal of Food Process Engineering, 40. [PDF]
  • Zschache, J. (2017). The explanations of social conventions by melioration learning. Journal of Artificial Societies and Social Simulation, 20(3). doi.org/10.18564/jasss.3428
  • Zeng, Y., Li, S., & Deng, L. (2017, October). The measurement, mathematical and logical modeling, and agent-based simulation of carbon intangible assets with embedded strategies. In Knowledge Engineering and Applications (ICKEA), 2017 2nd International Conference on (pp. 76-80). IEEE.[ PDF]
  • Zhang, L., & Zeng, Z. (2017).Cascading Failure in the Maximum Entropy Based Dense Weighted Directed Network: An Agent-based Computational Experiment. [ PDF]
  • Zhao, H., & Sun, Y. (2017). Communication effect of passengers on information diffusion in metro emergency. Wuhan University Journal of Natural Sciences, 22(6), 503-509.[ PDF]
  • Zhao, Y., Ortt, R. J., & Katzy, B. R. Agent Based Simulation of Technological Innovation using Hypercycle Model.Center for Technology Innovation and Management, the Netherlands.[ PDF]
  • Zia, K., Din, A., Shahzad, K., & Ferscha, A. (2017).A Cognitive Agent-based Model for Multi-Robot Coverage at a City Scale. Complex Adaptive Systems Modeling, 5(1), 1. [ HTML]
  • Zinterhof, P. (2017). Vectorization of Cellular Automaton-Based Labeling of 3-D Binary Lattices. In Sustained Simulation Performance 2017 (pp. 89-109). Springer, Cham.[PDF]

2016

  • Abrahamson, D. (Chair), D. Clements (Discussant), & K. Chase (Organizer) (2016). Discovery-based STEM learning 2.0: Are we there yet? Symposium presented at the annual meeting of the American Educational Research Association (Special Interest Group: Learning Sciences), Washington, DC, April 8 – 12.
  • Amarasinghe, U. G. L. S., & Rajapakse, C. (2016). Urban traffic simulation using agent-based modelling: A study in the Sri Lankan context. In Proceedings of the International Research Symposium on Pure and Applied Sciences. Sri Lanka. [ PDF ]
  • Ameerbakhsh, O., Maharaj, S., Hussain, A., Paine, T., & Taiksi, S. (2016, September). An exploratory case study of interactive simulation for teaching Ecology. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1-7). IEEE.
  • Araujo Granda, P., Gras, A., Ginovart, M. (2016). MbT-Tool: An open-access tool based on Thermodynamic Electron Equivalents Model to obtain microbial-metabolic reactions to be used in biotechnological process. Computational and Structural Biotechnology Journal, 14: 325-332. [ HTML]
  • Araujo Granda, P., Gras, A., Ginovart, M., Moulton, V. (2016). INDISIM-Paracoccus, an individual-based and thermodynamic model for a denitrifying bacterium.Journal of Theoretical Biology, 403: 45-58. [ HTML]
  • Aslan, U., & Wilensky, U. (2016). Old Tricks Revisited: Studying Probabilistic Reasoning through Incorporating Computer Modeling into Piagetian Research. In Jean Piaget Society 46th annual meeting (pp. 9-11).
  • Aslan, U., & Wilensky, U. (2016). Restructuration in Practice: Challenging a Pop-Culture Evolutionary Theory through Agent Based Modeling. Proceedings of the Constructionism 2016 Conference. Bangkok, Thailand.
  • Bahle G., Poxrucker A., Kampis G., Lukowicz P. (2016). An Adaptive and Dynamic Simulation Framework for Incremental, Collaborative Classifier Fusion. Communications in Computer and Information Science (CCIS, volume 674). [HTML]
  • Barroso, C. J. V., & Babanto, R. R. P. (2016). Unwanted Teenage Pregnancies: Sociological Model Based on Agents. Asia Pacific Journal of Social and Behavioral Sciences, 13.[ PDF ]
  • Barrientos, A. H., & de la Mota, I. F. (2016). Modeling Sustainable Supply Chain Management as a Complex Adaptive System: The Emergence of Cooperation. In Krmac, E. (Ed.) In Sustainable Supply Chain Management. InTech. [ HTML]
  • Beriro, D, Cave, M., Wragg, J., Hughes, A. (2016). Agent based modelling : initial assessment for use on soil bioaccessibility. British Geological Survey (pp 29). [ PDF]
  • Bezirgiannis, N., Prasetya, I. S. W. B., & Sakellariou, I. (2016). HLogo: A Parallel Haskell Variant of NetLogo. In proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. Lisbon, Portugal. (pp. 119-128). [ PDF]
  • Bollinger, L. A., van Blijswijk, M. J., Dijkema, G. P., & Nikolic, I. (2016). An Energy Systems Modelling Tool for the Social Simulation Community. Journal of Artificial Societies and Social Simulation, 19(1). [HTML]
  • Boumans, I.J.J.M., Hofstede, G.J., Bolhuis, E.J., de Boer, I.J.M., Bokkers, E.A.M. (2016). Agent-based modelling in applied ethology: An exploratory case study of behavioural dynamics in tail biting in pigs. Applied Animal Behaviour Science, Volume 183, (pp 10-18). [PDF]
  • Brady, C., Weintrop, D., Anton, G., & Wilensky, U. (2016). Constructionist Learning at the Group Level with Programmable Badges. Proceedings of the Constructionism 2016 Conference. Bangkok, Thailand.[PDF]
  • Brady, C., Orton, K., Weintrop, D., Anton, G., Rodriguez, S. & Wilensky, U. (2016). All Roads Lead to Computing: Making, Participatory Simulations, and Social Computing as pathways to Computer Science. IEEE Transactions on Education, 60(99), 1-8.[PDF]
  • Brewer, K., & Bareiss, C. (2016). Introduction to Computational Science. In Concise Guide to Computing Foundations (pp. 1-8). Springer, Cham.
  • Brewer, K., Bareiss, C. (2016). Procedures: Algorithms and Abstraction. Concise Guide to Computing Foundations (pp 45-57).[PDF]
  • Brughmans, T. & Poblome, J. (2016). MERCURY: An agent-based model of tableware trade in the Roman east. Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.2953
  • Calik, S. K., Kugu, E., Birtane, S., & Sahingoz, O. K. (2016). A Multi Agent Solution for UAV Path Planning Problem with NetLogo. International Journal of Applied Engineering Research, 11(15), 8397-8401. [HTML]
  • Canavesio, M.M., Quaglia, C., Martínez, E. Agent-based simulation of a project fractal company XXII Congreso Argentino de Ciencias de la Computación (CACIC 2016). [PDF]
  • Carbo, J., Sanchez-Pi, N., & Molina, J. M. (2016). Agent-based simulation with NetLogo to evaluate ambient intelligence scenarios. Journal of Simulation. [PDF]
  • Cartel, J. E., & Clutario, W. A. (2016). Socio-Environmental Agent-Based Simulation on the Livability of Two Cities. Journal of Science, Engineering and Technology, 4, 42-48. [HTML]
  • Cervantes, D. Flores, D.L., Gutiérrez, E., Chacón, M.A.(2016). Ce,Tb-Doped Y2SiO5 Phosphor Luminescence Emissions Modeling and Simulation. Properties and Characterization of Modern Materials. Volume 33 of the series Advanced Structured Materials (pp 145-156).[PDF]
  • Chandrasekaran, S., & Hougen, D. F. (2016). Trade-Offs in Cooperative Goal Seek using Nano-Devices. June, 13, 15.[HTML]
  • Chen, F., Meng, Q., & Li, F. (2016). Simulation of technology sourcing overseas post-merger behaviors in a global game model. Journal of Artificial Science and Social Simulation, 19(4). doi.org/10.18564/jasss.3122
  • Chen, Z. (2016). An Agent-Based Model for Information Diffusion Over Online Social Networks Kent State University.[PDF]
  • Christos, K., Dimitrios, B., & Dimitrios, A. (2016). Agent-based simulation for modeling supply chains: A comparative case study. International Journal of New Technology and Research, 2(10).
  • Cogliano, J. F., & Jiang, X. (2016). Agent-based computational economics: simulation tools for heterodox research. In F. S. Lee & B. Cronin (Eds.), Handbook of research methods and applications in heterodox economics (pp. 253-271). Edward Elgar Publishing Inc. 10.4337/9781782548461
  • Conales, C. P., Janamjam, C. T., & Polinar, J. U. (2016). A Path to Equality on Wealth Distribution: Basis for PhilippinesPolicy Reforms. Asia Pacific Journal of Social and Behavioral Sciences, 13.[PDF]
  • Egar, M. M., Arbutante, D. C. C., & Cauilan, J. J. (2016). An Agent-Based Model on the Potential of a Dipterocarp Forest Fire. Asia Pacific Journal of Social and Behavioral Sciences, 13.[PDF]
  • Ergazaki, M., & Ampatzidis, G. (2016). Can the idea of'Balance of Nature'be effectively challenged within a model-based learning environment? Insights from the second cycle of developmental research. [PDF]
  • Espina, M. O., & Lapates, J. M. (2016). Social Network Behaviours to Explain the Spread of Online Game. Asia Pacific Journal of Social and Behavioral Sciences, 13.[PDF]
  • Everton, R. R., de Castro, P.A.L., Sichman, J.S. (2016). Enhancing Classification Accuracy Through Feature Selection Methods. XIII Encontro Nacional de Inteligˆencia Artificial e Computacional. [PDF]
  • Farrenkopf, T., Guckert, M., Urquhart, N., & Wells, S. (2016). Ontology based business simulations. Journal of Artificial Science and Social Simulation, 19(4). doi.org/10.18564/jasss.3266
  • Feliciani, C., & Nishinari, K. (2016). An improved Cellular Automata model to simulate the behavior of high density crowd and validation by experimental data. Physica A: Statistical Mechanics and its Applications. [HTML]
  • Friege, J., Holtz, G., & Chappin, É. J. L. (2016). Exploring homeowners' insulation activity. Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.2941
  • García-Valdecasas, José Ignacio (2016). Simulación Basada en Agentes. Introducción a NetLogo. Cuadernos Metodológicos del Centro de Investigaciones Sociológicas, Madrid, Spain
  • Ge, J. & Polhill, G. (2016). Exploring the combined effect of factors influencing commuting patterns and CO2 emissions in Aberdeen using an agent-based model. Journal of Artificial Science and Social Simulation, 19(3). doi.org/10.18564/jasss.3078
  • Greco, A., Cannizzaro, F., Pluchino, A. (2016). Seismic collapse prediction of frame structures by means of genetic algorithms. Engineering Structures Volume 143, Pages 152–168. [HTML]
  • Greeven, S., Kraan, O., Chappin, É. J. L., & Kwakkel, J. H. (2016). The emergence of climate change mitigation action by society: An agent-based scenario discovery study. Journal of Artificial Science and Social Simulation, 19(3). doi.org/10.18564/jasss.3134
  • Guo, Y., & Wilensky, U. Small Bugs, Big Ideas: Teaching Complex Systems Principles Through Agent-Based Models of Social Insects. Proceedings of Artificial Life Conference 2016 (p. 664).Chicago.[HTML]
  • Hamhalter, A., & Švarný, P. (2016). Simulation of Self-reconfigurable Material Systems. In Applied Mechanics and Materials (Vol. 825, pp. 119-122). Trans Tech Publications Ltd.
  • Hasson, S. T., & Hasan, Z. Y. (2016). Simulating Road Modeling Approach’s in Vanet Environment Using Net Logo. Research Journal of Applied Sciences, 11(10), 1130-1136. [HTML]
  • Hjorth, A., Brady, C., Head, B. & Wilensky, U. (2016). Turtles All the Way Down: Presenting LevelSpace, a NetLogo Extension for Reasoning About Complex Connectedness. Constructionism 2016, Bangkok, Thailand.
  • Hjorth, A., Weintrop., D., Brady C. & Wilensky., U. (2016). LevelSpace: Constructing Models and Explanations Across Levels. Constructionism 2016, Bangkok, Thailand.
  • Hodzic, M., Selman, S., Hadzikadic, M. (2016). Complex Ecological System Modeling. Periodicals of Engineering and Natural Sciences Vol 4, No 1. [PDF]
  • Inovejas, C. J., Mirasol, J. M., Recente, J. M., & Frias, M. (2016). Modeling Civil Unrest in the Philippines. Asia Pacific Journal of Social and Behavioral Sciences, 13.[PDF]
  • JIANG, L., & Yueliang, S. U. (2016). Analysis of the Influence of the Penalty on the Cooperative Behavior Based on Netlogo Computer Simulation. Management Science and Engineering, 10(3), 1-6. [HTML]
  • Jimenez-Romero, C., & Johnson, J. (2016). SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo. Neural Computing and Applications, 1-10. [ HTML]
  • Jing, W., & Ling, C. (2016). Study on the relationship between the team commitment, knowledge sharing and performance. In Logistics, Informatics and Service Sciences (LISS), 2016 International Conference on (pp. 1-4). IEEE. [ pDF]
  • Kowalska-Styczeń, A. & Sznajd-Weron, K. (2016). From consumer decision to market share - unanimity of majority? Journal of Artificial Science and Social Simulation, 19(4). doi.org/10.18564/jasss.3156
  • Kponyo, J. J., K. S. Nwizege, K. A. Opare, A. R. Ahmed, H. Hamdoun, L. O. Akazua, S. Alshehri, and H. Frank. (2016). A Distributed Intelligent Traffic System Using Ant Colony Optimization: A NetLogo Modeling Approach. In Systems Informatics, Modelling and Simulation (SIMS), International Conference on (pp. 11-17). IEEE. [HTML]
  • Krishnan, R. (2016). Biomass residues for power generation: A simulation study of their usage at Liberia’s plantations. Diss. University of Michigan, 2016.[PDF]
  • Kurahashi-Nakamura, T., Mäs, M., & Lorenz, J. (2016). Robust clustering in generalized bounded confidence models. Journal of Artificial Societies and Social Simulation, 19(4). doi.org/10.18564/jasss.3220
  • Lai, Polly & Jacobson, Michael & Markauskaite, Lina. (2016). Agent-Based Models Versus Video-Based Visualizations to Learn Nanoscience Concepts: An Embodied Cognition Perspective.
  • Lamy, F., Quinn, B., Dwyer, R., Thomson, N., Moore, D., & Dietze, P. (2016). TreatMethHarm: An agent-based simulation of how people who use methamphetamine access treatment. Journal of Artificial Science and Social Simulation, 19(2). doi.org/10.18564/jasss.3069
  • Levin, J. A., & Ching, C. C. (2016). A multi-mediator framework for understanding teaching and learning in higher education classrooms.
  • Ma, Y., Shen, Z., & Nguyen, D. T. (2016). Agent-based simulation to inform planning strategies for welfare facilities for the elderly: Day care center development in a Japanese city. Journal of Artificial Science and Social Simulation, 19(4). doi.org/10.18564/jasss.3090
  • Maroulis, S. (2016). Interpreting school choice treatment effects: Results and implications from computational experiments. Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.3002
  • Martínez, D. L., & Halme, A. (2016). MarSim, a Simulation of the MarsuBots Fleet Using NetLogo. In Distributed Autonomous Robotic Systems (pp. 79-87). Springer Japan.[HTML]
  • Mehic, S., Tadano, K., Vicario, E. (2016). Combining Simulation and Mean Field Analysis in Quantitative Evaluation of Crowd Evacuation Scenarios EPEW 2016: Computer Performance Engineering (pp 174-186). [HTML]
  • Monett, D., Navarro-Barrientos, J. E. (2016). Simulating the fractional reserve banking using agent-based modelling with NetLogo Federated Conference on Computer Science and Information Systems (FedCSIS). [PDF]
  • Opiyo, N.N. (2016). Modelling temporal diffusion of PV mircogeneration systems in a rural developing community Energy and Resources Research Institute (University of Leeds). [PDF]
  • Orly, L., Nuha, C., Vadim, T. (2016). Use of a sonification system for science learning by people who are blind. Journal of Assistive Technologies, Vol. 10 Issue: 4, pp.187-198. [HTML]
  • Othmane, A.B., Tettamanzi, A., Villata, S. & Nhan, L.T. (2016). A Multi-context BDI Recommender System: From Theory to Simulation Institute of Electrical and Electronics Engineers Conference (13-16 Oct. 2016). Omaha, NE, USA.[PDF]
  • Ozaeta, L., Graña, M. (2016). Agent-Based Spatial Dynamic Modeling of Opinion Propagation Exploring Delaying Conditions to Achieve Homogeneity.International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. ICEUTE 2016, SOCO 2016, CISIS 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham[HTML]
  • Pardo, M., Wilfredo, F.C. (2016). Agent-based Modeling and Simulation to Adoption Process of Information Technologies in Health Systems Institute of Electrical and Electronics Engineers Latin American Transactions.[PDF]
  • Pashakalaei, A. G. (2016). Optimal Energy Managment System for a Net Zero Building Using Multi-Agent Systems Approach The University of Texas at El Paso. Ann Arbor.[HTML]
  • Plewe, D.A., Lee, H. (2016). Simulating the Outcomes of Contracts: A Visual Interface Supporting Start-Up Financing Advances in Human Factors, Business Management, Training and Education. Advances in Intelligent Systems and Computing, vol 498.[HTML]
  • Prats Soler, C., Montañola-Sales, C., Gilabert-Navarro, J. F., Valls, J., Casanovas, J. C. G., Vilaplana i Massaguer, C., ... & López, D. (2016). Individual-based modeling of tuberculosis in a user-friendly interface. Frontiers in Microbiology, 6(1654).[PDF]
  • Qi, H., Zhang, M., Chen, H., & Liu, F. (2016). Simulation of Chinese Coal Mine Safety Supervision System Performance Based on Netlogo Platform. Journal of Computational and Theoretical Nanoscience, 13(8), 5072-5080.[HTML]
  • Qiuyun, M., Yang, S. (2016). Design and Simulation Analysis of Multi-Agent Online Dissemination Model on the Basis of "The Spiral of Silence" Theory Journal of Computer and Information Science, 1913-8989.[PDF]
  • Rajanikanth, K.N., Meenakshi, D., Kumar, S.B., Nitin, K. (2016). Incorporating adaptivity using learning in avionics self adaptive software: A case study. Institute of Electrical and Electronics Engineers Conference (21-24 Sept. 2016).[PDF]
  • Razzaq, S., Riaz, F., Mehmood, T., & Ratyal, N. I. (2016, April). Multi-factors based road accident prevention system. In 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube) (pp. 190-195). IEEE.
  • Rouchier, J. & Tanimura, E. (2016). Learning with communication barriers due to overconfidence: What a "model-to-model analysis" can add to the understanding of a problem. Journal of Artificial Science and Social Simulation, 19(2). doi.org/10.18564/jasss.3039
  • Rubio, M. T., & Tulang, A. B. (2016). Socio-Environmental Learning Model. Asia Pacific Journal of Social and Behavioral Sciences, 13. [PDF]
  • Sanchez-Segura, M.I., Dugarte-Peña, G.L., Medina-Dominguez, F., Ruiz-Robles, A. (2016). A model of biomimetic process assets to simulate their impact on strategic goals. Information Systems Frontiers (pp 1-18).[HTML]
  • Santos, J.L., Sampaio, R.R. (2016). Informal social networks and knowledge diffusion: a modeling proposal applied to a software development environment. Perspectivas em Ciência da Informação, 1981-5344.[PDF]
  • Scott, N., Livingston, M., Hart, A., Wilson, J., Moore, D., & Dietze, P. (2016). SimDrink: An Agent-Based NetLogo Model of Young, Heavy Drinkers for Conducting Alcohol Policy Experiments. Journal of Artificial Societies and Social Simulation, 19(1).[HTML]
  • Sengupta, P., & Wilensky, U. (2016). Understanding Electric Current Using Agent-Based Models: Connecting the Micro-level with Flow Rate. In: Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016).
  • Shamsuddin, A Z M, Ahsan, T. and Momen, S. (2016). Trophallaxis and energy optimization in swarms of robots. 19th International Conference on Computer and Information Technology (ICCIT 2016, IEEE). [HTML]
  • Sheppard, C. J. R., A. Harris, and A. R. Gopal. (2016a). Cost-effective siting of electric vehicle charging infrastructure with agent-based modeling. IEEE Transactions on Transportation Electrification, 2(2), 174-189.
  • Sheppard, C. J., Gopal, A. R., Harris, A., & Jacobson, A. (2016b). Cost-effective electric vehicle charging infrastructure siting for Delhi. Environmental Research Letters, 11(6), 64010-64021.
  • Song, S. X., Liang, X. Y., Mei, Y. J., Wen, X., Wang, Y. N., & Mao, N. Z. (2016). Modeling and simulating land abandonment behavior of farmer households based on the CBDI. J. Nat. Resour, 31, 1926-1937.
  • Song, Y. M., & Kim, S. A. (2016). A Study on the Application of the Complex Systems Theory to Understand Urban Phenomena-Focusing on Citizen's Awareness Change through Information Transmission. Journal of the Architectural Institute of Korea Planning & Design, 32(1), 51-58.
  • Štekerová, K., Danielisová, A. (2016). Economic Sustainability in Relation to Demographic Decline of Celtic Agglomerations in Central Europe: Multiple-Scenario Approach. Computational Social Sciences (pp 335-357). [PDF]
  • Suwarno, A., van Noordwijk, M., Weikard, H-P, & Suyamto, D. (2016). Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES). Mitigation and Adaptation Strategies for Global Change. doi:10.1007/s11027-016-9721-0. [HTML]
  • ten Broeke, G., van Voorn, G., & Ligtenberg, A. (2016). Which sensitivity analysis method should I use for my agent-based model? Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.2857
  • Thomas, S. A., Lloyd, D. J., & Skeldon, A. C. (2016). Equation-free analysis of agent-based models and systematic parameter determination: A NetLogo Implementation. [ HTML
  • Thuy An Vo, T., van der Waerden, P. J. H. J., & Wets, G. (2016). Micro-simulation of car drivers’ movements at parking lots. Procedia Engineering, 142, 100-107. [HTML]
  • Troitzsch, K. G. (2016). Extortion Rackets: An Event-Oriented Model of Interventions. In Social Dimensions of Organised Crime (pp. 117-131). Springer International Publishing.[PDF]
  • Übler, H. & Hartmann, S. (2016). Simulating trends in artificial influence networks. Journal of Artificial Science and Social Simulation, 19(1). doi.org/10.18564/jasss.2978
  • Vanloo, G., Ng, C., Osborne, K., Wong, K., Ramsey, B., Wang, D., Bao, Z. (2016). Using High Performance Computing to Model Cellular Embryogenesis. Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale Article No. 12 [PDF]
  • Vo, T. T. A., van der Waerden, P., & Wets, G. (2016). Micro-simulation of car drivers’ movements at parking lots. Procedia Engineering, 142, 100-107.
  • Wagner N., Sahin C., Hanson D., Pena J., Vuksani E., and Tello B. (2016). Quantitative Analysis of the Mission Impact for Host-Level Cyber Defensive Mitigations. to appear in Proceedings of the 2016 ACM Spring Simulation Multi-Conference - Annual Simulation Symposium, April, 2016.
  • WaiShiang, C., YeeWai, S. Nizam, S. (2016). Agent Oriented Requirement Engineering for Lake. Mathematical Modelling: Preliminary Study. Journal of Telecommunication Electronic and Computer Engineering, 8(2), (pp 5-10). [PDF]
  • Wang, S., & Hu, K. (2016, July). Towards dynamic epistemic learning of actions in autonomic multi-agent systems. In 2016 IEEE International Conference on Autonomic Computing (ICAC) (pp. 237-238). IEEE.
  • Wang, Y., Chen, H., Song, S., MEI, Y., Wen, X. (2016). Simulation of households' planting behavior based on a CR-BDI model: Case study of Jiangxingzhuang Village of Mizhi County in Shaanxi Province. Department of Urban and Resource Sciences, Northwest University, Xi'an 710127, China. [ PDF]
  • Warnke, T., Reinhardt, O., & Uhrmacher, A.M. (2016). Population-Based CTMCS and Agent-Based Models. In Proceedings of the 2016 Winter Simulation Conference. Washington, D.C. [ PDF]
  • White, D. G., & Levin, J. A. (2016). Navigating the turbulent waters of school reform guided by complexity theory. Complicity: An International Journal of Complexity and Education, 13(1). [HTML]
  • Weintrop, D., Hjorth, A., & Wilensky, U. (2016). NetLogo Web: Bringing Turtles to the Cloud. Workshop at Constructionism 2016. Bangkok, Thailand.
  • Will, T. E. (2016) Flock Leadership: Building Collective Capacity by Managing Group Norms. Presented at the International Leadership Association’s Annual Global Conference, Atlanta, GA.
  • Will, T. E. (2016). Flock Leadership: Understanding and Influencing Emergent Collective Behavior. The Leadership Quarterly, 27: 261-279.[PDF]
  • Will, T. E. (2016) A Flocking Model of Human Organizing: Leadership Implications. Presented at the Annual Meeting of the Academy of Management, Anaheim, CA.
  • Wirth, E., Szabó, Gy., and Czinkóczky, A. (2016). MEASURE OF LANDSCAPE HETEROGENEITY BY AGENT-BASED METHODOLOGY. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 145-151, doi:10.5194/isprs-annals-III-8-145-2016, 2016. [PDF]
  • Wurzer, G., Pont, U., Lorenz, W.E., Mahdavi, A. (2016). COUPLING BUILDING MORPHOLOGY OPTIMIZATION AND ENERGY EFFICIENCY – A PROOF OF CONCEPT. Department of Building Physics and Building Ecology, TU Wien, Wien, Austria [ PDF]
  • Xiaohong, S. H. A. N., Panpan, J. I. A., & Xiaoyan, L. I. U. (2016). WeChat Information Dissemination Mechanism and Simulation Research Based on Field Theory. Journal of System Simulation, 28(11), 2867.
  • Zhang, R., & Yang, C. (2016). The Simulation Research of Potential Loop in Redistribution of Multipoint Two-way Routing Protocol Based on the Butterfly Effect. Proceedings of the 3rd International Conference on Engineering Technology and Application. Thailand. [ PDF]
  • ZHANG, Z. (2016). A Simulation Study on Cooperation Behavior Using NetLogo Software Considering Resource Re-Allocation. Canadian Social Science, 12(4), 20-26. [HTML]
  • Zhu, C., & Yangzhou, C., Guiping, D. (2016). Controller-Based Management of Connected Vehicles in the Urban Expressway Merging Zone Information Technology and Intelligent Transportation Systems (pp 65-75).[ HTML]


2015

  • Abayneh Abebe, Y., Vojinovic, Z., Nikolic, I., Hammond, M., Sanchez, A., & Pelling, M. (2015, April). Holistic flood risk assessment using agent-based modelling: the case of Sint Maarten Island. In EGU General Assembly Conference Abstracts (p. 11584).
  • Alharbi, H., & Hussain, A. (2015, March). An Agent-Based Approach for Modelling Peer to Peer Networks. In 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim) (pp. 532-537). IEEE.
  • Almagooshi, S. (2015). Simulation modelling in healthcare: Challenges and trends. Procedia Manufacturing, 3, 301-307.
  • Al-Sakran, H. O. (2015). Intelligent traffic information system based on integration of Internet of Things and Agent technology. International Journal of Advanced Computer Science and Applications (IJACSA), 6(2), 37-43.
  • Almarza Díaz, David. (2015). Evaluación del programa de simulación NetLogo como herramienta motivadora y eficaz para trabajar destrezas científicas. [Trabajo Fin de Máster].
  • Altawee, M. (2015). Book Review: Agent-based computational economics Using NetLogo.
  • Amblard, F., Daudé, E., Gaudou, B., Grignard, A., Hutzler, G., Lang, C., ... & Taillandier, P. (2015). Introduction à NetLogo. Simulation spatiale à base d'agents avec NetLogo, partie 1, 73-112. [HTML]
  • Angus, S. D. & Hassani-Mahmooei, B. (2015). "Anarchy" reigns: A quantitative analysis of agent-based modelling publication practices in JASSS, 2001-2012. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2952
  • Ayaragarnchanakul, E. (2015). An agent-based model of polycentric city formation: application to the Bangkok Metropolitan Region (Doctoral dissertation, Faculty of Economics, Thammasat University).
  • Badham, J. (2015). Review of An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NETLogo.
  • Balaraman, V., Athle, D., & Singh, M. (2015).Do Daily Routines Affect Convenience Store Footfalls? - Some Experiments with Agent Based Simulation. To appear in SummerSim 15.
  • Balestrini-Robinson, S., Horne, G., Ng, K., Huopio, S., & Schubert, J. Team 1: Cyber Defence in Support of NATO. Scythe, 2.
  • Banitz, T., Gras, A., & Ginovart, M. (2015).Individual-based modeling of soil organic matter in NetLogo: Transparent, user-friendly, and open. Environmental Modeling & Software 71, 39-45. [HTML]
  • Banos, A., Lang, C., & Marilleau, N. (2015). Agent-Based Spatial Simulation with NetLogo (Vol. 1). Elsevier. [HTML]
  • Barnett, R. (2015). A conversation with: Rod Barnett. Kerb: Journal of Landscape Architecture, (23), 30-33.
  • Basu, S., Sengupta, P., & Biswas, G. (2015).A scaffolding framework to support learning of emergent phenomena using multi-agent based simulation environments. Research in Science Education, 45(2), 293-324.
  • Baudry, D., Mustafee, N., Louis, A., Smart, P. A., Godsiff, P., & Mazari, B. (2015). Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system. Journal of Intelligent Manufacturing, 1-17. [ PDF]
  • Bennet, N. (2015). NetLogo Tutorial Series: Set Theory Concepts and Applications. licensed under a Creative Commons Atribution NonCommercial-ShareAlike 4.0 International License.
  • Bergant, D., Bergant, M. D., RNetLogo, I., & LazyData, T. R. U. E. (2015). Package ‘nlexperiment’. [PDF]
  • Berland, M., & Wilensky, U. (2015).Comparing Virtual and Physical Robotics Environments for Teaching Complex Systems and Computational Literacies. Journal of Science Education and Technology.
  • Bhatia, A., Sharma, C., & Goyal, R. (2015). Development of an agent based model illustrating the usage of deferred acceptance algorithm in the admission process (No. e825v1). PeerJ PrePrints.
  • Bhatia, A., Singh, A., & Goyal, R. (2015).A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems Computers 4, 2-23. [HTML]
  • Blondin, J., Halpern, C., & Malkus, T. (2015). Predator-prey game to maintain stable fish population for Ecotoxicological studies.
  • Blum, C., Lozano, J. A., Davidson, P.P. (2015).An Artificial Bioindicator System for Network Intrusion Detection. Artificial Life 21(2), 93-118. [HTML]
  • Boero, R. (2015). Behavioral Computational Social Science. WileyBlackwell.
  • Brady, C., Holbert, N., Soylu, F., Novak, M., & Wilensky, U. (2015).Sandboxes for Model-Based Inquiry. Journal of Science Education and Technology, 24(2). 265-286.
  • Brady, C., Weintrop, D., Gracey, K., Anton, G., & Wilensky, U. (2015).The CCL-Parallax Programmable Badge: Learning with Low-Cost, Communicative Wearable Computers. In Proceedings of the 16th Annual Conference on Information Technology Education (pp. 139–144). New York, NY, USA: ACM.
  • Buda, A., & Jarynowski, A. (2015). Agent-based modeling, complex networks and system dynamics–practical aproaches. [HTML]
  • Camus, B., Bourjot, C., & Chevrier, V. (2015). Considering a multi-level model as a society of interacting models: Application to a collective motion example. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2645
  • Cascalho, J., & Mabunda, P. (2015). Agent-Based Modelling for a Resource Management Problem in a Role-Playing Game. In Progress in Artificial Intelligence (pp. 696-701). Springer International Publishing. [HTML]
  • Challenge, N. M. S., Djidjev, C., Djidjev, H., & Djidjev, S. (2015). Spread of Viruses on a Computer Network.
  • Chandra Putra, H., Zhang, H., & Andrews, C. (2015). Modeling real estate market responses to climate change in the coastal zone. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2577
  • Chen, S. H., Chie, B. T., & Zhang, T. (2015). Network-based trust games: An agent-based model. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2767
  • Chhatwal, J., & He, T. (2015). Economic evaluations with agent-based modelling: an introduction. Pharmacoeconomics, 33(5), 423-433.
  • Chunying, Y. A. N. G. (2015). Generating Mechanism Model of and Simulation Analysis on the Group Event on Internet. Sci-Tech Information Development & Economy, 2015, 20.
  • Ciutacu, I., & Micu, L. A. (2015). THE FIRM, PART OF THE ECONOMIC SYSTEM: REASONS FOR EXITING A MARKET-AN AGENT-BASED MODELING APPROACH. Revista Economica, 67.
  • Collins, A. J., Frydenlund, E., Elzie, T., & Robinson, R. M. (2015, April). Agent-based pedestrian evacuation modeling: a one-size fits all approach?. In Proceedings of the Symposium on Agent-Directed Simulation (pp. 9-17). Society for Computer Simulation International. [HTML]
  • Collins, A., Petty, M., Vernon-Bido, D., & Sherfey, S. (2015). A call to arms: Standards for agent-based modeling and simulation. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2838
  • Constantine, M. (2015). A Model of Speciation on a Newly Formed Insular Environment Using a Netlogo Model. 대한지리학회 학술대회논문집, 227-227.
  • Corson, N., & Olivier, D. (2015). Dynamical Systems with NetLogo. In Agent-Based Spatial Simulation with NetLogo (pp. 183-221). Elsevier.
  • Cowie, G., Hurd, M., Sevostianov, V., Cundiff, M. E., & Guerin, M. S. (2015). PAVL: Personal Assistance for the Visually Limited. [HTML]
  • Das, U., Dzikowski, J., & Hood, C. S. (2015, September). Evaluating Cognitive Radio Networks an agent based modeling approach. In 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) (pp. 305-308). IEEE.
  • Dickerson, M. (2015). Agent-based modeling and NetLogo in the introductory computer science curriculum: tutorial presentation. Journal of Computing Sciences in Colleges, 30(5), 174-177. [HTML]
  • D’Alessandro, S., Johnson, L., Gray, D., & Carter, L. (2015). Consumer satisfaction versus churn in the case of upgrades of 3G to 4G cell networks. Marketing Letters, 26(4), 489-500.HTML]
  • del Sol, M., Hill, M., James, K., Ward, R., & Prescott, P. (2015). Using a Concentrated Heat System to Shock the P53 Protein to Direct Cancer Cells into Apoptosis. [HTML]
  • Dubrow, A. (2015).Back to school with new cyberlearning tools. National Science Foundation (NSF). [HTML]
  • Dykstra, P., Jager, W., Elsenbroich, C., Verbrugge, R., & de Lavaletter, G. R. (2015). An agent-based dialogical model with fuzzy attitudes. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2813
  • Dzikowski, J., & Hood, C. (2015). Modeling cognitive radio networks in NetLogo. In Proceedings of the Conference on Summer Computer Simulation (pp. 1-11). Society for Computer Simulation International. [HTML]
  • Fachada, N., Lopes, V.V., Martins, R.C., & Rosa, A.C. (2015).Towards a standard model for research in agent-based modeling and simulation. Warburg's lens: A mathematical oncology pre-print discussion forum. [HTML]
  • Faroqi, H., & Mesgari, M. S. (2015). Agent-based crowd simulation considering emotion contagion for emergency evacuation problem. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), 193.
  • Ferrando, P., & Fortiana, J. 33. FINANCIAL TIME SERIES OBTAINED BY AGENT-BASED SIMULATION. In Current Topics on Risk Analysis: ICRA6 and RISK 2015 Conference (p. 281).
  • Fitzpatrick, B., Martinez, J., Polidan, E., & Angelis, E. (2015). The big impact of small groups on college drinking. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2760
  • Flores, D. L., & Gómez, C. M. (2015). Computational modeling of the MAPK pathway using NetLogo. LATIN AMERICAN JOURNAL OF APPLIED ENGINEERING, 1(1).[HTML]
  • Fonseca, F., Ramos, R. A. R., & da Silva, A. N. R. (2015). An agent-based model to assess the attractiveness of industrial estates. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2893
  • Furtado, B. A., Sakowski, P. A. M., & Tovolli, M. H. (2015). Modeling Complex Systems for Public Policies. Institute for Applied Economic Research.
  • Gammack, D. (2015). Using NetLogo as a tool to encourage scientific thinking across disciplines. Journal of Teaching and Learning with Technology, 4(1), 22-39.
  • Gkiolmas, A., Chalkidis, A., Papaconstantinou, M., Iqbal, Z., & Skordoulis, C. (2015). An alternative use of the NetLogo modeling environment, where the student thinks and acts like an Agent, in order to teach concepts of Ecology. arXiv preprint arXiv:1501.05779.[HTML]
  • Goel, A. K., & Joyner, D. A. (2015). Impact of a Creativity Support Tool on Student Learning about Scientific Discovery Processes. In Proceedings of the Sixth International Conference on Computational Creativity June (p. 284).[HTML]
  • Golling, M., Koch, R., Hillmann, P., Eiseler, V., Stiemert, L., & Rekker, A. (2015, November). On the evaluation of military simulations: towards a taxonomy of assessment criteria. In 2015 Military Communications and Information Systems Conference (MilCIS) (pp. 1-7). IEEE.
  • Gong, T., Pei, L., Gao, S., Han, F., Zhao, S., & Cai, Z. (2015, July). Visual netlogo-based simulation of anti-SARS immune system and low-to-high resolution reconstruction of sequence medical ct images anti-sars CT. In 2015 International Workshop on Artificial Immune Systems (AIS) (pp. 1-8). IEEE.
  • Gordo, E., Khalaf, N., Strangeowl, T., Dolino, R., & Bennett, N. (2015). FACTORS AFFECTING SOLAR POWER PRODUCTION EFFICIENCY. [HTML]
  • Greasley, A., & Owen, C. (2015, July). Implementing an Agent-based Model with a Spatial Visual Display in Discrete-event Simulation Software. In SIMULTECH (pp. 125-129).
  • Grgurina, N., Barendsen, E., van Veen, K., Suhre, C., & Zwaneveld, B. (2015, November). Exploring Students' Computational Thinking Skills in Modeling and Simulation Projects: a Pilot Study. In Proceedings of the Workshop in Primary and Secondary Computing Education on ZZZ (pp. 65-68). ACM. [HTML]
  • Grow, A., Flache, A., & Wittek, R. (2015). An agent-based model of status construction in task focused groups. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2740
  • Gu, X., Blackmore, K., Cornforth, D., & Nesbitt, K. (2015). Modelling academics as agents: An implementation of an agent-based strategic publication model. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2725
  • Han, Z., Zhang, K., Yin, H., & Zhu, Y. (2015, May). An urban traffic simulation system based on multi-agent modeling. In Control and Decision Conference (CCDC), 2015 27th Chinese (pp. 6378-6383). IEEE.[HTML]
  • Haydari, S. & Smead, R. (2015). Does longer copyright protection help or hurt scientific knowledge creation? Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2720
  • Head, B., Hjorth, A., Brady, C., & Wilensky, U. (2015, December). Evolving agent cognition with netlogo levelspace. In L.Yilmaz, W.K.V. Chan, I. Moon, T.M.K. Roeder, C. Macal, & M.D. Rossetti (Eds.). Proceedings of the 2015 Winter Simulation Conference (pp. 3122-3123). IEEE Press.[HTML]
  • Hjorth, A., Brady, C., Head, B. and Wilensky, U. (2015).LevelSpaceGUI - Scaffolding Novice Modelers’ Inter-Model Explorations. In proceedings for Interaction Design & Children 2015. Boston, MA.
  • Hjorth, A., Brady, C., Head, B., Wilensky, U. (2015).Thinking Within and Between Levels: Exploring Reasoning with Multi-Level Linked Models. In T. Koschmann, P. Häkkinen, & P. Tchounikine (Eds.), "Exploring the material conditions of learning: opportunities and challenges for CSCL," the Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference Gothenburg, Sweden: ISLS.
  • Hmelo‐Silver, C. E., Liu, L., Gray, S., & Jordan, R. (2015). Using representational tools to learn about complex systems: A tale of two classrooms. Journal of Research in Science Teaching, 52(1), 6-35.[HTML]
  • Holbert, N., Brady, C., Soylu, F., Novak, M., & Wilensky, U. (2015).The Model Gallery: Supporting Idea Diffusion in Computational Modeling Activities. Poster presented at the AERA Annual Meeting, Chicago, IL: April, 2015.
  • Huang, L., Wang, S., Hsu, C. H., Zhang, J., & Yang, F. (2015). Using reputation measurement to defend mobile social networks against malicious feedback ratings. The Journal of Supercomputing, 71(6), 2190-2203.
  • Izquierdo, L. R., Olaru, D., Izquierdo, S. S., Purchase, S., & Soutar, G. N. (2015). Fuzzy Logic for Social Simulation Using NetLogo. Journal of Artificial Societies and Social Simulation, 18 (4) 1. [ HTML]
  • Jacobson, M. J., Kim, B., Pathak, S., & Zhang, B. (2015). To guide or not to guide: issues in the sequencing of pedagogical structure in computational model-based learning. Interactive Learning Environments, 23(6), 715-730.
  • Jeewan, A., & Hussain, R. (2015). Minimal Energy Consumption by WSN Nodes during Communication using LEACH and NetLogo in Intelligent Greenhouse. International Journal of Advanced Research in Computer Science 6.6 [HTML]
  • Jerry, K., Yujun, K., Kwasi, O., Enzhan, Z., & Parfait, T. (2015). NetLogo implementation of an ant colony optimisation solution to the traffic problem. Intelligent Transport Systems, IET, 9(9), 862-869. [HTML]
  • Jiang, G., Liu, X., & Wang, Y. (2015). An Agent-based Simulation System for Evolution of Interplay between E-Commerce Vendor and Consumers. International Journal of Hybrid Information Technology, 8(4), 81-88. [HTML]
  • Jiang, G., Wang, Y., & Zhang, N. (2015). Evolution of the Interplay Between E-Commerce Vendor and Consumers. In LISS 2014 (pp. 1243-1247). Springer Berlin Heidelberg.[HTML]
  • Jimenez-Romero, C., Sousa-Rodrigues, D., & Johnson, J.H. (2015). A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones. Proceedings of the UK Workshop on Computational Intelligence (UKCI 2015) Conference, at Exeter. [HTML]
  • Jimenez-Romero, C., Sousa-Rodrigues, D., & Johnson, J. H. (2015). Designing behaviour in bio-inspired robots using associative topologies of spiking-neural-networks. arXiv preprint arXiv:1509.07035.
  • JIN, Z., WU, Y., & YUE, D. (2015). Simulation for distributed load control based on Netlogo and Matlab. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 06.
  • Jun, Y. C. (2015). A qualitative case study of computer programming and unfolding creative processes: focusing on NetLogo-based computational thinking. The Journal of Korean Association of Computer Education, 18(3), 1-14.
  • Just, W., Callender, H. L., & Lamar, D. M. (2015, October). Exploring transmission of infectious diseases on networks with NetLogo. In International Symposium on Biomathematics.
  • Kahl, C. H. & Hansen, H. (2015). Simulating creativity from a systems perspective: CRESY. Journal of Artificial Societies and Social Simulation, 18(1). doi.org/10.18564/jasss.2640
  • Kahn, K. (2015). An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. Physics today, 68(8), 55.[HTML]
  • Kim, J. (2015). An Agent Based Modeling Approach to Exploring Technology Adoption Using MATLAB. Center for Complexity Education, 10.
  • Klaus, G. (2015). What One Can Learn from Extracting OWL Ontologies from a NetLogo Model That Was Not Designed for Such an Exercise. Journal of Artificial Societies and Social Simulation, 18(2), 14.
  • Knoth, L., Arif, A., & Schmitt, J. (2015). Salzburg Residential Mobility Model. GI_Forum,, 2015, 126-135.
  • Kout, A., Labed, S., & Chikhi, S. (2015). Netlogo, agent-based tool for modeling and simulation of routing problem in ad-hoc networks. Proceedings of the Second International Conference on Advances in Information Processing and Communication Technology.
  • Koutiva, I., & Makropoulos, C. (2015, April). Integrating the simulation of domestic water demand behaviour to an urban water model using agent based modelling. In EGU General Assembly Conference Abstracts (p. 6937).
  • Kravari, K. & Bassiliades, N. (2015). A survey of agent platforms. Journal of Artificial Societies and Social Simulation, 18(1). doi.org/10.18564/jasss.2661
  • Krejci, C. & Beamon, B. (2015). Impacts of farmer coordination decisions on food supply chain structure. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2727
  • Landriscina, F. (2015). Simulation and Learning. Springer-Verlag.
  • Lee, J. S., Filatova, T., Ligmann-Zielinska, A., Hassani-Mahmooei, B., Stonedahl, F., Lorscheid, I., Voinov, A., Polhill, G., Sun, Z., & Parker, D. C. (2015). The complexities of agent-based modeling output analysis. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2897
  • Lemos, C., Lopes, R. J., & Coelho, H. (2015, November). Analysis of the decision rule in Epstein's Agent-Based model of civil violence. In 2015 Third World Conference on Complex Systems (WCCS) (pp. 1-6). IEEE.
  • Le Page, C., Abrami, G., Becu, N., Bommel, P., Bonté, B., Bousquet, F., Gaudou, B., Müller, J.P. and Taillandier, P., 2015, September. Multi-platform training sessions to teach agent-based simulation. In Conference on Complex Systems, Tempe, Etats-Unis (Vol. 28).
  • Le, Q. B. (2015). Manual for Computer Lab Practice on Multi-Agent System (MAS) for Simulating Coupled Community-Landscape System Dynamics.
  • LI, R., GAO, H. W., SONG, L., SUN, L. P., & WANG, L. (2015). Coordination Game with Non-exclusive Convention on the Ring Network and the Study. Journal of Qingdao University (Natural Science Edition), 1, 004. [HTML]
  • Liu, H., Silva, E. A., & Wang, Q. (2015). Simulating the Dynamics of Creative Industries’ Interactions with Urban Land Use by Agent-Based Modelling. In Creative Industries and Urban Spatial Structure (pp. 101-133). Springer, Cham.
  • Liu, Z., Cabrera, E., Taboada, M., Epelde, F., Rexachs, D., & Luque, E. (2015). Quantitative evaluation of decision effects in the management of emergency department problems. Procedia Computer Science, 51, 433-442.
  • Lovelace, R., Birkin, M., Ballas, D., & van Leeuwen, E. (2015). Evaluating the performance of iterative proportional fitting for spatial microsimulation: New tests for an established technique. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2768
  • Lungeanu, A., Sullivan, S., Wilensky, U., & Contractor, N.S. (2015). A computational model of team assembly in emerging scientific fields. In L. Yilmaz, W.K.V. Chan, I. Moon, T.M.K. Roeder, C. Macal, & M.D. Rossetti (Eds.). Proceedings of the 2015 Winter Simulation Conference.
  • MA, Y., & LI, Y. (2015). Conceptual Research on Decision Making Meetings for Urban Water Management. International Review for Spatial Planning and Sustainable Development, 3(3), 16-24.[HTML]
  • Malchow, M., van Schaik, L., & Tietjen, B. (2015, April). Modelling the influence of plants on the spatial heterogeneity of soil water. In EGU General Assembly Conference Abstracts (p. 13583).
  • Malik, A., Crooks, A., Root, H., & Swartz, M. (2015). Exploring creativity and urban development with agent-based modeling. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2722
  • Malone, K., Schuchardt, A., & Schunn, C. (2015). Scalable approaches to modeling and engineering in high school biology. In National Association of Research in Science Teaching.
  • Manzanarez-Ozuna, E., Flores, D., Gómez-Gutiérrez, C. M., Abaroa, A., Castro, C., & Castañeda-Martínez, R. (2015). Computational modeling of the MAPK pathway using NetLogo. Lat. Am. J. Appl. Eng, 1, 11-17.
  • Manzo, G., & Baldassarri, D. (2015).Heuristics, Interactions, and Status Hierarchies An Agent-based Model of Deference Exchange. Sociological Methods Research 44(2), 329-387. [HTML]
  • Marsden, T., Blakely, R. I., Gegear, R. J., & Ryder, E. F. (2015). From individuals to populations: Using an agent-based modelling approach to understand mechanisms of pollinator decline. Swarmfest 2015: Agent-based Simulation and the Study of Complexity, University of South Carolina, Columbia, SC, United States.
  • Mayfield, J., & Mayfield, M. (2015). The diffusion process of strategic motivating language. In Academy of Management Proceedings (Vol. 2015, No. 1, p. 13723). Briarcliff Manor, NY 10510: Academy of Management.
  • Mayrhofer, C. (2015). Performance, Scale & Time in Agent-based Traffic Modelling with NetLogo. GI_Forum, 2015, 567-570.[HTML]
  • McPhee-Knowles, S. (2015). Growing food safety from the bottom up: An agent-based model of food safety inspections. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2717
  • Mezencev, K. N. (2015). Multi-agent simulation in netlogo software. Autom. Control Tech. Syst, 1, 10-20.
  • Micu, L. A., & Ciutacu, I. (2015). EU Vs. China: Is Agriculture the Way towards Sustainability? Case Study Using Agent-based Models. Procedia Economics and Finance, 27, 607-611. [HTML]
  • Mitchell, D., Keller, J. M., & Castillo, V. M. (2015, June). Saving Rivertown: Using Computer Simulations in an Earth Science Engineering Design Project for Pre-service Teachers. In 2015 ASEE Annual Conference & Exposition (pp. 26-1355).
  • Miyoshi, K., Jibiki, M., & Murase, T. (2015, July). Self-Organization of Shortest Spanning Tree and Optimal Sink Node Position for Various Shapes of Large-Scale Wireless Sensor Networks. In 2015 IEEE 39th Annual Computer Software and Applications Conference (Vol. 2, pp. 647-652). IEEE.
  • Momen, S. and Tabassum, K.T. (2015). Group Performance in a Swarm of Simulated Mobile Robots, ULAB Journal of Science and Engineering, vol 6, no. 1, pp: 25 - 31, ISSN: 2079-4398 (print), ISSN: 2414-102X (online)[HTML]
  • Moon, S., & Han, Y. (2015). An Agent Based Model for the Study on Undergraduate’s Choice of Seat and Seat Distribution.[PDF]
  • Munteanu, A. C. (2015). Knowledge Spillovers of FDI. Procedia Economics and Finance, 32, 1093-1099. [HTML]
  • Mustafee, N., Sahnoun, M. H., Smart, A., & Godsiff, P. (2015, June). An application of distributed simulation for hybrid modeling of offshore wind farms. In Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (pp. 171-172).
  • Nolting, B.C., Hinkelman, T.M., Brassil, C.E., & Tenhumberg, B. (2015).Composite random search strategies based on non-directional sensory cues. Ecological Complexity 22, 126-138. [HTML]
  • Olsen, J., & Jørgensen, T. R. (2015). Revisiting the cost-effectiveness of universal HPV-vaccination in Denmark accounting for all potentially vaccine preventable HPV-related diseases in males and females. Cost Effectiveness and Resource Allocation, 13(1), 1-10.
  • Olšvičová, K., Procházka, J., & Danielisová, A. (2015). Reconstruction of Prehistoric Settlement Network Using Agent-Based Model in NetLogo. In Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability-The PAAMS Collection (pp. 165-175). Springer International Publishing.[HTML]
  • Patarakin, E., Burov, V., & Parfenov, R. (2015, November). Identifying sets of key players and cliques in socio-educational co-creative projects. In Proceedings of the 2015 2nd International Conference on Electronic Governance and Open Society: Challenges in Eurasia (pp. 232-236).
  • Pereda, M., Poza, D., Santos, J. I., & Galán, J. M. (2015). Quality Uncertainty and Market Failure: An Interactive Model to Conduct Classroom Experiments. In International Joint Conference (pp. 549-557). Springer International Publishing.[HTML]
  • Pope, A. J., & Gimblett, R. (2015). Linking Bayesian and agent-based models to simulate complex social-ecological systems in semi-arid regions. Frontiers in Environmental Science, 3, 55.[HTML]
  • Priest B., Vuksani E., Wagner N., Tello B., Carter K., and Streilein W.(2015). Agent-Based Simulation in Support of Moving Target Cyber Defense Technology Development and Evaluation. in Proceedings of the 2015 ACM Spring Simulation Multi-Conference - Communications and Networking Simulation Symposium, Alexandria, VA, April, 2015. [HTML]
  • Polhill, G. (2015). Extracting OWL Ontologies from Agent-Based Models: A NetLogo Extension. Journal of Artificial Societies and Social Simulation, 18(2), 15. [HTML]
  • Procházka, J., Cimler, R., & Olševičová, K. (2015). Pedestrian Modelling in NetLogo. In Emergent Trends in Robotics and Intelligent Systems (pp. 303-312). Springer International Publishing. [HTML]
  • Procházka, J., & Olševičová, K. (2015). Monitoring Lane Formation of Pedestrians: Emergence and Entropy. In Intelligent Information and Database Systems (pp. 221-228). Springer International Publishing. [HTML]
  • Ramli, N. R., Razali, S., & Osman, M. (2015, August). An overview of simulation software for non-experts to perform multi-robot experiments. In Agents, Multi-Agent Systems and Robotics (ISAMSR), 2015 International Symposium on (pp. 77-82). IEEE. [HTML]
  • Ronquillo, A. O., Doloriel, D. M., & Doloriel, N. S. (2015). RATE OF NEWCASTLE DISEASE SPREAD AMONG CHICKENS: COMPARATIVE SIMULATION EXPERIMENT. SDSSU MULTIDISCIPLINARY RESEARCH JOURNAL, 3, 110-113.[PDF]
  • Rorabaugh, A. (2015). Modeling pre-European contact Coast Salish seasonal social networks and their impacts on unbiased cultural transmission. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2911
  • Rossiter, S. (2015). Simulation design: Trans-paradigm best-practice from software engineering. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2842
  • Rotenberry, J.T., Swanger, E., & Zuk, M. (2015).Alternative reproductive tactics arising from a continuous behavioral trait: callers versus satellites in field crickets. American Naturalist 185:469-490. DOI: 10.1086/680219. [HTML]
  • Ryder, E. F., Boyd, J. R., Marsden, T. B., La, S., Sullender, M. E., Reider, D., & White, B. T. (2015). Modeling from molecules to moose: Teaching students to develop agent-based simulations in biology. Swarmfest 2015: Agent-based Simulation and the Study of Complexity, University of South Carolina, Columbia, SC, United States.
  • Scherer, S., Wimmer, M., Lotzmann, U., Moss, S., & Pinotti, D. (2015). Evidence based and conceptual model driven approach for agent-based policy modelling. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2834
  • Sengupta, P., Dickes, A., Farris, A. V., Karan, A., Martin, D., & Wright, M. (2015). Programming in K-12 science classrooms. Communications of the ACM, 58(11), 33-35. [PDF]
  • Sirer, M. I., Maroulis, S., Guimera, R., Wilensky, U., & Amaral, L. A. N. (2015). The currents beneath the “rising tide” of school choice: An analysis of student enrollment flows in the Chicago public schools. Journal of Policy Analysis and Management, 34(2), 358-377.[PDF]
  • Shook, E., Wren, C., Marean, C. W., Potts, A. J., Franklin, J., Engelbrecht, F., ... & Esler, K. J. (2015, July). Paleoscape model of coastal South Africa during modern human origins: progress in scaling and coupling climate, vegetation, and agent-based models on XSEDE. In Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure (p. 2). ACM. [PDF]
  • Shutters, S. T. & Hales, D. (2015). Altruism displays a harmonic signature in structured societies. Journal of Artificial Societies and Social Simulation, 18(3). doi.org/10.18564/jasss.2780
  • Siddique, O. (2015). Steven F. Railsback and Volker Grimm. Agent-Based and Individual-Based Modelling: A Practical Introduction. Pakistan Development Review, 54(1), 76-77. [HTML]
  • Singh, M., & Balaraman, V. (2015).Exploring Norm Establishment and Spread in Different Organizational Structures Using an Extended Axelrod Model. Spring Sim 15, Proceedings of the 2015 Spring Sim Simulation Multiconference
  • Simpson, O., & Camorlinga, S. (2015, April). A methodology to create Complex Adaptive System models that support Cardiovascular Diseases simulation. In 2015 Annual IEEE Systems Conference (SysCon) Proceedings (pp. 224-229). IEEE.
  • Takács, K. & Squazzoni, F. (2015). High standards enhance inequality in idealized labor markets. Journal of Artificial Societies and Social Simulation, 18(4). doi.org/10.18564/jasss.2940
  • TANG, H., XIONG, S., & JIN, Z. (2015). Research for Simulation of Rescue Behaviors of Industrial Accidents Based on Communication of Virtual Human. Industrial Safety and Environmental Protection, 01.
  • Terna, P. (2015). Agent-based Models for Exploring Social Complexity, with an Application of Network Analysis to Agents. In SIMULTECH (pp. IS-11).
  • Tovar, R. J. C., Fernández, O., & Medina, L. J. C. (2015). Dialéctica entre teorías y ciencias de la complejidad. Un Acercamiento a Través del Análisis Crítico. Entelequia: revista interdisciplinar, (18), 137-142.
  • Trab, S., Bajic, E., Zouinkhi, A., Abdelkrim, M. N., Chekir, H., & Ltaief, R. H. (2015). Product Allocation Planning with Safety Compatibility Constraints in IoT-based Warehouse. Procedia Computer Science, 73, 290-297. [HTML]
  • Troitzsch, K. G. (2015). What One Can Learn from Extracting OWL Ontologies from a NetLogo Model That Was Not Designed for Such an Exercise. Journal of Artificial Societies and Social Simulation, 18(2), 14. [HTML]
  • Uemura, M., Matsushita, H., & Kraetzschmar, G. K. (2015, November). Path Planning with Slime Molds: A Biology-Inspired Approach. In Neural Information Processing (pp. 308-315). Springer International Publishing.[HTML]
  • Varela, C. A. R., Velandia, F. B., Rey, M. A. M., Romero, N. G., & Neira, N. O. (2015). Foraging Multi-Agent System Simulation Based on Attachment Theory. In ISCS 2014: Interdisciplinary Symposium on Complex Systems (pp. 359-364). Springer International Publishing. [HTML]
  • Wang, H., Mostafizi, A., Cramer, L. A., Cox, D., & Park, H. (2015). An agent-based model of a multimodal near-field tsunami evacuation: Decision-making and life safety. Transportation Research Part C: Emerging Technologies.[HTML]
  • Wang, Y. N., & Chen, H. (2015). Scenario Simulation of Land Use Based on Net Logo Model—A Case Study for Matiwa Village of Mizhi County of Shaanxi Province. Journal of Anhui Agricultural Sciences, 25, 111. [HTML]
  • Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2015).Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127–147.
  • Weintrop, D., Head, B., & Wilensky, U. (2015).Plotting Programming Trajectories with the NetLogo Data Explorer. In Proceedings of Information Visualization, 2015. Chicago, IL. IEEE.
  • Wilensky, U. & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social and engineered complex systems with NetLogo. Cambridge, MA: MIT Press.
  • Wilkerson-Jerde, M. H., Wagh, A. & Wilensky, U. (2015).Balancing curricular and pedagogical needs in computational construction kits: Lessons from the DeltaTick project. Science Education, 99(3), 465-499. [HTML]
  • Wilkerson-Jerde, M. H. & Wilensky, U. (2015).Patterns, probabilities, and people: Making sense of quantitative change in complex systems. Journal of the Learning Sciences, 24(2), 204-251. doi: 10.1080/10508406.2014.976647
  • Weiss, J., & Steel, J. (2015). Continuing with agent based models to aid decision making in weed management. A case study of buffel grass; a weed dispersed by vehicular wind turbulence.
  • Wirth, E. (2015). Pi from agent border crossings by NetLogo package. Wolfram Library Archive.
  • Wrasse, K., Hayka, H., & Stark, R. (2015). Simulation of product-service-systems piloting with agent-based models (outlined revision). Procedia CIRP, 30, 108-113.
  • Wright, M. & Sengupta, P. (2015). Modeling oligarchs' campaign donations and ideological preferences with simulated agent-based spatial elections. Journal of Artificial Societies and Social Simulation, 18(2). doi.org/10.18564/jasss.2736
  • Wurzer, G., Kowarik, K., & Reschreiter, H. (Eds.). (2015). Agent-based modeling and simulation in archaeology. New York: Springer International Publishing.
  • Yongchen, G., Yang, S., & Jing, M. (2015). A Study on the Multi-Agent Simulation of BBS Public Opinion Evolution Based on the Theory of Opinion Leaders. Journal of Intelligence, 2, 003.[HTML]
  • Zandi, M., & Mohebbi, M. (2015). An agent‐based simulation of a release process for encapsulated flavour using the NetLogo platform. Flavour and Fragrance Journal, 30(3), 224-229. [HTML]
  • Zandi, M. (2015). Simulation of Ascorbic Acid Release from Alginate‐Whey Protein Concentrates Microspheres at the Simulated Gastrointestinal Condition Using Netlogo Platform. Journal of Food Process Engineering. [HTML]

2014

  • Alden, K., Timmis, J., & Coles, M. (2014). Easing Parameter Sensitivity Analysis of Netlogo Simulations using SPARTAN. ALIFE 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems. [HTML]
  • Ampatzidis, G., & Ergazaki, M. (2014). Towards a learning environment for challenging the idea of the balanced nature: Insights from the first cycle of research. In C. P. Constantinou, N. Papadouris & A. Hadjigeorgiou (Eds.), E-Book. Proceedings of the ESERA 2013 Conference: Science Education Research For Evidence-based Teaching and Coherence in Learning. Part 3 (pp. 44-54). Nicosia, Cyprus: European Science Education Research Association. [HTML]
  • Balaraman, V., & Singh, M. (2014). Exploring Norm Establishment in Organizations Using an Extended Axelrod Model with Two New Metanorms. SummerSim '14, Proceedings of the 2014 Summer Simulation Multiconference, Article No. 39.
  • Becher, M. A., Grimm, V., Thorbek, P., Horn, J., Kennedy, P. J., & Osborne, J. L. (2014). BEEHAVE: a systems model of honeybee colony dynamics and foraging to explore multifactorial causes of colony failure. Journal of applied ecology, 51(2), 470-482.[HTML]
  • Biotechnology and Biological Sciences Research Council. (2014). Virtual bees help to unravel complex causes of colony decline. ScienceDaily. [HTML]
  • Brady, C., Holbert, N. Soylu, F., Novak, M., Wilensky, U. (2014). Sandboxes for model-based inquiry. Science Teaching and Learning with Models,” Journal of Science Education and Technology (JOST) [Special Issue].
  • Buttò, M., Pereira, C., & Taylor, M. (2014). Sunshine or shield? Secret voting procedures and legislative accountability. Journal of Artificial Science and Social Simulation, 17(4). doi.org/10.18564/jasss.2620
  • Dickerson, M. (2014). Multi-agent simulation, netlogo, and the recruitment of computer science majors. Journal of Computing Sciences in Colleges, 30(1), 131-139. [HTML]
  • Epstein, J.M. (2014). Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science. New Jersey: Princeton University Press. [HTML]
  • Galic, N., R. Ashauer, H. Baveco, A.-M. Nyman, A. Barsi, P. Thorbek, E. Bruns, & P. J. Van den Brink. (2014). Modeling the contribution of toxicokinetic and toxicodynamic processes to the recovery of Gammarus pulex populations after exposure to pesticides. Environmental Toxicology and Chemistry 33:1476-1488.
  • Gkiolmas A., Papaconstantinou M., Chalkidis A., & Skordoulis C. (2014). "Learning about Populations in Ecosystems by “Building Them From Inside” with NetLogo: A Constructionist Approach for Teaching Population Ecology’s Principles. Proceedings of the Constructionism. Vienna, Austria [PDF]
  • Gooding, T. (2014). Modelling society's evolutionary forces. Journal of Artificial Science and Social Simulation, 17(3). doi.org/10.18564/jasss.2497
  • Guo, Y., & Wilensky, U. (2014). Beesmart: a microworld for swarming behavior and for learning complex systems concepts. Proceedings of the Constructionism 2014 Conference. Vienna, Austria. August 2014. [PDF]
  • Head, B., Liang, C., & Wilensky, U. (2014). Flying like a School of Fish: Discovering Flocking Formations in an Agent-Based Model with Analogical Reasoning. In Proceedings of the Michigan Complexity Mini-Conference. University of Michigan, Ann Arbor, Michigan.
  • Head, B., Orton, K., & Wilensky, U. (2014). An Agent-Based Approach to Modeling Membrane Formation. In Proceedings of the Michigan Complexity Mini-Conference. University of Michigan, Ann Arbor, Michigan.
  • Heijnen, P., Chappin, É. J. L., & Nikolic, I. (2014). Infrastructure network design with a multi-model approach: Comparing geometric graph theory with an agent-based implementation of an ant colony optimization. Journal of Artificial Science and Social Simulation, 17(4). doi.org/10.18564/jasss.2533
  • Hjorth, A., & Wilensky, U. (2014). Redesigning Your City - A Constructionist Environment for Urban Planning Education. Proceedings of Constructionism 2014, Vienna, Aug 19-23.
  • Hjorth, A., & Wilensky, U. (2014). Redesigning Your City – A Constructionist Environment for Urban Planning Education. Informatics in Education-An International Journal, (Vol13_2), 197-208. Chicago
  • Hjorth, A. & Wilensky, U. (2014). Re-grow Your City – a NetLogo curriculum unit on Regional Development. In J. L. Polman, E. A. Kyza, D. K. O'Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O'Connor, T. Lee & L. D'Amico (Eds.), Proceedings of "Learning and Becoming in Practice," the 11th International Conference of the Learning Sciences (ICLS) 2014 (Vol. 3, pp. 1553-1555). Boulder, CO: International Society of the Learning Sciences.
  • Hjorth, A., Wilensky, U., Villamar, J., Brown, H. (2014). Using Agent-Based Modeling to Explore and Visualize the Effects of Prevention Implementation Strategies for Policy. In Computational and Technical Approaches to Improve the Implementation of Prevention Programs. Panel chaired by Dr. Hendricks Brown at 7th Annual Conference on the Science of Dissemination and Implementation. Bethesda, MD.
  • Horn, M., Brady, C., Hjorth, A., Wagh, A., & Wilensky, U. (2014). Frog Pond: A code first learning environment on natural selection and evolution. Proceedings of IDC 2014.
  • Horn, M.S., Weintrop, D., & Routman, E. (2014). Programming in the Pond: A Tabletop Computer Programming Exhibit. In Proceedings of the Extended Abstracts of the 32nd Annual ACM Conference on Human Factors in Computing Systems (pp. 1417-1422). New York, NY, USA: ACM.
  • Iwamura, T., Lambin, F.E., Silvius, M.K., Luzar, B.J & Fragoso, MV.J. (2014). Agent-based modeling of hunting and subsistence agriculture on indigenous lands: Understanding interactions between social and ecological systems. Environmental Modelling and Software 58(2014)109-127 [HTML]
  • Izquierdo, L.R., Izquierdo, S.S. & Vega-Redondo, F. (2014). Leave and let leave: A sufficient condition to explain the evolutionary emergence of cooperation. Journal of Economic Dynamics & Control 46, pp. 91–113. [PDF]
  • Jona, K., Wilensky, U., Trouille, L., Horn, M. S., Orton, K., Weintrop, D., & Beheshti, E. (2014). Embedding Computational Thinking in Science, Technology, Engineering, and Math (CT-STEM). Presented at the 2014 CE21 PI and Community Meeting, Orlando, FL.
  • Kautz, M., Muhammad, A.I., & Schopf, R. (2014). Individual traits as drivers of spatial dispersal and infestation patterns in a host-bark beetle system. Ecological Modelling, 273, 264-276. [HTML]
  • Knoeri, C., Nikolic, I., Althaus, H. J., & Binder, C. R. (2014). Enhancing recycling of construction materials: An agent based model with empirically based decision parameters. Journal of Artificial Science and Social Simulation, 17(3). doi.org/10.18564/jasss.2528
  • Labarbe, E., & Thiel, D. (2014). Information Sharing to Reduce Misperceptions of Interactions Among Complementary Projects: A Multi-Agent Approach. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 9. [HTML] (Jan 2014)

  • Lee, K., Lee, H., & Kim, C.O. (2014). Pricing and Timing Strategies for New Product Using Agent-Based Simulation of Behavioural Consumers. Journal of Artificial Societies and Social Simulation (JASSS), 17 (2): 1. [HTML] (March 2014)
  • Lee, T., Yao, R., & Coker, P. (2014). An analysis of UK policies for domestic energy reduction using an agent based tool. Energy Policy, 66, 267-279. [HTML] (March 2014)
  • León, F.J., Miguel, F.J., & Alcaide, V. (2014). The Production of Step-Level Public Goods in Structured Social Networks: An Agent-Based Simulation. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 4. [HTML] (Jan 2014)
  • Levin, J. A., Jacobson, M. J., & Markauskaite, L. (2014). Combining computational modeling, theory, and data: Steps toward a meta-model framework for the study of learning. Paper presented at the 2014 American Educational Research Association meetings. Philadelphia, PA. [HTML]
  • Lim, D., Lee, H., Zo, H., & Ciganek, A. (2014). Opinion Formation in the Digital Divide. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 13. [PDF] (Jan 2014)
  • Lucas, P., de Oliveira, A. C. M., & Banuri, S. (2014). The effects of group composition and social preference heterogeneity in a public goods game: An agent-based simulation. Journal of Artificial Science and Social Simulation, 17(3). doi.org/10.18564/jasss.2522
  • Lynch, S.C., & Ferguson, J. (2014). Reasoning about Complexity – Software Models as External Representations. Proceedings of the 25th Workshop of The Psychology of Programming Interest Group, Brighton, UK. June 2014. [PDF]
  • Maldos, J.P.A., & de Figueiredo, J.C.B. (2014). Projeto de Iniciação Científica: O Uso de Programação Multiagente no Estudo da Difusão de Inovações Tecnológicas. [HTML]
  • Maroulis, S., Bakshy, E., Gomez, L. & Wilensky, U. (2014). Modeling the Transition to Public School Choice. Journal of Artificial Societies and Social Simulation. /li>
  • Maroulis, S & Wilensky, U. (2014). Social and Task Interdependencies in the Street-Level Implementation of Innovation. Journal of Public Administration Research and Theory.
  • Medina, F. J. L., Quesada, F. J. M., & Lozano, V. A. (2014). The production of step-level public goods in structured social networks: An agent-based simulation. Journal of Artificial Science and Social Simulation, 17(1). doi.org/10.18564/jasss.2419
  • Nissen, V. & Saft, D. (2014). A practical guide for the creation of random number sequences from aggregated correlation data for multi-agent simulations. Journal of Artificial Science and Social Simulation, 17(4). doi.org/10.18564/jasss.2593
  • Oremland, M., & Laubenbacher, R. (2014). Optimization of Agent-Based Models: Scaling Methods and Heuristic Algorithms. Journal of Artificial Societies and Social Simulation (JASSS), 17 (2): 6. [HTML] (March 2014)
  • Pluchino, A., Garofalo, C., Inturri, G., Rapisarda, A., & Ignaccolo, M. (2014). Agent-Based Simulation of Pedestrian Behaviour in Closed Spaces: A Museum Case Study. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 16. [HTML] (Jan 2014)
  • Rhee, J.M. (2014). Promoting Convergence: The Phi Spiral in Abduction of Mouse Corneal Behaviors. In Complexity. [HTML]
  • Salgado, M., Marchione, E., & Gilbert, N. (2014). Analysing differential school effectiveness through multilevel and agent-based modelling. Journal of Artificial Science and Social Simulation, 17(4). doi.org/10.18564/jasss.2534
  • Shaker, N., Togelius, J., & Nelson, M.J. (2014). Procedural Content Generation in Games: A textbook and an overview of current research. Springer Publishing. [HTML]
  • Sie, R., Sloep, P.B., & Bitter-Rijpkema, M. (2014). If We Work Together, I Will Have Greater Power: Coalitions in Networked Innovation. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 3. [HTML] (Jan 2014)
  • Soylu, F., Brady, C., Holbert, N., Wilensky, U. (2014). The thinking hand: Embodiment of tool use, social cognition and metaphorical thinking and implications for learning design. Paper presented at the AERA Annual Meeting (SIG: Brain, Neurosciences, and Education), Philadelphia, PA: April, 2014
  • Stoica, V. I. & Flache, A. (2014). From Schelling to schools: A comparison of a model of residential segregation with a model of school segregation. Journal of Artificial Science and Social Simulation, 17(1). doi.org/10.18564/jasss.2342
  • Stroup, W., & Wilensky, U. (2014). On the Embedded Complementarity of Agent-Based and Aggregate Reasoning in Students' Developing Understanding of Dynamic Systems. Technology, Knowledge and Learning, 19(1-2).
  • Szilagyi, M.N. (2014). Solution of partial differential equations by agent-based simulation. European Journal of Physics 35, 018003, 1-4.
  • Thiele, J.C., Kurth, W., Grimm, V. (2014). Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and R. Journal of Artificial Societies and Social Simulation, 17 (3).[HTML]
  • Wagh, A. & Wilensky, U. (2014). EvoBuild: Programming models of evolutionary change using blocks. Poster presented at the 2014 Annual Meeting of the AERA, Philadelphia.
  • vWagh, A. & Wilensky, U. (2014). Seeing patterns of change: Supporting student noticing in building models of natural selection. Proceedings of 2014 Constructionism, Vienna, Aug 19-23.
  • Wilensky, U. (2014). Computational Thinking through Modeling and Simulation. Whitepaper presented at the summit on Future Directions in Computer Education. Orlando, FL. Jan 8-9, 2014. [PDF]
  • Wilensky, Brady & Horn (2014). Fostering Computational Literacy in Science Classrooms. Communications of the ACM.
  • Wilensky, U. & Jacobson, M. (In press). Complex Systems in the Learning Sciences. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd Edition). Cambridge, UK: Cambridge University Press.
  • Wilkerson-Jerde, M. H. & Wilensky, U. (2014). Patterns, probabilities, and people: Making sense of quantitative change in complex systems. Online First in Journal of the Learning Sciences. doi:10.1080/10508406.2014.976647[HTML]
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  • Zhang, H., & Li, Y. (2014). Agent-Based Simulation of the Search Behavior in China's Resale Housing Market: Evidence from Beijing. Journal of Artificial Societies and Social Simulation (JASSS), 17 (1): 18. [HTML] (Jan 2014)

2013

  • Abdollahian, M., Yang, Z., & Nelson H. (2013). Techno-Social Energy Infrastructure Siting: Sustainable Energy Modeling Programming (SEMPro). Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 6. [HTML] (June 2013)
  • Alaliyat, S., Osen, O. L., & Kvile, K. O. (2013). An Agent-Based Model To Simulate Pathogen Transmission Between Aquaculture Sites In The Romsdalsfjord. In ECMS (pp. 46-52).
  • Almudí, I., Fatás-Villafranca, F. & Izquierdo, L.R. (2013).Industry dynamics, technological regimes and the role of demand. Journal of Evolutionary Economics Vol. 23, Issue 5, pp 1073-1098. [PDF]
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  • Anghinolfi, D., Capogrosso, A., Paolucci, M., & Perra, F. (2013, October). An agent-based simulator for the evaluation of the measurement of effectiveness in the military naval tasks. In 2013 17th International Conference on System Theory, Control and Computing (ICSTCC) (pp. 733-738). IEEE.
  • Ariuntsetseg, E., & Yom, J. H. (2013). Foot-and-mouth disease spread simulation using agent-based spatial model. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 31(3), 209-219.
  • Aroor, A. (2013). QoS aware distributed service composition using agents (Doctoral dissertation, Dhirubhai Ambani Institute of Information and Communication Technology).
  • Arslan, M.O. & İcan, Ö. (2013). "An Agent-Based Analysis of Tax Compliance for Turkey." Anadolu University Journal of Social Sciences, 13(2), 143-152. [PDF]
  • Arslan, M.O. & İcan, Ö. (2013). "The Effects of Neighborhood on Tax Compliance Rates: Evidence from an Agent-Based Model." Journal of Cukurova University Institute of Social Sciences, 22(1), 337-350. [PDF]
  • Bajracharya, K., & Duboz, R. (2013, April). Comparison of three agent-based platforms on the basis of a simple epidemiological model (WIP). In Proceedings of the Symposium on Theory of Modeling & Simulation-DEVS Integrative M&S Symposium (pp. 1-6). [PDF]
  • Balbi, S., Giupponi, C., Perez, P., & Alberti, M. (2013). A spatial agent-based model for assessing strategies of adaptation to climate and tourism demand changes in an alpine tourism destination. Environmental Modelling & Software, 45, 29-51.
  • Banos, A., Marilleau, N., & MIRO Team. (2013). Improving individual accessibility to the city. In Proceedings of the European Conference on Complex Systems 2012 (pp. 989-992). Springer, Cham.
  • Basu, S., Dickes, A., Kinnebrew, J. S., Sengupta, P., & Biswas, G. (2013, May). CTSiM: A Computational Thinking Environment for Learning Science through Simulation and Modeling. In CSEDU (pp. 369-378).
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  • Bhattacharya, S., Czejdo, B., Malhotra, R., Perez, N., & Agrawal, R. (2013, July). Agent based modeling of moving point objects in geospatial data. In 2013 Fourth International Conference on Computing for Geospatial Research and Application (pp. 132-133). IEEE.
  • Bhattacharya, S., Czejdo, B., Malhotra, R., Perez, N., & Agrawal, R. (2013, July). Characterization of Moving Point Objects in Geospatial Data. In 2013 Fourth International Conference on Computing for Geospatial Research and Application (pp. 151-151). IEEE.
  • Bichraoui, N., Guillaume, B., & Halog, A. (2013). Agent-based modelling simulation for the development of an industrial symbiosis-preliminary results. Procedia Environmental Sciences, 17, 195-204.
  • Biggs, M. B., & Papin, J. A. (2013). Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation. PLoS One, 8(10), e78011.
  • Bintoro, B. P. K. (2013). Model Berbasis Agen untuk Pengenalan Produk Baru dengan Twitter. Business and Management Review, 2(2).
  • Bintoro, B. P. K., Epicentrum, K. K. K. R., & Jie, F. (2013). AGENT-BASED MODELING FOR NEW PRODUCT INTRODUCTION USING TWITTER. In International DSI and ASIA Pacific DSI 2013 Bali Conference (pp. 1-11). Decision Sciences Institute.
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  • Boone, Randall B., Moore, John C., Koyama, Akihiro, Holfelder, Kirstin (2013, November 21). "Soil microbe-predator model with enzymes" (Version 1). CoMSES Computational Model Library.
  • Borsboom, D, & Cramer, A. O. (2013).Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121.[HTML]
  • Bravo, R. and D.E. Axelrod. A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments. Theoretical Biology and Medical Modeling 10:66 (2013), DOI: 10.1186/1742-4682-10-66, [HTML]
  • Bredeche, N., Montanier, J. M., Weel, B., & Haasdijk, E. (2013). Roborobo! a fast robot simulator for swarm and collective robotics. arXiv preprint arXiv:1304.2888.
  • Brooks, M. (2013). Facilitating the creation of advanced agents within netlogo by allowing specification and control using the behaviour oriented design methodology.
  • Buchin, K., Buchin, M., van Kreveld, M., Speckmann, B., & Staals, F. (2013, August). Trajectory grouping structure. In Workshop on Algorithms and Data Structures (pp. 219-230). Springer, Berlin, Heidelberg.
  • Cabreira, T. M., de Aguiar, M. S., & Dimuro, G. P. (2013, June). An extended evolutionary learning approach for multiple robot path planning in a multi-agent environment. In 2013 IEEE Congress on Evolutionary Computation (pp. 3363-3370). IEEE.
  • Caillou, P., Gil-Quijano, J., & Zhou, X. (2013). Automated observation of multi-agent based simulations: a statistical analysis approach.
  • Carrasco-Jiménez, J. C., Celaya-Padilla, J. M., Montes, G., Brena, R. F., & Iglesias, S. (2013, June). Social interaction discovery: A simulated multiagent approach. In Mexican Conference on Pattern Recognition (pp. 294-303). Springer, Berlin, Heidelberg.
  • Cecconi, F. (2013). Simulating Crime: Models, Methods, Tools. Informatica e diritto, 22(1), 181-191.
  • Čech, P., Tučník, P., Bureš, V., & Husráková, M. (2013, September). Modelling complexity of economic system with multi-agent systems. In 5th International Conference on Knowledge Management and Information Sharing (KMIS 13), Vilamoura, Algarve, Portugal (pp. 464-469).
  • Chang-hong, L., Feng, L., Jian-ping, F., & Zhi-hong, S. (2013). Research on Selections and Influencing Factors of Industry-university-research Cooperation Models in Developing New Products. Sci-tech Innovation and Productivity, 2.
  • Chen, P., Plale, B., & Evans, T. (2013, October). Dependency provenance in agent based modeling. In 2013 IEEE 9th International Conference on e-Science (pp. 180-187). IEEE.
  • Chen, P., Zhu, S. Y., Xu, L. J., Ma, X. F., & Du, Z. G. (2013). Multi-agent simulation of emergency evacuation on the sidewalk. In Applied Mechanics and Materials (Vol. 253, pp. 2005-2008). Trans Tech Publications Ltd.
  • CHI, J. J., LUO, X. M., & SUN, X. N. (2013). Simulation & Evaluation of Complex Electromagnetic Environment Effect on Radar Network" Four Countering" Capabilities. Journal of Academy of Armored Force Engineering, (1), 16.
  • Ciancamerla, E., Minichino, M., & Palmieri, S. (2013, July). Modeling cyber attacks on a critical infrastructure scenario. In IISA 2013 (pp. 1-6). IEEE.
  • Cimler, R. (2013). Analytic Hierarchy Process and agent-based simulation for traffic modeling. In Proceedings of the 12th International Symposium on the Analytic Hierarchy Process, Kuala Lumpur, Malaysia, 23rd-26th June.
  • Cimler, R., & Olševičová, K. (2013). Analysis Simulation of aircraft disembarking methods. Global Journal on Technology, 3.
  • Collard, P., Mesmoudi, S., Ghetiu, T., & Polack, F. (2013).Emergence of Frontiers in Networked Schelling Segregationist Models. Journal Complex Systems, 22(1). [HTML]
  • Collard, P., Verel, S. & Clergue, M. (2013).Systèmes Complexes: une introduction par la pratique [Complex Systems: An Introduction to Practice]. Presses Polytechniques et Universitaires Romandes. Publisher of the EPFL Press, Lausanne [France], 306 p. (ISBN: 978-2-88074-991-0)
  • Comer, K. W., & Loerch, A. G. (2013). The impact of agent activation on population behavior in an agent-based model of civil revolt. Procedia Computer Science, 20, 183-188.
  • Curtin, T., Donnelly, M., Maynard, G., Nguyen, N., Nourie, A., & Thomas, M. (2013). Can the Great Pacific Garbage Patch be Eliminated With Plastic-eating Microbes?.
  • Dai D, Z. Y. (2013). Simulating fire spread in a community using an agent-based model. In Proceedings of the 12th International Conference on GeoComputation. LIESMARS Wuhan University, Wuhan, China (pp. 130-132).
  • Damaceanu, R. C. (2013). Agent-Based Computational Economics Using NetLogo. Bentham Science Publishers. [HTML]
  • Dangles, O. J. (2013). SimAdapt: An individual-based genetic model for simulating landscape management impacts on populations.
  • Dao, N. B., Revel, A., Menard, M., & El Hamidi, A. (2013). Simulation Models for Grassland Ecosystem and Inter-species Plant Competition: Interation in NetLogo.
  • Davies, B., & Floyd, B. Simulating the effects of prehistoric migration events on body dimensions among Oceanic populations.
  • Decker, W., Cromartie, J., Brandon, D., Warden, R., Ward, M. O., & Ryder, E. F. (2013). Simulation of embryonic development in C. elegans using agent-based modeling, International C. elegans Meeting, University of California, Los Angeles, CA, United States.
  • Delaney, L., Kleczkowski, A., Maharaj, S., Rasmussen, S., & Williams, L. (2013). Reflections on a Virtual Experiment Addressing Human Behavior During Epidemics. Summer Computer Simulation Conference. ACM Digital Library. [HTML]
  • Demarest, J., Pagsuyoin, S., Learmonth, G., Mellor, J., & Dillingham, R. (2013). Development of a Spatial and Temporal Agent-Based Model for Studying Water and Health Relationships: The Case Study of Two Villages in Limpopo, South Africa. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 3. [HTML] (Oct 2013)
  • de Senna Carneiro, T. G., de Andrade, P. R., Câmara, G., Monteiro, A. M. V., & Pereira, R. R. (2013). An extensible toolbox for modeling nature–society interactions. Environmental Modelling & Software, 46, 104-117.
  • de Siqueira Braga, D., Alves, F. O. M., Menezes, L. C. D. S., & de Lima Neto, F. B. (2013, September). Tools for Social Simulation-What Is Missing?. In 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (pp. 651-652). IEEE.
  • De Zeeuw, A., Craig, T., & You, H. S. (2013, October). Assessing conceptual understanding in mathematics. In 2013 IEEE Frontiers in Education Conference (FIE) (pp. 1742-1744). IEEE.
  • Dijkstra, J., & Jessurun, A. J. (2013). Modeling planned and unplanned store stops for the scenario based simulation of pedestrian activity in city centers. In Proceedings of the Fifth International Conference on Advances in System Simulation (SIMUL2013) (Vol. 27, pp. 1-22).
  • Dixit, S. K., & Sachan, D. (2013). A Web Service Selection Model using Cognitive Parameters. International Journal of Computer Applications, 73(21).
  • Dong, S. X., Tang, L. A., & Shao, Z. Z. (2013). Research on Evolution Simulation of Technology Innovation Community in High-tech Zone. In Applied Mechanics and Materials (Vol. 411, pp. 2472-2476). Trans Tech Publications Ltd.
  • Drogoul, A., Amouroux, E., Caillou, P., Gaudou, B., Grignard, A., Marilleau, N., ... & Zucker, J. D. (2013, May). Gama: multi-level and complex environment for agent-based models and simulations. In 12th International Conference on Autonomous agents and multi-agent systems (pp. 2-p). Ifaamas.
  • Duarte Olson, I. (2013). Cultural Differences Between Favela and Asfalto in Complex Systems Thinking. Journal of Cognition and Culture, 13(1-2), 145-157. doi:10.1163/15685373-12342089 [PDF]
  • Duarte Olson, I. (2013). Inequality in Levels: Navigating Everyday Complexity. In Ojalehto,B., Medin, D. Conceptualizing Complex Systems: How People Navigate Social and Ecological Systems. Symposium accepted to the Society for Anthropological Sciences Meeting.
  • Duarte Olson, I. (2013). "It's like an epidemic, it catches on": Community Knowledge of Everyday Complex Phenomena. In C. Lee and G. Saxe. (Co-chairs). Capitalizing on Knowledge Co-Constructed via the Praxis of Historically Nondominant Groups. Symposium accepted to the American Educational Research Association Conference.
  • Duckham, M. (2013). Decentralized Spatial Computing: Foundations of Geosensor Networks.Springer. [HTML]
  • Duckham, M. (2013). Simulating Scalable Decentralized Spatial Algorithms. In Decentralized Spatial Computing (pp. 209-244). Springer, Berlin, Heidelberg.
  • Dykstra, P., Elsenbroich, C., Jager W., de Lavalette, G. R., & Verbrugge, R. (2013). Put Your Money Where Your Mouth Is: DIAL, A Dialogical Model for Opinion Dynamics. Journal of Artificial Societies and Social Simulation (JASSS), 16 (3): 4. [HTML] (June 2013)
  • Ekmekci, O. (2013). INFLUENCE OF INTERPROFESSIONAL EDUCATION ON REDUCING STEREOTYPING AND INCREASING COLLABORATIVE BEHAVIOR IN HEALTH CARE TEAMS. In INTED2013 Proceedings (pp. 3771-3776). IATED.
  • Emílio Almeida, J., Kokkinogenis, Z., & Rossetti, R. J. (2013). NetLogo implementation of an evacuation scenario. arXiv, arXiv-1303.
  • Ferguson, J. (2013, January). Collecting and Managing Multiple Video Data Sources for Visual Semiotic Analysis of Science Students’ Reasoning with Digital Representations. In Proceedings of the 2013 Contemporary Approaches to Research in Mathematics, Science, Health and Environmental Education Symposium (pp. 1-9). Centre for Research in Educational Futures and Innovation, Deakin University.
  • Fioretti, G. (2013). Romulus-Catalin Damaceanu: Agent-based computational economics using netlogo.
  • Flores-Parra, J. M. (2013). Wíinik: Towards an Agent-Based Simulation Design Tool for Distributed Agency and Cognitive Software Agents. In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AA-MAS 2013).
  • Forbey, J. S. (2013). Individual-based modeling for the masses. Ecology, 94(1), 260-262.
  • Fotuhi, F., Huynh, N., Vidal, J. M., & Xie, Y. (2013). Modeling yard crane operators as reinforcement learning agents. Research in transportation economics, 42(1), 3-12.
  • Frank, B.M. & Baret, P.V. (2013). "Simulating brown trout demogenetics in a river/nursery brook system: The individual-based model DemGenTrout." Ecological Modelling, 248, 184-202. [HTML]
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  • Galic, N., G. M. Hengeveld, P. J. Van den Brink, A. Schmolke, P. Thorbek, E. Bruns, & H. M. Baveco. (2013). Persistence of Aquatic Insects across Managed Landscapes: Effects of Landscape Permeability on Re-Colonization and Population Recovery. PLoS ONE 8:e54584.
  • Ghorbani, A., Bots, P., Dignum, V. and Dijkema, G. (2013). MAIA: a Framework for Developing Agent-Based Social Simulations. Journal of Artificial Societies and Social Simulation (JASSS), 16 (2): 9. [HTML]
  • Ghosh, S., Dutta, A., Mascardi, V., & Briola, D. (2013, September). Exploiting MAS-Based Simulation to Improve the Indian Railways’ Efficiency. In German Conference on Multiagent System Technologies (pp. 278-291). Springer, Berlin, Heidelberg.
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  • Ginovart Gisbert, M. (2013). A simple discrete simulation model to explore in the classroom some rules involved in the decision-making process: the cellular automata. Hellenic Mathematical Society International Journal for Mathematics in Education, 5, 3-23.
  • Gkiolmas, A., Karamanos, K., Chalkidis, A., Skordoulis, C., Papaconstantinou, M., & Stavrou, D. (2013). Using Simulations of NetLogo as a Tool for Introducing Greek High-School Students to Eco-Systemic Thinking. Advances In Systems Science and Applications, 13(3), 275-297. [PDF]
  • Grüter, C., Schürch, R., Farina, W.M. (2013). Task-partitioning in insect societies: Non-random direct material transfers affect both colony efficiency and information flow. J. Theor. Biol., 327, 23–33. doi:10.1016/j.jtbi.2013.02.013
  • Guerin, A. J., Jackson, A. C., & Youngson, A. F. (2013). An agent-based model of Atlantic salmon migration in Scottish coastal waters.
  • Guerreiro, O., Ferreira, M., Cascalho, J., & Borges, P. (2013, September). Towards an Agent Based Modeling: The prediction and prevention of the spread of the drywood termite Cryptotermes brevis. In Portuguese Conference on Artificial Intelligence (pp. 480-491). Springer, Berlin, Heidelberg.
  • Guerrero, D.A., Jimenez, R.M., & Rodriguez-Colina, E. (2013). WSN simulation model with a complex systems approach. In Proceedings of the 2013 Summer Computer Simulation Conference (SCSC '13), Article No. 41.
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  • GUO, L. B., LUO, X. X., & ZHU, M. X. (2013). An evolutionary game model in mobile business commerce customer trust. Journal of Theoretical & Applied Information Technology, 48(1).
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  • Halter, C., & Levin, J. (2013). Multi-mediator models of new teacher learning through creating multimedia. Paper presented at the 2013 American Educational Research Association meetings. San Francisco, CA. [HTML]
  • Hang, L. U. O. (2013). Multi-Agent Simulation Research on Urban Agglomeration Integrationand Local Government Interaction. Journal of Dalian University of Technology (Social Sciences), (2), 10.
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  • Hasson, S. T., & Al-Najar, A. A. M. (2013). Performance Evaluation of Breera Using Net Logo Simulator.
  • Hayes, B. (2013). The math of segregation. American Scientist, 101(5), 338-341.
  • Heckbert, S. (2013). MayaSim: An Agent-Based Model of the Ancient Maya Social-Ecological System. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 11. [HTML] (Oct 2013)
  • Hirsh, A., & Levy, S. T. (2013). Biking with particles: Junior triathletes’ learning about drafting through exploring agent-based models and inventing new tactics. Technology, Knowledge and Learning, 18(1-2), 9-37.
  • Hogg, C. J., Keith, R. L., & Tenney, C. M. (2013). Neighborhood Evacuation Model (No. SAND2013-7633P). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
  • Holst, N. (2013). A universal simulator for ecological models. Ecological Informatics, 13, 70-76.
  • Hongtao, X., Lincheng, S., Huayong, Z., Daibing, Z., & Xiaojia, X. (2013, January). Multi-Agent Coevolutionary Learning Method Based on Individual Rule Set. In 2013 Third International Conference on Intelligent System Design and Engineering Applications (pp. 978-981). IEEE.
  • Huang, L., Deng, S., Li, Y., Wu, J., Yin, J., & Li, G. (2013). A trust evaluation mechanism for collaboration of data-intensive services in cloud. Applied Mathematics & Information Sciences, 7(1L), 121-129.
  • Hui, L. I. U. (2013). A NetLogo Simulation Study of the Entry and Exit Behavior of Entrepreneurial Team Members. Journal of Wuyi University (Natural Science Edition), (2), 10.
  • Iandoli, L., Marchione, E., Ponsiglione, C., & Zollo, G. (2013). Learning and structural properties in small firms’ networks: a computational agent-based model. Research in Economics and Business: Central and Eastern Europe, 1(1).
  • Ilyas, Q. M. (2013). A netlogo model for ramy al-jamarat in hajj. Journal of Basic and Applied Scientific Research, 3(12), 199-209.
  • Izquierdo, L.R., Izquierdo, S.S., Galán, J.M. & Santos, J.I. (2013). Combining Mathematical and Simulation Approaches to Understand the Dynamics of Computer Models. In Simulating Social Complexity: A Handbook. Series: Understanding Complex Systems. Springer-Verlag, pp. 235-271. [PDF]
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  • Jaye, M., & Burks, R. (2013). Docking Two Models of Insurgency Growth. International Journal of Operations Research and Information Systems (IJORIS), 4(3), 19-30.
  • Johnson, J. F., & Hoe, D. H. (2013, July). Designing an agent based model for the efficient removal of red imported fire ant colonies. In Proceedings of the 2013 Summer Computer Simulation Conference (pp. 1-7).
  • Jordan, R. C., Hmelo-Silver, C., Liu, L., & Gray, S. A. (2013). Fostering reasoning about complex systems: using the aquarium to teach systems thinking. Applied Environmental Education & Communication, 12(1), 55-64.
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  • Kasmire, J., Nikolic, I., & Dijkema, G. (2013). Evolving Greenhouses: An Agent-Based Model of Universal Darwinism in Greenhouse Horticulture. Journal of Artificial Societies and Social Simulation (JASSS), 16 (4): 7. [HTML] (Oct 2013)
  • Kazak, S. (2013). Modeling random binomial rabbit hops. In Modeling Students' Mathematical Modeling Competencies (pp. 561-570). Springer, Dordrecht.
  • Kennedy, W. G., & Harrison, J. F. (2013). Towards Representing Disasters in Computational Social Simulations.
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  • Kurniawan, F. (2013, November). Completion camper reaver game scenario in 3D view with uninformed search methode using Netlogo software.
  • Lam, C. Y., Lin, J., Sim, M. S., & Tai, K. (2013). Identifying vulnerabilities in critical infrastructures by network analysis. International journal of critical infrastructures, 9(3), 190-210.
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  • Lee, J. K., & Park, J. W. (2013). A Study on the Calculation of Pedestrian’s Conflict Index Using Multi-Agent Based Model (Multi-ABM)-Focused on the Netlogo Simulation Model. Journal of Korea Transport Institute, 20(4), 105-116.
  • Lee, K., Kim, S., Kim, C. O., & Park, T. (2013). An agent-based competitive product diffusion model for the estimation and sensitivity analysis of social network structure and purchase time distribution. Journal of Artificial Socities and Social Simulation, 16(1). doi.org/10.18564/jasss.2080
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  • Li, N. W., Huang, J. T., & Niu, L. X. (2013). Evolutionary model of miners’ violation behavior based on multi-agent simulation. China Saf. Sci. J, 23(11), 10-15.
  • Liao, S. L., & Lai, J. Z. (2013). Research on Modeling and Simulation of Complex Adaptive Systems Based on Multi-Agent. In Advanced Materials Research (Vol. 756, pp. 4497-4501). Trans Tech Publications Ltd.
  • Liu, C., Sibly, R.M., Grimm, V. & Thorbek, P. (2013). Linking pesticide exposure and spatial dynamics: an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes. Ecological Modelling, 248, 92-102. [PDF]
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  • Vinatier, F., Gosme, M., & Valantin-Morison, M. (2012). A tool for testing integrated pest management strategies on a tritophic system involving pollen beetle, its parasitoid and oilseed rape at the landscape scale. Landscape Ecology, 27 (10): 1421-1433. [PDF]
  • Wagh, A. & Wilensky, U. (2012). Evolution in blocks: Building models of evolution using blocks. Proceedings of Constructionism, Athens, Greece, Aug 21-25. [HTML]
  • Wagh. A. & Wilensky, U. (2012). Breeding birds to learn about artificial selection: Two birds with one stone? Proceedings of ICLS, Sydney, Australia, July 2-6.
  • Wagh, A. & Wilensky, U. (2012). Mechanistic Explanations of evolutionary change facilitated by agent-based models. Paper presented at AERA, Vancouver, April 13-17.
  • Walker, L., & Barros, J. (2012). An agent-based population model for Wolverhampton, UK: a spatio-temporal activity based approach to population modelling. GISRUK, Lancaster.
  • Wan, S., Cao, Q., & Wang, D. (2012, December). Aoc based vehicular Ad-hoc network modeling research. In Proceedings of 2012 2nd International Conference on Computer Science and Network Technology (pp. 964-967). IEEE.
  • Wang, Y.S., Ellwood, F., Maestre, F.T., Yang, Z.Y., Wang, G. & Chu C.J. (2012). Positive interactions can produce species-rich communities and increase the species temporal turnover. Journal of Plant Ecology, 5:417-421. [PDF]
  • Wen-ze, N. I. N. G., & Wei-xing, J. I. N. G. (2012). The impact of guidance price fuzziness on construction production earning power [J]. Journal of Xi'an University of Architecture & Technology (Natural Science Edition), 1.
  • Westervelt, J. & Cohen, G. (2012). Ecologist-Developed Spatially-Explicit Dynamic Landscape Models. Springer.
  • Westervelt, J. D., & Cohen, G. L. (2012). Never fear: You already model!. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 1-6). Springer, Boston, MA.
  • Westervelt, J. D., & Hannon, B. (2012). A Collaborative Process for Multidisciplinary Group Modeling Projects. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 7-25). Springer, Boston, MA.
  • Westervelt, J. D., & MacAllister, B. (2012). Forecasting gopher tortoise (Gopherus polyphemus) distribution and long-term viability at Fort Benning, Georgia. In Ecologist-Developed Spatially-Explicit Dynamic Landscape Models (pp. 85-107). Springer, Boston, MA.
  • Whitehouse, H., Kahn, K., Hochberg, M.E., & Bryson, J.J. (2012). The role for simulations in theory construction for the social sciences: case studies concerning Divergent Modes of Religiosity. Religion, Brain & Behavior, 2(3).
  • Wong, R., & Sheng, S. Y. (2012). A business application of the system dynamics approach: Word-of-mouth and its effect in an online environment. Technology Innovation Management Review, 2(6).
  • Worsley, M. (2012, October). Multimodal learning analytics: enabling the future of learning through multimodal data analysis and interfaces. In Proceedings of the 14th ACM international conference on Multimodal interaction (pp. 353-356).
  • Worsley, M., & Blikstein, P. (2012). An Eye For Detail: Techniques for using eye tracker data to explore learning in computer-mediated environments.
  • Worth, D. J., Chin, L. S., & Greenough, C. (2012). FLAME tutorial examples: a simple SIR infection model. STFC.
  • Xianjun, C. M. Q. (2012). Simulation Study on Information Resources Allocationin Network Information Ecosystem [J]. Journal of Intelligence, 5.
  • Xiaobo, L. (2012). Netlogo Platform-based Realization of Dynamic Model of Public Opinion. Information and Documentation Services, 33(1), 55-60.
  • Xike, S., & Fengxian, L. (2012, December). An agent-based simulation of farmer's behaviors adapting to ecological policies using netlogo. In 2012 Fourth International Symposium on Information Science and Engineering (pp. 426-429). IEEE.
  • Xu, J., Liu, L., & Shao, Z. (2012, August). Research on the evolution of enterprise clusters based on theory of MAS. In 2012 International Symposium on Information Technologies in Medicine and Education (Vol. 2, pp. 1094-1098). IEEE.
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  • Yu, S. Y. (2012). GIS and agent-based modeling of emergency evacuation. Journal of the Korean Society of Hazard Mitigation, 12(1), 127-132.
  • Zhang, D., Sun, Y., & Zhong, Z. (2012, August). Simulation of the Incentive Mechanism for Knowledge Sharing between Employees Based on the Organization Design. In 2012 Fifth International Conference on Business Intelligence and Financial Engineering (pp. 89-92). IEEE.
  • Zhang, F., More, T. K., & Zhang, X. (2012). Simulation of Wealth Distribution. In Recent Advances in Computer Science and Information Engineering (pp. 175-179). Springer, Berlin, Heidelberg.
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2011

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  • Alfaro, J. F. & Miller, S. A. (2011). Planning the development of electricity grids in developing countries: An initial approach using Agent Based Models. Proceedings of 2011 IEEE International Symposium on Sustainable Systems and Technology (ISSST), May 16-18, 2011, Chicago, IL, 1-6. [PDF]
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  • Armendáriz, M., Burguillo, J. C., Peleteiro-Ramallo, A., Arnould, G., & Khadraoui, D. (2011, June). Carpooling: A Multi-Agent Simulation In Netlogo. In ECMS (pp. 61-67).
  • Arnould, G., Khadraoui, D., Armendariz, M., Burguillo, J. C., & Peleteiro, A. (2011). A transport based clearing system for dynamic carpooling business services. Proceedings of 2011 11th International Conference on ITS Telecommunications (ITST), 23-25 Aug. 2011, pp. 527-533. [PDF]
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  • Banirostam, T., & Fesharaki, M. N. (2011, November). Effect of Learning and Database in Robustness of Security Tools: Based on Immune System Modeling. In 2011 UKSim 5th European Symposium on Computer Modeling and Simulation (pp. 47-52). IEEE.
  • Banirostam, T., & Fesharaki, M. N. (2011, May). Modeling and Simulation of Influenza with Biological Agent: A New Approch for Increasing System Robustness. In 2011 Fifth Asia Modelling Symposium (pp. 13-17). IEEE.
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  • Baracaldo, N., Lopez, C., Anwar, M., Lewis, M. (2011)"Simulating the Effect of Privacy Concerns in Online Social Networks." Proceedings of the IEEE International Conference on Information Reuse and Integration, IRI 2011, 3-5 August 2011, Las Vegas, Nevada, USA 2011. [PDF]
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  • Bollinger, L. A. (2011). Evolving climate-resilient energy infrastructures. TU Delft.
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  • Braun, A. & Rosner, H.-J. (2011). Disturbance and succession. Potential of agent-based systems for modeling vulnerable ecosystems. Application to land degradation processes. In Car, A. / Griesebner, G. / Strobl, J. (Eds.) Geospatial Crossroads @ GI_Forum '11. Proceedings of the Geoinformatics Forum Salzburg, pp. 12-21.
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  • Brown, B. N., Price, I. M., Toapanta, F. R., DeAlmeida, D. R., Wiley, C. A., Ross, T. M., ... & Vodovotz, Y. (2011). An agent-based model of inflammation and fibrosis following particulate exposure in the lung. Mathematical biosciences, 231(2), 186-196.
  • Bryson, J. J. (2011). Coursework 2: Understanding Lions or Map Learning.
  • Buchtová, M., Brom, C., & Šisler, V. (2011). EDUCATIONAL GAMES AND SIMULATIONS AT SCHOOL: THE HIGH-SCHOOL STUDENTS’EXPERIENCES AND ATTITUDES. A QUALITATIVE STUDY. 7th DisCo Conferen New Media and E.
  • Cain, N., Nelson, H., Abdollahian, M., Close, B., & Hoffman, J. (2011). Not on Planet Earth (Nope): An Agent Based Model Simulating Energy Infrastructure Siting Dynamics. In APSA 2011 Annual Meeting Paper.
  • Casilli, A. A., & Tubaro, P. (2011). Why net censorship in times of political unrest results in more violent uprisings: A social simulation experiment on the UK riots. Available at SSRN 1909467.
  • Challenge, S. C., Ramirez, I., Chao, L., Miller, G., & Edington, C. (2011). Water & Ice.
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  • Chen, W., Ward, K., Li, Q., Kecman, V., Najarian, K., & Menke, N. (2011, August). Agent based modeling of blood coagulation system: implementation using a GPU based high speed framework. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 145-148). IEEE.
  • Chen, X. (2011, July). Agent-Based modeling of emergency evacuation in a spatially-aware and time-aware environment. In Proceedings of the 25th International Cartographic Conference (pp. 3-8).
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  • D'Alessandro, S. (2011). If we build it will they come? Or is this only if the price right? Adoption and use of 4G versus 3G technology: results from a Netlogo simulation. In Australian and New Zealand Marketing Academy Conference (2011) (p. 4). ANZMAC2011 Conference.
  • Damaceanu, R-C. (2011). Agent-based Computational Social Sciences using NetLogo. [LAP LAMBERT Academic Publishing.]
  • Damaceanu, R-C. (2011). An Agent-based Computational Study of Wealth Distribution in Function of Technological Progress Using NetLogo. [American Journal of Economics, Vol 1.1, pg. 15-20.]
  • Dauschan, A. A. M. (2011). Implementation of Virtual Embryology using the Thrust library for CUDA.
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  • de Bakker, G., van Bruggen, J. Jochems, W., & Sloep, P. B. (2011). Introducing the SAPS System and a corresponding allocation mechanism for synchronous online reciprocal peer support activities. Journal of Artificial Societies and Social Simulation (JASSS), 14 (1). [PDF]
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  • Dixon, D. S. (2011). An Agent-Based Adaptation of Friendship Games: Observations on Network Topologies. In Computational Social Sciences Society of the Americas: Second Annual Conference of the Computational Social Sciences Society of the Americas.
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  • Doloswala, N., Winzar, H., Song, E., & Nakandala, D. J. (2011). The discovery of liars. In Australian and New Zealand Marketing Academy Conference (2011) (p. 4). ANZMAC2011 Conference.
  • Drucker, N., & Campbell, K. (2011). Examining the Relationships Between Education, Social Networks and Democratic Support With ABM.
  • Elementary, A., Ionkov, A., & Poston, D. (2011). Does Queen+ King= Checkmate?.
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  • Ferreira, J. C., da Silva, A. R., & Afonso, J. L. (2011). Agent Based Approaches for Smart Charging Strategy for Electric Vehicles (No. 2011-39-7214). SAE Technical Paper.
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  • Ferreira, J. F., Mendes, A., Cunha, A., Baquero, C., Silva, P., Barbosa, L. S., & Oliveira, J. N. (2011, June). Logic training through algorithmic problem solving. In International Congress on Tools for Teaching Logic (pp. 62-69). Springer, Berlin, Heidelberg.
  • Filho, H. S. B., de Lima Neto, F. B., & Fusco, W. (2011). "Migration and Social Networks - An Explanatory Multi-evolutionary Agent-Based Model." Proceedings of the 2011 IEEE Symposium on Intelligent Agent (IA), 11-15 April 2011, pp. 1-7. [PDF]
  • Fong, M. (2011). Creating an Agent-based Model to Examine Spatial Behavior of Eriocheir Sinensis.
  • Frank, B.M.; Piccolo, J.J. & Baret, P.V. (2011). "A review of ecological models for brown trout: towards a new demogenetic model." Ecology of Freshwater Fish 20(2) 167-198.
  • Fuks, K., & Kawa, A. (2011, June). Virtual Organization Networking Strategies–Simulations Experiments. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 602-609). Springer, Berlin, Heidelberg.
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  • Gabrielsen, P. J., Wilson, J. L., & Pullin, M. (2011). Agent-based modeling of hyporheic dissolved organic carbon transport and transformation. AGUFM, 2011, H51B-1208.
  • Gobert, J., O'Dwyer, L., Horwitz, P., Buckley, B., Levy, S.T., & Wilensky, U. (2011). Examining the relationship between students' epistemologies of models and conceptual learning in three science domains: Biology, Physics, & Chemistry. International Journal of Science Education, 33(5), 653-684.
  • Gong, C., Li, L., Zhu, K., & Gao, Y. (2011). Evolutionary Model of Coal Mine Water Hazards Based on Multi-Agent. Systems Engineering Procedia, 2, 358-365.
  • Guerram, T., & Dehimi, N. E. H. (2011). Multi agent Simulation: A Unified Framework for the analysis of viral infections within a bovine population. International Journal of Computer Science Issues (IJCSI), 8(5), 170.
  • Gui-sheng, Y., Ji-jie, W., Hong-bin, D., & Jia, L. (2011)"Intelligent Viral Marketing algorithm over online social network." Proceedings of 2011 Second International Converence on Networking and Distributed Computing (ICNDC), 21-24 Sept. 2011, pp. 319-323. [PDF]
  • Guoxiang, R. U. A. N., & Jing, R. P. S. (2011). The Evolutionary Game Simulation on Knowledge Sharing Among Innovation Network Members. Journal of Intelligence, 30(2), 100-104.
  • Haack, J. N., Fink, G. A., Maiden, W. M., McKinnon, A. D., Templeton, S. J., & Fulp, E. W. (2011, April). Ant-based cyber security. In 2011 Eighth International Conference on Information Technology: New Generations (pp. 918-926). IEEE.
  • Hachichi, H., Chelloug, S., & Athmouni, F. (2011, April). A virtual topology for routing in adhoc networks. In 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC) (pp. 1-6). IEEE.
  • Halverson, R., Blakesley, C., & Figueiredo-Brown, R. (2011). Video game design as a model for professional learning. Learning to play, 9-28.
  • HAN, Z. B., LI, Y., PING, Y., KOU, X. D., & WU, J. J. (2011). College Confidential Project Management Modeling Based on Multi-agent System. Microprocessors, (1), 14.
  • Hashem, K., & Mioduser, D. (2011). The contribution of learning by modeling (LbM) to students' understanding of complexity concepts. International Journal of e-Education, e-Business, e-Management and e-Learning, 1(2), 151-157.
  • Hong, X. S. Z. (2011). Analysis of Dynamics of Knowledge Transfer Based on Supernetwork [J]. Journal of Intelligence, 7.
  • Honwad, S., Hmelo-Silver, C., Jordan, R., Sinha, S., Eberbach, C., Goel, A., & Rugaber, S. (2011). Learning about Ecosystems in a Computer Supported Collaborative Learning Environment.
  • Huang, L., Deng, S., Li, Y., Wu, J., & Yin, J. (2011, July). Data-dependency aware trust evaluation for service choreography. In 2011 IEEE International Conference on Web Services (pp. 708-709). IEEE.
  • Huang, S., & Yu, S. (2011). Model and simulation for global supply chain enterprises to migrate to port cluster. Journal of Tongji University. Natural Science, 39(9), 1401-1406.
  • Hussain, T., Al-Mutib, K. N., Alghamdi, A. S. (2011)"Towards software engineering process for C4I systems." Proceedings of the 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), 27-29 May 2011, pp. 125-129. [PDF]
  • Inkoom, J. N. (2011). Simulating Spatial Growth Patterns in Developing Countries: an Agent Based Modelling Approach. A Case of Shama in the Western Region of Ghana. AGUFM, 2011, U22B-05.
  • Isaac, A. G. (2011). The ABM template models: A reformulation with reference implementations. Journal of Artificial Societies and Social Simulation, 14(2). doi.org/10.18564/jasss.1749
  • Ismail, A. R. (2011). Immune-inspired self-healing swarm robotic systems (Doctoral dissertation, University of York).
  • Israel, N., & Wolf-Branigin, M. (2011). Nonlinearity in social service evaluation: A primer on agent-based modeling. Social Work Research, 35(1), 20-24.
  • Jacobson, M. J., Kapur, M., So, H. J., & Lee, J. (2011). The ontologies of complexity and learning about complex systems. Instructional Science, 39(5), 763-783.
  • Jamshidnejad, A., & Mahjoob, M. J. (2011, December). Traffic simulation of an urban network system using agent-based modeling. In 2011 IEEE Colloquium on Humanities, Science and Engineering (pp. 300-304). IEEE.
  • Johnson, T. E., & Rosenberg-Kima, R. B.Development of a training effects algorithm for modeling the impact of training in IMPRINT for 21st century Air Force needs.. SpringSim (MMS), 86–91.
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  • Karlsson, C., Frey, C. L., Zarzhitsky, D. V., Spears, D. F., & Widder, E. A. (2011). Physicomimetics for distributed control of mobile aquatic sensor networks in bioluminescent environments. In Physicomimetics (pp. 145-191). Springer, Berlin, Heidelberg.
  • Kauffmann, P. D. (2011). Traffic Flow on Escalators and Moving Walkways: Quantifying and Modeling Pedestrian Behavior in a Continuously Moving System (Doctoral dissertation, Virginia Tech).
  • Kim, J-W., & Hanneman, R.A. (2011). A Computational Model of Worker Protest. Journal of Artificial Societies and Social Simulation (JASSS), 14 (3) 1. [HTML] (June 2011)
  • Kim, J. Y., Park, H. J., & Jun, Y. C. (2011). The Effects of Educational Contents Authoring in a Project-Based Learning using NetLogo for Pre-service Teachers' Creativity. Journal of Engineering Education Research, 14(4), 29-38.
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  • Lammoglia, A., Marilleau, N., & Josselin, D. (2011). How to assess the robustness of a flexible transport using an Agent Based Model?.
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  • Suárez, J. L. & Sancho, F. (2011). A virtual laboratory for the study of history and cultural dynamics. Journal of Artificial Societies and Social Simulation, 14(4). doi.org/10.18564/jasss.1855
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2010

  • Abrahamson, D. (2010). A tempest in a teapot is but a drop in the ocean: action-objects in analogical mathematical reasoning. In K. Gomez, L. Lyons & J. Radinsky (Eds.) Learning in the Disciplines: Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010) (Vol. 1 [Full Papers], pp. 492-499). International Society of the Learning Sciences: Chicago, IL.
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  • Buzby, C. K., & Jona, K. (2010). EcoCasting: Using NetLogo models of aquatic ecosystems to teach scientific inquiry. AGUFM, 2010, ED21A-0650.
  • Cecconi, F., Campenni, M., Andrighetto, G., & Conte, R. (2010). What do agent-based and equation-based modelling tell us about social conventions: The clash between ABM and EBM in a congestion game framework. Journal of Artificial Societies and Social Simulation, 13(1). doi.org/10.18564/jasss.1585
  • Chu, C.J., Weiner, J., Maestre, F.T., Wang, Y.S., Morris, C., Xiao, S., Yuan, J.L., Du, G.Z. & Wang, G. (2010). Effects of positive interactions, competitive modes and abiotic stress on self thinning in simulated plant populations. Annals of Botany 106: 647-652
  • Cioffi-Revilla, C. (2010). A methodology for complex social simulations. Journal of Artificial Societies and Social Simulation, 13(1). doi.org/10.18564/jasss.1528
  • Cong, R., & Wei, Y. (2010). Potential impact of (CET) carbon emissions trading on China’s power sector: A perspective from different allowance allocation options. Energy, 35, p 3921-3931. [PDF]
  • Davilia, A. A., & An, G. (2010). An Agent Based Model of Liver Damage, Inflammation, and Repair: in Silico Translation of Cellular and Molecular Mechanisms to the Clinical Phenomena of Cirrhosis Using NetLogo. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • Davilia, A. A., & An, G. (2010). An Agent Based Model of Liver Damage, Inflammation, and Repair: in Silico Translation of Cellular and Molecular Mechanisms to the Clinical Phenomena of Cirrhosis Using NetLogo. Journal of Surgical Research (ASC Abstracts Issue), 158(2):411.
  • Edmonds, B. (2010). Bootstrapping knowledge about social phenomena using simulation models. Journal of Artificial Societies and Social Simulation, 13(1). doi.org/10.18564/jasss.1523
  • Georgeon, O., & Sakellariou, I. Designing Environment-Agnostic Agents. In Adaptive Learning Agents workshop (ALA2012), of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), pp. 25-32. Valencia, Spain 2012.2[HTML]
  • Gobattoni, F., Lauro, G., Leone, A., Monaco, R., & Pelorosso, R. (2010). A simulation method for the stability analysis of landscape scenarios by using a NetLogo application in GIS environment. EGUGA, 5769.
  • Hadzikadic, M., Carmichael, T., & Curtin, C. (2010). Complex Adaptive Systems and Game Theory: An Unlikely Union. Complexity. Vol 16, Issue 1, p34.
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  • Holbert, N., Penney, L., & Wilensky, U. (2010). Bringing Constructionism to Action Gameplay. In J. Clayson & I. Kalas (Eds.), Proceedings of the Constructionism 2010 Conference. Paris, France, Aug 10-14.
  • Holbert, N., & Wilensky, U. (2010). FormulaT Racing: Combining gaming culture and intuitive sense of mechanism for video game design. In K. Gomez & J. Radinsky (Ed.), Proceedings of the 9th International Conference of the Learning Sciences. Chicago, IL.
  • Izquierdo, S.S., Izquierdo, L.R. & Vega-Redondo, F. (2010). The Option to Leave: Conditional Dissociation in the Evolution of Cooperation. Journal of Theoretical Biology Volume 267, Issue 1, pp. 76–84. [PDF]
  • Jang, S. M., Rhee, J. H., & Oh, S. H. (2010). Implementation of Microscopic Pedestrian Simulation by using NetLogo. Journal of The Korean Society of Civil Engineers, 30(2D), 119-125.
  • Johnson, T., Karaman, S., & Rosenberg-Kima, R. B. (2010, October). Computer modelling of teams learning: an agent based social simulation of team learning (SSTeL).. Paper presented at AECT (Association for Educational Communications and Technology), Anaheim, CA.
  • Johnson, T., Sikorski, E. G., Rosenberg-Kima, R. B., Novak, E., & Andrews, D. E. (2010, May). Development of a training effects algorithm for use within an agent-based modeling and simulation tool.. Paper presented at AERA (American Educational Research Association), Denver, Colorado.
  • Kahn, K., & Noble, H. (2010). The BehaviourComposer 2.0: a web-based tool for composing NetLogo code fragments. Constructionist approaches to create learning, thinking and education: Lessons for the 21st century: Proceedings for Constructionism.
  • Kahn, K. & Noble, H. (2010). The Modelling4All Project -- A web-based modelling tool embedded in Web 2.0. Paper presented at the Proceedings of Constructionism 2010, Paris, France
  • Kim, J-W. (2010). A Tag-Based Evolutionary Prisoner's Dilemma Game on Networks with Different Topologies. Journal of Artificial Societies and Social Simulation (JASSS), 13 (3): 2. [HTML] (June 2010)
  • Kleczkowski, A., Maharaj, S. (2010). Stay at home, Wash Your Hands: Epidemic Dynamics with Awareness of Infection. In proceedings of the SCS Summer Computer Simulation Conference, Ottawa, July 2010.
  • Kurahashi, C., & An, G. (2010). Examining the Spatial Dynamics of the Inflammatory Response with Topographical Metrics in an Agent-Based Computational Model of Inflammation and Healing. Journal of Surgical Research (ASC Abstracts Issue), 158(2):382.
  • Kurahashi, C., & An, G. (2010). Examining the Spatial Dynamics of the Inflammatory Response with Topographical Metrics in an Agent-Based Computational Model of Inflammation and Healing. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • Lerner, R., Levy, S.T., & Wilensky, U. (2010). Encouraging Collaborative Constructionism: Principles Behind the Modeling Commons. n J. Clayson & I. Kalas (Eds.), Proceedings of the Constructionism 2010 Conference. Paris, France, Aug 10-14.
  • Levy, S. T., Wilensky, U. (2010). Mining students' actions for understanding of complex systems: Students' explorations of gas models in the Connected Chemistry curriculum. Paper presented at AERA 2010, Denver, CO. [PDF]
  • Li, Y., Wang, W., Ji, L., & Zhu, J. (2010, October). Research on Muti-Agent simulation and emulation in battlefield based on NETLOGO. In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010) (Vol. 6, pp. V6-131). IEEE.
  • Maroulis, S., Guimera, R., Petry, H., Stringer, M., Gomez, L., Amaral, L., & Wilensky, U. (2010). A complex systems approach to Educational Policy Research. Science 1 October 2010: Vol. 330. no. 6000, pp. 38 .
  • Mastrangeli, M., Schmidt, M., & Lacasa, L. (2010). The Roundtable: An Abstract Model of Conversation Dynamics. Journal of Artificial Societies and Social Simulation 13 (4) 2 [HTML]
  • Miodownik, D., Cartrite, B., & Bhavnani, R. (2010). Between replication and docking: "Adaptive agents, political institutions, and civic traditions" revisited. Journal of Artificial Societies and Social Simulation, 13(3). doi.org/10.18564/jasss.1627
  • Monett, D., Janisch, R., Starroske, S. (2010). NL-Analyzer: Enhancing Simulation Tools to Assist Multiagent Systems' Teaching. In K. Gomez & J. Radinsky (Ed.), In W. van der Hoek, G.A. Kaminka, Y. Lespérance, M. Luck, S. Sen (eds.), Proceedings of the Workshop Multi-Agent Systems for Education and Interactive Entertainment (MASEIE), 9th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010, pp. 1-6, Toronto, Canada.
  • Muis, J. (2010). Simulating Political Stability and Change in the Netherlands (1998-2002): an Agent-Based Model of Party Competition with Media Effects Empirically Tested. Journal of Artificial Societies and Social Simulation (JASSS) 13 (2): 4 [HTML] (March 2010)
  • Niazi, M., Siddique, Q., Hussain, A., & Kolberg, M. (2010). Verification & Validation of an Agent-Based Forest Fire Simulation Model. Agent-Directed Simulation Symposium, SCS Spring Simulation Conference, April 2010, Orlando, FL.
  • Olson, I., Horn, M., & Wilensky, U. (2010). NetLogo Tango: Supporting Student Programming with Tangible Objects and Multi-Touch Displays. In K. Gomez & J. Radinsky (Ed.), Proceedings of the 9th International Conference of the Learning Sciences. Chicago, IL.
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  • Olševičová, K., Bodnárová, A., & Čech, P. (2010, November). Managing Net-Logo Models in CLIPS. In Proceedings of the 9th WSEAS International Conference on Data Networks, Communications, Computers (DNCOCO 10) (pp. 150-152).
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  • Rezayan, H., Delavar, M.R., Frank, A.U. & Mansouri, A. (2010). Spatial rules that generate urban patterns: Emergence of the power law in the distribution of axial line length. International Journal of Applied Earth Observation and Geoinformation. Volume 12, Issue 5, October 2010, pp. 317-330.
  • Rogers, C., Nguy, K. N. T., Amajor, O. A., Seaquist, T., Crawford, B., Mubayi, A., ... & Oyediran, O. (2010). Daily behavior of trypanosoma cruzi hosts and vectors in Texas: An agent-based modeling approach in Netlogo. Mathematics Department Technical Report 2010-18, University of Texas at Arlington, 2010. Online at http://www. uta. edu/math/preprint/rep2010 18. pdf.
  • Russell, E., Buzby, C., & Wilensky, U. (2010). Watershed Modeling For Education. Paper presented at the First International Conference for Geospatial Research & Application, Washington, DC.
  • Sakellariou, I. Agent based Modelling and Simulation using State Machines. SIMULTECH 2012. Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, pp 270-279, Rome, Italy, 28 - 31 July, 2012 [HTML]
  • Sakellariou, I. Turtles as State Machines - Agent Programming in NetLogo using State Machines. Proceedings of the 4th International Conference on Agents and Artificial Intelligence (ICAART 2012), Volume 2 - Agents, Joaquim Filipe, Ana L. N. Fred (Eds.), SciTePress 2012, ISBN 978-989-8425-96-6, pp. 375-378, Vilamoura, Algarve, Portugal, 6-8 February, 2012 [HTML]
  • Seal, J.B., Alverdy, J.C., & An, G. (2010). Mechanistic Computational Representation of Iron Metabolism in the Gut Milieu. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 3, 2010.
  • Seal, J.B., Alverdy, J.C., Zaborina, O., Zaborin, A., Babrowski, T., Romanowski, K., & An, G. (2010). Computational mechanistic representation of phosphate sensing and virulence activation in Pseudomonas aeruginosa in the gut milieu. Poster Presentation for the 39th Annual Critical Care Congress of the Society of Critical Care Medicine, Miami, FL, January 11, 2010.
  • Sengupta, P., & Wilensky, U. (2010). Balancing Electrons & Learning Electricity in 5th Grade: Emergence, Electric Current and Multi- Agent Based Models . Cognition and Instruction.
  • Sheth, K.R., & An, G. (2010). In Silico Translation of Cellular and Molecular Mechanisms to Clinical Phenomena in Atheroma Development with an Agent Based Model. Journal of Surgical Research (ASC Abstracts Issue), 158(2):382-3.
  • Sheth, K.R., & An, G. (2010). In Silico Translation of Cellular and Molecular Mechanisms to Clinical Phenomena in Atheroma Development with an Agent Based Model. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • Stonedahl, F. & Rand, W., & Wilensky, U. (2010). Evolving Viral Marketing Strategies. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. Portland, OR.
  • Stamatopoulou, I., Sakellariou, I., & Kefalas, P. Formal Agent-Based Modelling and Simulation of Crowd Behaviour in Emergency Evacuation Plans. Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on , vol.1, no., pp.1133-1138, 7-9 Nov. 2012 [HTML]
  • Stonedahl, F. & Rand, W., & Wilensky, U. (2010). Evolving Viral Marketing Strategies. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. Portland, OR.
  • Stonedahl, F., & Wilensky, U. (2010). Evolutionary Robustness Checking in the Artificial Anasazi Model. Proceedings of the AAAI Fall Symposium on Complex Adaptive Systems: Resilience, Robustness, and Evolvability. November 11-13, 2010. Arlington, VA.
  • Stonedahl, F. & Wilensky, U. (2010). Finding Forms of Flocking: Evolutionary Search in ABM Parameter-Spaces. Proceedings of the MABS workshop at the Ninth International Conference on Autonomous Agents and Multi-Agent Systems. Toronto, Canada.
  • Suhadolnik, N., Galimberti, J., & Da Silva, S. (2010). Robot traders can prevent extreme events in complex stock markets. Physica A: Statistical Mechanics and its Applications, 389(22): 5182-5192.
  • Thiele, J.C., & Grimm, V. (2010). NetLogo meets R: Linking agent-based models with a toolbox for their analysis. Environmental Modeling and Software. 25(8), 972-974.
  • Valbuena, D., Verburg, P.H., Veldkamp, A., Bregt, A.K. & Ligtenberg, A. (2010). Effects of farmers' decisions on the landscape structure of a Dutch rural region: An agent-based approach. Landscape and Urban Planning. 97(2), 98-110.
  • Wagh, A. & Wilensky, U. (2010). Agent-based and aggregate level reasoning elicited by problem scenarios and an agent-based model. Poster presented at the annual meeting of the American Education Research Association, Denver, CO, April 30-May 4.
  • Wandling, M., & An, G. (2010). Multi-Scale Dynamic Knowledge Representation of Pulmonary Inflammation with an Agent-Based Model: from Gene Regulation to Clinical Phenomenon. Journal of Surgical Research (ASC Abstracts Issue), 158(2):381.
  • Wandling, M., & An, G. (2010). Multi-Scale Dynamic Knowledge Representation of Pulmonary Inflammation with an Agent-Based Model: from Gene Regulation to Clinical Phenomenon. Oral Presentation for the 5th Annual Academic Surgical Congress, San Antonio, TX, February 5, 2010.
  • Wilensky, U., & Novak, M. (2010). Teaching and learning evolution as an emergent process: The BEAGLE project. Epistemology and science education: Understanding the evolution vs. intelligent design controversy, 213-243.
  • Wilensky, U., & Papert, S. (2010). Restructurations: Reformulations of Knowledge Disciplines through new representational forms. In J. Clayson & I. Kallas (Eds.), Proceedings of the Constructionism 2010 Conference. Paris, France.
  • Wilkerson-Jerde, M. & Wilensky, U. (2010). NetLogo HotLink Replay: A Tool for Exploring, Analyzing and Interpreting Mathematical Change in Complex Systems. Poster to be presented at ICLS 2010, Chicago, IL, Jun 29 - Jul 2.
  • Wilkerson-Jerde, M. & Wilensky, U. (2010). Qualitative Calculus of Systems: Exploring Students' Understanding of Rate of Change and Accumulation in Multiagent Systems. Paper accepted for presentation at AERA 2010, Denver, CO.
  • Wilkerson-Jerde, M., & Wilensky, U. (2010). Restructuring Change, Interpreting Changes:The DeltaTick Modeling and Analysis Toolkit. Paper to be presented at Constructionism 2010, Paris.
  • Wilkerson-Jerde, M. & Wilensky, U. (2010). Seeing Change in the World from Different Levels: Understanding the Mathematics of Complex Systems. In M. Jacobson (Org.), U. Wilensky (Chair), and Peter Reimann (Discussant), Learning about Complexity and Beyond: Theoretical and Methodological Implications for the Learning Sciences. To be presented at ICLS 2010, Chicago, IL, Jun 29 - Jul 2.
  • Yang, C.K. & Wilensky, U. (2010). Reinterpreting school effects from the bottom up: Merging statistical analysis and a complex systems perspective. Poster presented at the Constructionism conference. Paris, France. August 16-20, 2010.

2009

  • Abrahamson, D. (2009). A student's synthesis of tacit and mathematical knowledge as a researcher's lens on bridging learning theory. In M. Borovcnik & R. Kapadia (Eds.), Research and developments in probability education [Special Issue]. International Electronic Journal of Mathematics Education, 4(3), 195 - 226.
  • Abrahamson, D. (2009). Embodied design: Constructing means for constructing meaning. Educational Studies in Mathematics, 70(1), 27-47. [PDF]
  • An, G. (2009). A model of TLR4 signaling and tolerance using a qualitative, particle–event-based method: Introduction of spatially configured stochastic reaction chambers (SCSRC). Mathematical biosciences, 217(1), 43-52.
  • An, G., & Wilensky, U. (2009). From artificial life to in silico medicine: NetLogo as a means of translational knowledge representation in biomedical research. In A. Adamatzky & M. Komosinski (Eds.), Artificial Life Models in Software (2nd Ed.). Berlin: Springer-Verlag. [PDF]
  • Ang, C.S., & Zaphiris, P. (2009). Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design. In Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (INTERACT '09), Tom Gross, Jan Gulliksen, Paula Kotzé, Lars Oestreicher, Philippe Palanque, Raquel Oliveira Prates, and Marco Winckler (Eds.). Springer-Verlag, Berlin, Heidelberg, 443-456.
  • Bandini, S., Manzoni, S., & Vizzari, G. (2009). Agent based modeling and simulation: An informatics perspective. Journal of Artificial Societies and Social Simulation, 12(4).
  • BenDor, T., Westervelt, J., Aurambout, J. & Meyer, W. (2009). Simulating population variation and movement within fragmented landscapes: An application to the gopher tortoise (Gopherus polyphemus). Ecological Modelling 220(6), 867-878.
  • Bettge, T. (2009). System dynamics modeling of community sustainability in netlogo. TJHSST Computer Systems Lab Senior Research Project, 2008–2009, 1-8.
  • Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using multi-agent simulation. International Journal of Computers for Mathematical Learning, 14(1), 81-119. [PDF]
  • Blikstein, P., Wilensky, U., & Abrahamson, D. (2009, April). Towards a framework for cognitive research using agent-based modeling and complexity sciences. In M. Jacobson (Chair), M. Kapur (Organizer) & N. Sabelli (Discussant), Complexity, learning, and research: Under the microscope, new kinds of microscopes, and seeing differently. Symposium conducted at the annual meeting of the American Educational Research Association, San Diego, CA.
  • Carmichael, T., Hadzikadic, M.,Dréau, D., & Whitmeyer, J. (2009). Characterizing Threshold Effects Across Diverse Phenomena. In Z. Ras & W. Ribarsky (Eds), Advances in Information and Intelligent Systems. New York: Springer.
  • Castillo, L. F., Bedia, M. G., Uribe, A. L., & Isaza, G. (2009). A formal approach to test commercial strategies: Comparative study using Multi-agent based techniques. Journal of Physical Agents, 3(3), 25-30.(1), 27-47. [PDF]
  • Chu, C.J., Weiner, J., Maestre, F.T., Xiao, S., Wang, Y.S., Li, Q., Yuan, J.L., Zhao, L.Q., Ren, Z.W. & Wang, W. 2009.Positive interactions can increase size inequality in plant populations. Journal of Ecology 94: 1401-1407.
  • Crooks, A., Hudson-Smith, A., & Dearden, J. (2009). Agent street: An environment for exploring agent-based models in Second Life. Journal of Artificial Societies and Social Simulation, 12(4).
  • Drachsler, H., Hummel, H., van den Berg, B., Eshuis, J., Waterink, W., Nadolski, R., Berlanga, A., Boers, N. & Koper, R. (2009). Evaluating the Effectiveness of Personalised Recommender Systems in Learning Networks. In R. Koper (ed.), Learning Network Services for Professional Development (pp.95-113), Berlin:Springer-Verlag. [PDF]
  • Dréau, D., Stanimirov, D., Carmichael T., & Hadzikadic M. (2009). An agent-based model of solid tumor progression. Paper presented at the 1st International Conference on Bioinformatics and Computational Biology, New Orleans, LA., April 2009.
  • Filatova, T., Parker, D., & van der Veen, A. (2009). Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change. Journal of Artificial Societies and Social Simulation (JASSS), 12 (1): 3. [HTML]
  • Friedman, D. & Abraham, R. (2009). Bubbles and crashes: Gradient dynamics in financial markets. Journal of Economic Dynamics and Control 33(4), 922-937.
  • Fuks, K., & Kawa, A. (2009). Simulation of resource acquisition by e-sourcing clusters using netlogo environment. Agent and Multi-Agent Systems: Technologies and Applications, 687-696.[PDF]
  • Graham, S. (2009). Behaviour Space: Simulating Roman Social Life and Civil Violence. Digital Studies / Le Champ NuméRique, 1(2). [HTML]
  • Hamill, L. & Gilbert, N. (2009). Social circles: A simple structure for agent-based social network models. Journal of Artificial Societies and Social Simulation, 12(2).
  • Han, J. X., & Xue, H. F. (2009). Netlogo based modeling and simulation of oem enterprise knowledge management. Journal of Xi'an Technological University, 29, 192-199.
  • Handel, A., Yates, A., Pilyugin, S.S., & Antia, R. (2009). Sharing the burden: Antigen transport and firebreaks in immune responses. Journal of the Royal Society Interface 6, 447-454.
  • Hunt, C.A. , Ropella, G. E. P.,  Lam, T. N., Tang, J., Kim, S. H. J., Engelberg, J. A., & Sheikh-Bahaei, S. (2009). At the biological modeling and simulation frontier. Pharmaceutical Research 26(11), 2369-2400. [PDF]
  • Izquierdo, L. R., Izquierdo, S. S., Galán, J. M., & Santos, J. I. (2009). Techniques to Understand Computer Simulations: Markov Chain Analysis. Journal of Artificial Societies and Social Simulation (JASSS), 12 (1): 6 [HTML]
  • Janssen, M.A. (2009). Understanding Artificial Anasazi. Journal of Artificial Societies and Social Simulation (JASSS), 12 (4): 13.
  • Jepsen, MR , Simonsen, J & Ethelberg, S (2009). Spatio-temporal cluster analysis of the incidence of Campylobacter cases and patients with general diarrhea in a Danish county.International Journal of Health Geographics , 8(11), pp. 1-12.
  • Jiang, L., & Zhao, C. (2009, August). The Netlogo-Based Dynamic Model for the Teaching. In 2009 Ninth International Conference on Hybrid Intelligent Systems (Vol. 2, pp. 49-53). IEEE.
  • Johnson, B.R. (2009). A self-organizing model for task allocation via frequent task quitting and random walks in the honeybee. American Naturalist 174, 537-547.
  • Kimbrough, S. O., & Murphy, F. H. (2009). Learning to collude tacitly on production levels by oligopolistic agents. Computational Economics 33(1), 47-78. [PDF]
  • Kornhauser, D., Wilensky, U., & Rand, W. (2009). Design guidelines for agent based model visualization. Journal of Artificial Societies and Social Simulation, JASSS, 12(2), 1. [PDF]
  • Lansing, J.S., Cox, M.P., Downey, S.S., Janssen, M.A., & Schoenfelder, J.W. (2009). A robust budding model of Balinese water temple networks. World Archaeology 41(1), 112-133.
  • Lerner, R., Levy S. T., & Wilensky, U. (2009). Design of the Modeling Commons. Chais Conference, Tel Aviv, Israel.
  • Levin, J. A. (2009).Notes toward a landmark mediated geometry. [HTML]
  • Levy, S. T., & Wilensky, U. (2009). Crossing levels and representations: The Connected Chemistry (CC1) curriculum. Journal of Science Education and Technology, 18(3), 224-242. [PDF]
  • Levy, S. T., & Wilensky, U. (2009). Students' learning with the Connected Chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18(3), 243-254. [PDF]
  • Linard, C., Ponçon, N., Fontenille, D., & Lambin, E.F., (2009). A multi-agent simulation to assess the risk of malaria re-emergence in southern France. Ecological Modelling, 220(2): 160-174.
  • Maharaj, S., McCaig, C., & Shankland C. E. (2009). Studying the effects of adding spatiality to a process algebra model. 8th Workshop on Process Algebra and Stochastically Timed Activities :153-158, 2009. Edinburgh.
  • Manzo, G. (2009). “Boudon’s Model of Relative Deprivation Revisited.” In Cherkaoui, M. & Hamilton, P. (eds.) Raymond Boudon: A Life in Sociology, Oxford, Bardwell Press, vol. 3, part 3, ch. 46, 91-121. [PDF]
  • Nadolski, R. J., van den Berg, B., Berlanga, A. J., Drachsler, H., Hummel H. G. K., Koper, R., & Sloep, P. B. (2009). Simulating Light-Weight Personalised Recommender Systems in Learning Networks: a Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies. Journal of Artificial Societies and Social Simulation (JASSS), 12(1). [HTML]
  • New York Times Magazine. (2009). "Random Promotions" from the 9th Annual Year in Ideas. Retrieved March 23, 2010 from http://www.nytimes.com/projects/magazine/ideas/2009/#social_science-12. [HTML]
  • Niazi, M., and Hussain, A. (2009). Agent-Based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad Hoc, and Other Complex Networks. IEEE Communications Magazine, Vol.47 No.3. [HTML]
  • Niazi, M., Hussain, A., & Kolberg, M. (2009). Verification and Validation of Agent-Based Simulations using the VOMAS approach. Proceedings of the Third Workshop on Multi-Agent Systems and Simulation '09, as part of MALLOW 09, Sep 7-11, 2009, Torino, Italy.
  • Nikolai, C. & Madey, G. (2009). Tools of the trade: A survey of various agent based modeling platforms. Journal of Artificial Science and Social Simulation, 12(2).
  • Novak, M., Levy, S. T., & Wilensky, U. (2009). Playing in a particle sandbox and gaining a glass box perspective: The Connected Chemistry curriculum. Working paper.
  • Olševičová, K. (2009). Ambient Intelligence, Agents and NetLogo. In Ambient Intelligence Perspectives II: Selected Papers from the Second International Ambient Intelligence Forum 2009 (Vol. 5, p. 77). IOS Press.
  • O'Sullivan, D. (2009). "Changing neighborhoods—Neighborhoods Changing; A Framework for spatially explicit agent-based models of social systems." Sociological Methods Research, 37(4), 498-530. [HTML
  • Patarakin E., Yarmakhov B. (2009). "Modeling organizational relations with the NetLogo "links" Educational Technology & Society" Educational Technology & Society, No. 2. pp. 409-422. (In Russian) [PDF]
  • Rolón, M., Canavesio, M. & Martínez, E. (2009). Agent Based Modelling and Simulation of Intelligent Distributed Scheduling Systems. In Jezowski Jacek & Thullie Jan (Eds.), Proceedings of the 19th European Symposium on Computer Aided Process Engineering (Vol. 26, pp. 985-990). Amsterdam, Holland: Elsevier.
  • Rolón, M., Canavesio, M. & Martinez, E. (2009). Generative Modeling of Holonic Manufacturing Execution Systems for Batch Plants. In R.M. de Brito Alves, C.A.O. do Nascimento , & E.C. Biscaia Jr. (Eds.),10th International Symposium on Process Systems Engineering: Part A (Vol. 27, 795-800). Amsterdam, Holland: Elsevier.
  • Sakellariou, I., Kefalas, P., Stamatopoulou, I. (2009). MAS Coursework Design in NetLogo. In Proceedings of the Educational Uses of Multi Agent Systems, Budapest, Hungary. [PDF]
  • Sammons, P. J., & Page, J. (2009). Experimentation and validation of vehicle cluster simulator using NetLogo. Adelaide, SA, Australia.
  • Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21-50. [PDF]
  • Schultz, L., Koons, P. O., & Schauffler, M. (2009). Middle-School Understanding of the Greenhouse Effect using a NetLogo Computer Model. AGUFM, 2009, ED23A-0545.
  • Siddiqa, A., Niazi, M., Mustafa, F., Bukhari, H., Hussain A., Akram, N., Shaheen, S., Ahmed F., & Iqbal, S. (2009). A New Hybrid Agent-Based Modeling & Simulation Decision Support System For Breast Cancer Data Analysis, IEEE ICICT 09, IBA, Karachi.
  • Steiniger, S. & Hay, G.J. (2009). Free and open source geographic information tools for landscape ecology. Ecological Informatics 4(4), 183-195.
  • Stonedahl, F. (2009, May). NetLogo: Meditations on a Tool for Learning and Modeling. In International Workshop on the Educational Uses of Multi-Agent Systems (EduMAS) (p. 3).
  • Stonedahl, F., Wilkerson-Jerde, M., & Wilensky, U. (2009). Re-conceiving introductory computer science curricula through agent-based modeling. Paper presented at the Eighth International Conference on Autonomous Agents and Multi-agent Systems (AAMAS) - EduMAS Workshop, Budapest, Hungary. [PDF]
  • Sudhira, H.S. and Ramachandra, T.V. (2009). A Spatial Planning Support System for Managing Bangalore’s Urban Sprawl. In Stan Geertman and John Stillwell (Eds.), Planning Support Systems: Best Practice and New Methods. Springer.
  • Thompson, N. S. & Derr, P. (2009). Contra Epstein, good explanations predict. Journal of Artificial Societies and Social Simulation, 12(1).
  • Vattam, S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. & Jordan, R. (2009). From Conceptual Models to Agent-based Simulations: Why and How. Paper presented at the 14th International Conference on Artificial Intelligence in Education (AIED), 2009. [PDF]
  • Wang, B., Ji, F., & Zhou, M. (2009). Analyzed on the Forming and Its Evolving Of Complex Logistics Network Based On Netlogo. In Logistics: The Emerging Frontiers of Transportation and Development in China (pp. 519-526).
  • Weisberg, M., & Muldoon, R. (2009). Epistemic Landscapes and the Division of Cognitive Labor. Philosophy of Science, 76, 225-252
  • Wilkerson, M. (2009). Agents with attitude: Exploring Coombs Unfolding technique with agent-based models. International Journal of Computers for Mathematical Learning, 14 (1), 51-60. [PDF]
  • Wilkerson-Jerde, M., & Wilensky, U. (2009, April). Complementarity in agent-based and equation-based models. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.
  • Zhai, Z., Schoenharl, T., Chen, F., & Madey, G. (2009). Design and Implementation of an Agent-Based Simulation for Emergency Response and Crisis Management. Department of Computer Science and Engineering, University of Norte Dame, IN. [PDF]

2008

  • Abraham, R., & Friedman, D. (2008). Bubbles and Crashes: a Simulation Approach.
  • Aktipis, A. (2008). A SIMPLE model for the evolution of movement and cooperation: Social dilemmas emerge from interactions with a shared environment. Paper presented at Swarmfest, 2008.
  • Amblard, F. & Jager, W. (2008). Advances in Complex Systems (ACS): Special Issue on Social Simulation (Vol. 11(2)). World Scientific Publishing.
  • An, G. (2008). Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theoretical Biology and Medical Modelling, 5(1), 11.
  • Arunachalam, S., Zalila-Wenkstern, R., & Steiner, R. (2008). Environment Mediated Multi Agent Simulation Tools - A Comparison. In Proceedings of the 2008 Second IEEE international Conference on Self-Adaptive and Self-Organizing Systems Workshops (57-62). Washington, DC: IEEE Computer Society. [PDF]
  • Blikstein, P., & Wilensky, U. (2008). Implementing multi-agent modeling in the classroom: Lessons from empirical studies in undergraduate engineering education. In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 266-273). Utrecht, The Netherlands: ISLS. [PDF]
  • Blikstein, P., Abrahamson, D., & Wilensky, U. (2008). The classroom as a complex adaptive system: An agent-based framework to investigate students' emergent collective behaviors. In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 312-313). Utrecht, The Netherlands: ISLS. [PDF]
  • Bonura, A., Capizzo, M. C., Fazio, C., & Guastella, I. (2008, May). Electric conduction in solids: a pedagogical approach supported by laboratory measurements and computer modelling environments. In AIP Conference Proceedings (Vol. 1018, No. 1, pp. 227-230). American Institute of Physics.
  • Bravo, G. (2008). Imitation and cooperation in different helping games. Journal of Artificial Societies and Social Simulation, 11(1).
  • Chu, C.J. Maestre, F.T., Xiao, S., Weiner, J., Wang, Y.S., Duan, Z.H. & Wang, G. (2008). Balance between facilitation and resource competition determines biomass-density relationships in plant populations. Ecology Letters 11: 1189-1197
  • Crooks, A., Castle, C. & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems 32(6), 417-430.
  • Colosimo, A. (2008). Biological Simulations by Autonomous Agents: Two Examples Using the NetLogo Environment. Biophysics and Bioengineering Letters, 1(3).[PDF]
  • Damaceanu, R. (2008). An agent-based computational study of wealth distribution in function of resource growth interval using NetLogo. Applied Mathematics and Computation, 201, 371-377.
  • Diappi, L. & Bolchi, P. (2008). Smith's rent gap theory and local real estate dynamics: A multi-agent model. Computers, Environment and Urban Systems 32(1), 6-18.
  • Earnest, D. C. (2008). Voting Complexity and Electoral Outcomes: An Agent-Based Model of Condorcet Social Choice Problems. Complexity and Policy Analysis: Decision Making in an Interconnected World. Kurt A. Richardson, Linda Dennard and Goktug Morcol, eds. Greenwich, CT: Information Age Publishing: 2008. 147-166.
  • Earnest, D.C. (2008). Coordination in Large Numbers: An Agent-Based Model of International Negotiations. International Studies Quarterly 52, 2: 363-382. [HTML]
  • Feldman, T., Friedmam, D., & Abraham R. (2008). Bubbles & Crashes: An Experimental Approach. Retrieved February 25, 2010 from http://www.vismath.org/research/landscapedyn/articles/vela2.pdf. [PDF]
  • Fioretti, G. & Lomi, A. (2008). An agent-based representation of the garbage can model of organizational choice. Journal of Artificial Societies and Social Simulation, 11(1).
  • Francisco, M. (2008). Designing classrooms with ABM. Paper presented at Swarmfest, 2008.
  • Gilbert G. (2008). Agent-based models. Sage Publications, Inc.
  • Graham, S., & Steiner, J. (2008). “Travellersim: Growing Settlement Structures and Territories with Agent-Based Modelling” in Jeffrey T. Clark and Emily M. Hagemeister (eds) Digital Discovery: Exploring New Frontiers in Human Heritage. CAA 2006. Computer Applications and Quantitative Methods in Archaeology. Proceedings of the 34th Conference, Fargo, United States, April 2006. Budapest: Archaeolingua.
  • Iozzi, F. (2008). A simple implementation of Schelling's segregation model in NetLogo. Dondena Centre for Research on Social Dynamics Dondena Working Paper, 15.
  • Isaac, A. G. (2008). Simulating evolutionary games: A Python-based introduction. Journal of Artificial Societies and Social Simulation, 11(3).
  • Janota, A. (2008). MAS Model of the Level Crossing. International Journal of ITS Research, Japan, Vol. 6, No. 2 (p. 111-116). ISSN 1348-8503
  • Janssen, M.A. & Bushman, C. (2008). Evolution of cooperation and altruistic punishment when retaliation is possible. Journal of Theoretical Biology 254: 451-455.
  • Janssen, M. A., Alessa, L. N., Barton, M., Bergin, S., & Lee, A. (2008). Towards a community framework for agent-based modelling. Journal of Artificial Societies and Social Simulation, 11(2).
  • Jolly, R., & Wakeland, W. W. (2008). Using Agent Based Simulation and Game Theory Analysis to Study Information Sharing in Organizations - The InfoScape. HICSS 2008: 335.
  • Jones, G.T. (2008). Dynamical Jurisprudence: Law as a Complex System. Georgia State University Law Review, 24(4), 873-883. [PDF]
  • Jovani, R., and V. Grimm. (2008). Breeding synchrony in colonial birds: from local stress to global harmony. Proceedings of the Royal Society of London B 275:1557-1563.
  • Kanarek, A., Lamberson, R., & Black, J. M. (2008). An individual-based model for traditional foraging behavior: investigating effects of environmental fluctuation. Natural Resource Modeling, Vol 21 (1), pp 93-116. [HTML]
  • Le, Q.B., Park, S.J., Vlek, P.L. & Cremers, A.B. (2008). Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system. I. Structure and theoretical specification. Ecological Informatics 3(2), 135-153.
  • Levy, S.T., & Wilensky, U. (2008). Inventing a "mid-level" to make ends meet: Reasoning through the levels of complexity. Cognition and Instruction, 26(1), 1-47.
  • Lysenko, M. & D'Souza, R. M. (2008). A framework for megascale agent based model simulations on graphics processing units. Journal of Artificial Societies and Social Simulation, 11(4).
  • Menezes, R., & Bullen, H. (2008). A study of terrain coverage models. Proceedings of the 2008 ACM symposium on Applied computing. [HTML]
  • Menke, N. (2008). Modeling the Effects of Trauma on Epidermal Wound Healing. Paper presented at Swarmfest, 2008.
  • Merlone, U., Sonnessa, M., & Terna, P. (2008). Horizontal and vertical multiple implementations in a model of industrial districts. Journal of Artificial Societies and Social Simulation, 11(2).
  • Millington, J., Romero-Calcerrada, R., Wainwright, J., & Perry, G. (2008). An agent-based model of Mediterranean agricultural land-use/cover change for examining wildfire risk. Journal of Artificial Societies and Social Simluation, 11(4).
  • Niazi, M. (2008). Self-organized customized content delivery architecture for ambient assisted environments. In Proceedings of the Third international Workshop on Use of P2p, Grid and Agents For the Development of Content Networks (Boston, MA, USA, June 23-23, 2008). HPDC, UPGRADE '08. ACM, New York, NY, 45-54.
  • Niazi, M., & Baig, A.R. (2008). Growth of Research Institutes in Developing Nations. International Research Conference 2008, West Visayas State University, La Paz Iloilo City, Philippines, Feb 27- 29, 2008.
  • Niazi, M., Hussain, A., Baig, A.R., & Bhatti, S. (2008). Simulation of the research process. in Winter Simulation Conference, Miami, FL, pp.1326-1334.
  • Okuyama, T. (2008). Intraguild predation with spatially structured interactions. Basic and Applied Ecology 9(2), 135-144.
  • Pathak, S.A., Jacobson, M.J., Kim, B., Zhang, B., & Feng, D. (2008). Learning the Physics of Electricity with Agent-Based Models: The paradox of productive failure. Paper presented at the 2008 International Conference on Computers in Education. [PDF]
  • Polhill, J. G., Parker, D., Brown, D., & Grimm, V. (2008). Using the ODD protocol for describing three agent-based social simulation models of land-use change. Journal of Artificial Societies and Social Simulation, 11(2).
  • Rahmani, A. T., Saberi, A., Mohammadi, M., Nikanjam, A., Mosabbeb, E. A., & Abdoos, M. (2008, May). SHABaN multi-agent team to herd cows. In International Workshop on Programming Multi-Agent Systems (pp. 248-252). Springer, Berlin, Heidelberg.
  • Rand, W., Blikstein, P., & Wilensky, U. (2008). GoGoBot: Group collaboration, multi-agent modeling and robots. In L. Padgham, D. Parkes, J. Müller & S. Parsons (Eds.), Proceedings of the 7th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS (Vol. 3, pp. 1717-1722). Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). [PDF]
  • Russell, E., & Wilensky, U. (2008, May). Consuming spatial data in NetLogo using the GIS Extension. Paper presented at the annual meeting of the Swarm Development Group, Chicago, IL.
  • Sakellariou, I., Kefalas, P., Stamatopoulou, I. (2008). Enhancing NetLogo to Simulate BDI Communicating Agents. In J. Darzentas et al. (Eds.), Proceedings of 5th Hellenic Conference on Artificial Intelligence, SETN 08, (pp. 263-275). Syros, Greece: Springer-Verlag. [PDF]
  • Sakellariou, I., Kefalas, P., Stamatopoulou, I. (2008). Teaching Intelligent Agents using NetLogo. Paper presented at the ACM-IFIP Informatics Education Europe III Conference, IEEIII 2008, Venice, Italy, December 4-5, 2008. [PDF]
  • Schumann, D., & Simon, A. (2008). Public acceptance of CO2 capture and storage (CCS): Simulating the impact of communication. Paper presented at ZUMA (Zentrum für Umfragen, Methoden und Analysen, Centre for surveys, methods and analyses) Advanced Simulation Workshop, April 8-11, Koblenz.
  • Sengupta, P., & Wilensky, U. (2008, March). Designing across ages: On the low-threshold-high-ceiling nature of NetLogo-based learning environments. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
  • Sengupta, P., & Wilensky, U. (2008, March). On the representational and epistemological affordances of NetLogo-based science curricula. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
  • Sengupta, P., & Wilensky, U. (2008). On learning electricity in 7th grade with multi-agent based computational models (NIELS). In G. Kanselaar, J. van Merriëboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 123-125). Utrecht, The Netherlands: ISLS. [PDF]
  • Sengupta, P., & Wilensky, U. (2008). Learning activities as tools for formative assessment - Case study of a computational multi-agent based electricity curriculum (NIELS: NetLogo Investigations In Electromagnetism). In G. Kanselaar, J. van Merriëboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 383-391). Utrecht, The Netherlands: ISLS.
  • Sengupta, P., & Wilensky, U. (2008). On the learnability of electricity as a complex system. In G. Kanselaar, J. van Merriëboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 258-264). Utrecht, The Netherlands: ISLS.
  • Stonedahl, F., Kornhauser, D., Russell, E., Brozefsky, C., Verreau, E., Tisue, S., & Wilensky, U. (2008, May). Tinkering with turtles: An overview of NetLogo's Extensions API. Paper presented at the annual meeting of the Swarm Development Group, Chicago, IL. [PDF]
  • Stonedahl, F., Rand, W., & Wilensky, U. (2008, July). CrossNet: A framework for crossover with network-based chromosomal representations. Paper presented at the 2008 Genetic and Evolutionary Computation Conference (GECCO), Atlanta, GA. [PDF]
  • Stonedahl, F., Rand, W., & Wilensky, U. (2008, May). Multi-agent learning with a distributed genetic algorithm: Exploring innovation diffusion on networks. Paper presented at the Seventh International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), Estoril, Portugal. [PDF]
  • Sudhira, H. S. (2008).Modelling the Strategic Interactions in Urban Governance. In Proceedings of Third International Conference on Public Policy and Governance, Centre for Public Policy, Indian Institute of Management - Bangalore, India.
  • Usher, C, Tilson, L, Olsen, J, Jepsen, M, Walsh, C, Barry, M & Jepsen, MR (2008). Cost-effectiveness of human papillomavirus vaccine in reducing the risk of cervical cancer in Ireland due to HPV types 16 and 18 using a transmission dynamic model. Vaccine , 26(44), pp. 5654-5661.
  • Voinov, A. A., Sven Erik, J., & Brian, F. (2008). Software. Encyclopedia of Ecology (pp. 3270-3277). Oxford: Academic Press.
  • Wang, B. J., Ji, F., & Zhou, M. (2008, September). Investigate on the forming and its evolving of complex diffusion network based on Netlogo. In 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings (pp. 236-242). IEEE.
  • Wang, J., Dam, G., Yildrim, S., Rand, W., Wilensky, U., & Houk, J. C. (2008). Reciprocity between the cerebellum and the cerbral cortex: Nonlinear dynamics in microscopic modules. Complexity, 14(2), 29-45. [PDF]
  • Weisberg, M., & Reisman, K. (2008). The Robust Volterra Principle. Philosophy of Science, 75, 106–131.
  • Whitmeyer, J., Carmichael, T., Eichelberger, C., Hadzikadic, M., Khouja, M., Saric, A., & Sun, M. (2008). A Computer Simulation Laboratory for Social Theories. Paper presented at the 2008 IEEE/WIC/ACM International Conference on Intelligence Agent Technology, Sydney, Australia, December 2008.
  • Wilkerson-Jerde, M., Sengupta, P., & Wilensky, U. (2008). Perceptual supports for sense-making: A case study using multi-agent based computational learning environments. In G. Kanselaar, J. van Merriënboer, P. Kirschner & T. de Jong (Eds.), Proceedings of the Eighth International Conference for the Learning Sciences, ICLS2008 (Vol. 3, pp. 151-152). Utrecht, The Netherlands: ISLS. [PDF]
  • Will, O. & Hegselmann, R. (2008). A replication that failed: On the computational model in 'Michael W. Macy and Yoshimichi Sato: Trust, cooperation and market formation in the U.S. and Japan. Proceedings of the National Academy of Sciences, May 2002'. Journal of Artificial Societies and Social Simulation, 11(3).

2007

  • Abrahamson, D., & Wilensky, U. (2007). Learning Axes and Bridging Tools in a Technology-Based Design for Statistics. International Journal of Computers for Mathematical Learning. 12(1), 23-55.
  • Abrahamson, D., Blikstein, P., & Wilensky, U. (2007). Classroom Model, Model Classroom: Computer-Supported Methodology for Investigating Collaborative-Learning Pedagogy. Proceedings of the Computer-Supported Collaborative Learning conference, New Brunswick, NJ.
  • Abrahamson, D., Wilensky, U., & Levin, J. (2007). Agent-Based Modeling as a Bridge Between Cognitive and Social Perspectives on Learning. In D. Abrahamson (Org.), Learning Complexity: Agent-Based Modeling Supporting Education Research on Student Cognition in Social Contexts. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Albiero, F., Fitzek, F. H., & Katz, M. D. (2007). Analysis of Cooperative Power Saving Strategies with NetLogo. In Cognitive Wireless Networks (pp. 603-620). Springer, Dordrecht.
  • Albiero, F., Fitzek, F. H., & Katz, M. D. (2007). Introduction to NetLogo. In Cognitive Wireless Networks (pp. 579-602). Springer, Dordrecht.
  • Bakshy, E., & Wilensky, U. (2007). Turtle Histories and Alternate Universes; Exploratory Modeling with NetLogo and Mathematica. In M. J. North, C. M. Macal & D. L. Sallach (Eds.), Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence (pp. 147-158). IL: Argonne National Laboratory and Northwestern University. [PDF]Blikstein, P., & Wilensky, U. (2007). Bifocal modeling: a framework for combining computer modeling, robotics and real-world sensing. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Blikstein, P., & Wilensky, U. 2007). Modeling manifold epistemological stances with agent-based computer simulation. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Blikstein, P., Abrahamson, D., & Wilensky, U. (2007). Multi-agent simulation as a tool for investigating cognitive-developmental theory. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Blikstein, P., Rand, W., & Wilensky, U. (2007). Just a Cog in the Machine: Participatory Robotics as a Powerful Tool for Understanding Collaborative Learning. Computer Supported Collaborative Learning (CSCL), Rutgers University, Rutgers, NJ, USA. [PDF]
  • Blikstein, P., Rand, W., & Wilensky, U. (2007). Examining group behavior and collaboration using ABM and robots. In M. J. North, C. M. Macal & D. L. Sallach (Eds.), Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence (pp. 159-172). IL: Argonne National Laboratory and Northwestern University. [PDF]
  • Bryson, J., Ando, Y., Lehmann, H. (2007). Agent-based modelling as scientific method: A case study analysing primate social behavior. Philosophical Transactions of the Royal Society, 362, pp.1685-1695. [PDF]
  • Capizzo, M.C. and Bonura, A. and Fazio, C. (2007). Characteristic Properties of Semiconductors Through Experiments and Modelling. Paper presented at GIREP EPEC Conference Frontiers of Physics Education, 26-31 August, 2007, Opatija, Croatia. [HTML]
  • Damaceanu, R-C. (2007). An agent-based computational study of wealth distribution in function of resource growth interval using NetLogo. Applied Mathematics & Computation, Vol 12 (43). [HTML]
  • Dekker, A. (2007). Studying organisational topology with simple computational models. Journal of Artificial Societies and Social Simulation, 10(4).
  • Doi, M. & Kawaguchi, I. (2007). Ecological impacts of umbrella effects of radiation on the individual members. Journal of Environmental Radioactivity 96(1-3), 32-38.
  • Fitzek, Frank H.P. & Katz, Marcos D. (2007). Cognitive Wireless Networks. Springer. [HTML]
  • Gilbert, N. (2007). Agent-Based Models. Sage Publications.
  • Gong, X. & Xiao, R. (2007). Research on multi-agent simulation of epidemic news spread characteristics. Journal of Artificial Societies and Social Simulation, 10(3).
  • Hmelo-Silver, C., Liu, L., Gray, S., Finkelstein, H., & Schwartz, R. (2007). Enacting things differently: Using NetLogo models to learn about complex systems. In biennial meeting of European Association for Research on Learning and Instruction.
  • Hundhausen, C.D. & Brown, J.L. (2007). What You See Is What You Code: A "live" algorithm development and visualization environment for novice learners. Journal of Visual Languages & Computing 18(1), 22-47.
  • Izquierdo, S.S., & Izquierdo, L.R. (2007). The impact of quality uncertainty without asymmetric information on market efficiency. Journal of Business Research, Volume 60, Issue 8, pp. 858-867. []
  • Johnson, I. D. (2007). Mathematical modeling with NetLogo: Cognitive demand and fidelity. [PDF]
  • Kahn, K. (2007). Building computer models from small pieces. In Proceedings of the 2007 Summer Computer Simulation Conference (San Diego, California, July 16 - 19, 2007). Summer Computer Simulation Conference (931-936). San Diego, CA: Society for Computer Simulation International. [PDF]
  • Kahn, K. (2007). Comparing Multi-Agent Models Composed from Micro-Behaviours. Retrieved February 25, 2010 from http://dfl.cetis.ac.uk/wiki/uploads/0/0d/M2M_2007_C2L_v4.pdf. [PDF]
  • Katzper, M. (2007). Roles for autonomous physiologic agents; an oxygen supply and demand example. Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come, pp 1483 - 1486. [HTML]
  • Koné, M., Berlanga, Sloep, P. B., Koper, R. (2007). A Learning Community Simulation. Retrieved February 25, 2010 from https://dspace.ou.nl/bitstream/1820/1247/1/KoneetalWBC2007.pdf. [PDF]
  • Kornhauser, D., Rand, W., & Wilensky, U. (2007). Visualization Tools for Agent-Based Modeling in NetLogo. Paper presented at Agent2007, Chicago, November 15-17. [PDF]
  • Lam, R. (2007). Agent-based simulations of service policy decisions. Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come. [PDF]
  • Lee, K. C. & Lee, N. (2007). CARDS: Case-based reasoning decision support mechanism for mutli-agent negotiation in mobile commerce. Journal of Artificial Societies and Social Simulation, 10(2).
  • Levy, S., & Wilensky, U. (2007). How do I get there...straight, oscillate or inch? High-school students' exploration patterns of Connected Chemistry. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Levy, S.T., & Wilensky, U. (2007). Action across levels (AAL): A multiple levels perspective on what it means to make sense of complex systems. Paper presented at the EARLI 2007 conference, Budapest, Hungary, September 2007. [PDF]
  • Miller, J.H. & Page, S.E. (2007). Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.
  • Momen, S., Amavasai, B.P., Siddique, N.H. (2007). Mixed Species Flocking for Heterogeneous Robotic Swarms. IEEE Eurcon 2007, The International Conference on 'computer as a tool,' pp: 2329-2336. [HTML]
  • North, M. & Macal, C. (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simluation. Oxford University Press.
  • North, M. J, & Macal, C. M. (2007). Agent-based modeling and simulation: desktop ABMS. Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come. [HTML]
  • Niazi, M., & Baif, A.R. (2007). Phased Approach to Simulation of Security Algorithms for Ambient Intelligent (AmI) Environments. Winter Simulation Conference 07 (WSC07), PhD Student Colloquium, December 7-11, 2007.
  • Ottino-Loffler, J., Rand, W., & Wilensky, U. (2007). Coevolution of Predators and Prey in a Spatial Model. Paper presented at the GECCO 2007 Conference. London, England. July 7-11. [PDF]
  • Rand, W., & Sondahl, F. (2007). The El Farol Bar Poblem and Computational Effort: Why People Fail to Use Bars Efficiently. In M. J. North, C. M. Macal & D. L. Sallach (Eds.), Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence (pp. 71-86). IL: Argonne National Laboratory and Northwestern University. [PDF]
  • Rand, W., & Wilensky, U. (2007). Full-Spectrum Modeling: From Simplicity to Elaboration and Realism in Urban Pattern Formation. Paper presented at the North American Association Computational Social and Organization Sciences conference (NAACSOS), Atlanta, GA. [PDF
  • Sengupta, P., Wilkerson, M. & Wilensky, U. (2007). On The Relationship Between Spatial Knowledge And Learning Electricity: Comparative Case Studies of Students Using 2D And 3D Emergent, Computational Learning Environments. Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, April 9-13. [PDF]
  • Sklar, E. (2007). Software Review: NetLogo, a Multi-agent Simulation Environment. Artificial Life, 13, 303-311.
  • Sondahl, F., & Rand, W (2007). Evolution of Non-Uniform Cellular Automata using a Genetic Algorithm: Diversity and Computation. Presented at the GECCO 2007 Conference, London, UK.
  • Sondahl, F., & Rand, W. (2007). Multi-agent Communication Disorders: Dynamic Breeding Networks in Genetic Algorithms. Presentation at Swarmfest 2007 Conference, DePaul University, Chicago, IL. July 2007.
  • Stamatopoulo I., Sakellariou, I., Kefala P., & Eleftheraki G. (2007). Formal Modelling for In-silico Experiments with Social Insect Colonies. Paper presented at the 11th Panhellenic Conference in Informatics, 18-20 May 2007. Patras, Greece. [PDF]
  • Sudhira, H.S., & Ramachandra, T.V. (2007). Integrated Spatial Planning Support System for Managing Urban Sprawl. Reviewed Paper #199, In Conference Proceedings of 10th International Conference on Computers in Urban Planning and Urban Management, Iguassu Falls, PR, Brazil.
  • Sudhira, H.S., & Ramachandra, T.V. (2007). Modelling the dynamics of urban sprawl using system dynamics and agent-based models. International Conference on Framing Land Use Dynamics – II, University of Utrecht, The Netherlands, 18-20 April 2007.
  • Tatara, E., North, M.J., Howe, T., Collier, N., & Parker, M. (2007). Building Models in Repast Symphony: A predator-prey example. Paper presented at NAACSOS 2007, Atlanta, GA.
  • Thompson, J.W., & Sorvig, K. (2007). Sustainable Landscape Construction: A Guide to Green Building Outdoors. 2nd edition. Island Press. [HTML]
  • Wilensky, U., & Centola, D. (2007). Simulated evolution: Facilitating students' understanding of the multiple levels of fitness through multi-agent modeling. Paper presented at the Evolution Challenges Conference. Phoenix, AZ. November 3, 2007.
  • Wilensky, U., & Rand, W. (2007). Making models match: Replicating agent-based models. Journal of Artificial Societies and Social Simulation (JASSS), 10(4). [PDF]
  • Zhang, T. & Zhang, D. (2007). Agent-based simulation of consumer purchase decision-making and the decoy effect. Journal of Business Research 60(8), 912-922.
  • Zhao C., Zhong N., & Hao Y. (2007). AOC-by-Self-discovery Modeling and Simulation for HIV. In S. Istrail, P. Pevzner, and M. Waterman (Eds.), Life System Modeling and Simulation (4689/2007, 462-469). Springer: Netherlands. [PDF]

2006

  • Abrahamson, D., Berland, M.W., Shapiro, R. B., Unterman, J. W., & Wilensky, U. (2006). Leveraging epistemological diversity through computer-based argumentation in the domain of probability. For the Learning of Mathematics, 26(3), 39-55.
  • Abrahamson, D., Janusz, R. M. & Wilensky, U. (2006). There once was a 9-Block... -- A middle-school design for probability and statistics. Journal of Statistics Education, 14(1). [HTML]
  • Alessa, L. N., Laituri, M., & Barton, M. (2006). An "all hands" call to the social science community: Establishing a community framework for complexity modeling using agent based models and cyberinfrastructure. Journal of Artificial Societies and Social Simulation, 9(4).
  • An, G. (2006). Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling. Journal of critical care, 21(1), 105-110.
  • Andruss, C. (2006). Computational Modeling of the Interaction of the T. cruzi Parasite and its Environment. Final research report presented at the VCU Bio Informatics Institute, Virginia. [PDF]
  • Berland, M.& Wilensky, U. (2006).Constructionist collaborative engineering: Results from an Implementation of PVBOT. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Blikstein, P. , Abrahamson, D., & Wilensky, U. (2006). Minsky, mind, and models: Juxtaposing agent-based computer simulations and clinical-interview data as a methodology for investigating cognitive-developmental theory. Paper presented at the annual meeting on the Jean Piaget Society, Baltimore, MD, June 1-3. [HTML]
  • Blikstein, P. & Wilensky, U. (2006). An atom is known by the company it keeps: A constructionist learning environment for Materials Science using multi-agent simulation. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA, April 7-11. [PDF]
  • Blikstein, P. & Wilensky, U. (2006). 'Hybrid modeling': Advanced scientific investigation linking computer models and real-world sensing. Paper presented at the Proceedings of the Seventh International Conference of the Learning Sciences, Bloomington, IL, June 27-July 1. [PDF]
  • Blikstein, P. & Wilensky, U. (2006). From inert to generative modeling: Case studies of multi-agent-based simulation in undergraduate engineering education. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. [PDF]
  • Blikstein, P. & Wilensky, U. (2006). A case study of multi-agent-based simulation in undergraduate materials science education. Paper presented at the Annual Conference of the American Society for Engineering Education, Chicago, IL, June 18-21.
  • Blikstein, P., & Wilensky, U. (2006). The Missing Link: A Case Study of Sensing-and-Modeling Toolkits for Constructionist Scientific Investigation. Proceedings of the International Conference for Advanced Learning Technologies (ICALT 2006) (pp. 980-982). Kerkrade, The Netherlands. [PDF]
  • Blikstein, P., Rand, W. & Wilensky, U. (2006). Participatory, embodied, multi-agent simulation. Paper presented at AAMAS 2006. [PDF]
  • Bonura, A., Capizzo, M. C., & Fazio, C. (2006). A Pedagogical Approach to Modelling Electric Conduction in Solids. In Proceedings of the XXI GIREP International Conference “Modelling in Physics and Physics Education” (pp. 824-830). AMSTEL Institute, University of Amsterdam.
  • Bryson, J. J., Caulfield, T. J., & Drugowitsch, J. (2006). Integrating Life-Like Action Selection into Cycle-Based Agent Simulation Environments. in Proceedings of Agent 2005: Generative Social Processes, Models, and Mechanisms, Michael North, David L. Sallach and Charles Macal eds., pp. 67-81, Argonne National Laboratory 2006. [PDF]
  • Felsen, M., Watson, B., & Wilensky, U. (2006). Urban Complexity + Emergence: Procedural Modeling of City Activity and Form. In Surfacing Urbanisms: Recent Approaches to Metropolitan Design (pp. 261-265). Pasadena, CA: Woodbury University. [PDF]
  • Garofalo, M. (2006). Modeling the El Farol Bar Problem in NetLogo. Submitted for publication. [PDF]
  • Graham, S. (2006). Networks, Agent-Based Modeling, and the Antonine Itineraries. In The Journal of Mediterranean Archaeology 19.1: 45-64.
  • Hammond, R., & Axelrod, R. (2006). The Evolution of Ethnocentrism. Journal of Conflict Resolution, 50(6), 926-936. [HTML]
  • Hills, T. T. (2006). Building" ethical agent" based simulations: A case study of a pathological problem in altruistic punishment. In ALife Ethics Workshop Artificial Life X, Bloomington, IN, USA.
  • Izquierdo, L.R. and Polhill, J.G. (2006). Is your model susceptible to floating-point errors? Journal of Artificial Societies and Social Simulation 9(4)4. [PDF]
  • Jacobson, M. & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), pp. 11-34. [PDF]
  • Jepsen, MR , Leisz, S , Rasmussen, K , Jakobsen, J, Moller-Jensen, L & Christiansen, L (2006). Agent-based modelling of shifting cultivation field patterns, Vietnam. International Journal of Geographical Information Science , 20(9), pp. 1067-1085.
  • Joyce, D., Kennison, J., Densmore, O., Guerin, S., Barr, S., Charles, E., et al. (2006). My Way or the Highway: a More Naturalistic Model of Altruism Tested in an Iterative Prisoners' Dilemma. Journal of Artificial Societies and Social Simulation, 9(2).
  • Koehler, M. (2006). Using NetLogo in the Data Farming Environment.
  • Kumar, S. & Mitra, S. (2006). Self-organizing traffic at a malfunctioning intersection. Journal of Artificial Societies and Social Simulation, 9(4).
  • Lechner, T., Watson, B., Ren, P., Wilensky, U., Tisue, S. & Felsen, M. (2006). Procedural modeling of urban land use. ACM SIGGRAPH 2006 conference. [PDF]
  • Levy, S.T, Novak, M. & Wilensky, U. (2006). Students' foraging through the complexities of the particulate world: Scaffolding for independent inquiry in the connected chemistry (MAC) curriculum. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. [PDF]
  • Levy, S.T. & Wilensky, U. (2006). Gas laws and beyond: Strategies in exploring models of the dynamics of change in the gaseous state. Paper presented at the annual meeting of the National Association for Research in Science Teaching, San Francisco, CA. [PDF]
  • Levy, S.T. & Wilensky, U. (2006). Emerging knowledge through an emergent perspective: High-school students' inquiry, exploration and learning in the Connected Chemistry curriculum. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. [PDF]
  • Makowsky, M. (2006). An agent-based model of mortality shocks, intergenerational effects, and urban crime. Journal of Artificial Societies and Social Simulation, 9(2).
  • Maroulis, S., & Wilensky, U. (2006). Using agent-based modeling to understand the social dynamics of schools. Paper presented at the Teacher Networks conference, Northwestern University, Evanston, IL, November 8.
  • Muscalagiu, I., Jiang, H., & Emil, P. H. (2006, September). Implementation and Evaluation Model for the Asynchronous Search Techniques: From a Synchronously Distributed System to an Asynchronous Distributed System. In 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (pp. 209-216). IEEE.
  • Pugh, G. A. (2006, March). Agent-based simulation of discrete-event systems. In Proceedings of the 2006 Illinois-Indiana and North Central Joint Section Conferences,(Mar 31-Apr 1).
  • Railsback, S., Lytinen, S., & Jackson, S. (2006). Agent-based Simulation Platforms: Review and Development Recommendations. SIMULATION, 82(9), 609-623.
  • Rand, W. (2006). Machine Learning Meets Agent-Based Modeling: When Not to Go to a Bar. Paper presented at Agent 2006, Chicago, IL. [PDF]
  • Rand, W. & Wilensky, U. (2006). Verification and Validation through Replication: A Case Study Using Axelrod and Hammond's Ethnocetnrism Model. Paper presented at NAACSOS 2006, South Bend, IN, June 2006. [PDF]
  • Rand, W. & Wilensky, U. (2006). NetLogo 3.1: Low Threshold, No Ceiling. Paper presented at NAACSOS 2006, South Bend, IN, June 2006.
  • Rand, W., Blikstein, P. & Wilensky, U. (2006). Widgets, Planets, and Demons: the Case for the Integration of Human, Embedded, and Virtual Agents via Mediation. Paper presented at Swarmfest 2006, South Bend, IN, June 2006. [PDF]
  • Richiardi, M., Leombruni, R., Saam, N., & Sonnessa, M. (2006). A common protocol for agent-based social simulation. Journal of Artificial Societies and Social Simulation, 9(1).
  • Riggs, W. W. (2006, October). Agent-based modeling as constructionist pedagogy: An alternative teaching strategy for the social sciences. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1417-1423). Association for the Advancement of Computing in Education (AACE).
  • Roberts, B. (2006). Blocked exit syndrome: A serious risk in venue emergencies. Fire & Safety Magazine, Fall 2006.
  • Sengupta, P. & Wilensky, U. (2006). NIELS: An agent-based modeling environment for learning electromagnetism. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Stondahl, F., Tisue, S. & Wilensky, U. (2006). Breeding faster turtles: Progress towards a NetLogo compiler. Paper presented at Agent 2006, Chicago, IL. [PDF]
  • Thorne, B., Bailey, A, Benedict, K., & Peirce-Cottler, S. (2006). Modeling blood vessel growth and leukocyte extravasation in ischemic injury: an integrated agent-based and finite element analysis approach. Journal of Critical Care, 21(4), 346. [HTML]
  • Unterman, J. & Wilensky, U. (2006). PANDA BEAR: Perimeter and area by embodied agent reasoning. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Wilensky, U. (2006). Complex systems and restructuration of scientific disciplines: Implications for learning, analysis of social systems, and educational policy. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA, April 7-11.
  • Wilensky, U. (2006). Promoting ABM literacy: implications for design, scientific content and education. Paper presented at Agent 2006, Chicago, IL.
  • Wilensky, U. & Abrahamson, D. (2006). Is a disease like a lottery?: Classroom networked technology that enables student reasoning about complexity. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Wilensky, U. & Reisman, K. (2006). Thinking Like a Wolf, a Sheep or a Firefly: Learning Biology through Constructing and Testing Computational Theories -- an Embodied Modeling Approach. Cognition & Instruction, 24(2), pp. 171-209. [PDF]
  • Wilensky, U., & Papert, S. (2006). Restructurations: Reformulations of Knowledge Disciplines through a change in representational forms. Unpublished working paper. Evanston, IL. Center for Connected Learning and Computer-Based Modeling. Northwestern University.
  • Xie, Q., & Tinker, R. (2006). Molecular Dynamics Simulations of Chemical Reactions for Use in Education. Journal of Chemical Education, 83(1), 77. [PDF]

2005

  • Abraham, R., Miller, M., & Miller, J. (2005). Emerging 4D graphics for math and science education. Paper presented at the ACM SIGGRAPH 2005 Educators program, Los Angeles, California. [PDF]
  • Abrahamson, D., Blikstein, P., Lamberty, K. K. & Wilensky, U. (2005). Mixed-media learning environments. Paper presented at the annual meeting of Interaction Design and Children 2005, Boulder, Colorado.
  • Abrahamson, D., & Wilensky, U. (2005). Collaboration and equity in classroom activities using Statistics As Multi-Participant Learning-Environment Resource (S.A.M.P.L.E.R.). In W. Stroup and U. Wilensky (Chairs), & C. D. Lee (Discussant), "Patterns in group learning with next-generation network technology." Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 11 - 15. [PDF]
  • Abrahamson, D. & Wilensky, U. (2005). Piaget? Vygotsky? I'm game: Agent-based modeling for psychology research. Paper presented at the annual meeting of the Jean Piaget Society. Vancouver, Canada, June 1-3. [PDF]
  • Abrahamson, D. & Wilensky, U. (2005). ProbLab goes to school: Design, teaching, and learning of probability with multi-agent interactive computer models. Paper presented at the Fourth Conference of the European Society for Research in Mathematics Education, San Feliu de Guixols, Spain. [PDF]
  • Abrahamson, D., & Wilensky, U. (2005). The stratified learning zone: Examining collaborative-learning design in demographically-diverse mathematics classrooms. In D. Y. White (Chair) & E. H. Gutstein (Discussant), "Equity and diversity studies in mathematics learning and instruction." Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 11 - 15
  • Abrahamson, D. & Wilensky, U. (2005). Understanding chance: From student voice to learning supports in a design experiment in the domain of probability. In G.M. Lloyd, M. Wilson, J. L. M. Wilkins & S.L. Behm (Eds.), Proceedings of the Twenty Seventh Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education.Roanoke, VA. [PDF]
  • Agar, M. (2005). Agents in living color: Towards emic agent-based models. Journal of Artificial Societies and Social Simulation, 8(1).
  • Aldinger, T., Kopf, S., Scheele, N., & Effelsberg, W. (2005). Participatory Simulation of a Stock Exchange. [PDF]
  • Amos, M., & Wood, A. (2005). Effect of door delay on aircraft evacuation time. Arxiv preprint cs.MA/0509050. [PDF]
  • Banos, A. Godara, A., & Lassarre, S. (2005). Simulating pedestrians and cars behaviours in a virtual city: an agent-based approach. Paper presented at the European Conference on Complex Systems, Paris, 14-18 November. [PDF]
  • Barton, A. (2005). Modelling the Foraging Patterns of a Colony of Co-operating Army Ant Agents using NetLogo. NRC/ERB-1122. February 21, 2005. [PDF]
  • Beer, M. D., & Hill, R. (2005). Integrating Multi-Agent Systems into the Wider Computing Curriculum. Paper presented at the The AAMAS-2005 Workshop on Teaching Multi-Agent Systems - TeachMAS Utrecht, the Netherlands.
  • Berland, M., & Wilensky, U. (2005). Complex play systems -- Results from a classroom implementation of VBOT. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 11 - 15.
  • Blikstein, P. & Wilensky, U. (2005). Less is more: Agent-based simulation as a powerful learning tool in materials science. Paper presented at the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), Utrecht, Netherlands. [PDF]
  • Blikstein, P., Abrahamson, D. & Wilensky, U. (2005). NetLogo: Where we are, where we're going. Paper presented at the annual meeting of Interaction Design and Children. Boulder, Colorado. [PDF]
  • Cace, I., & Bryson, J. (2005). Agent Based Modelling of Communication Costs: Why information can be free. In Emergence and Evolution of Linguistic Communication C. Lyon, C. L Nehaniv and A. Cangelosi, eds., pp. 305-322, Springer 2007. [PDF]
  • Calvez, B., & Hutzler, G. (2005). Parameter Space Exploration of Agent-Based Models. In Knowledge-Based Intelligent Information and Engineering Systems, 3684, 633-639. Springer Berlin / Heidelberg. [PDF]
  • Capizzo, M. C., Fazio, C., & Sperandeo-Mineo, R. M. (2005). Object based modelling environments applied to science education. Methods and Technologies for Learning, 34, 193.
  • Da Costa, L. E., & Terhesiu, D. (2005). A simple model for the diffusion of ideas. Research Project for the Complex Systems Summer School, SFI (Santa Fe, New Mexico). [PDF]
  • de Marchi, S. (2005). Computational and Mathematical Modeling in the Social Sciences. Cambridge University Press.
  • Forsyth, A. J., Horne, G. E., Upton, S. C., & Center, A. T. (2005). Marine Corps applications of data farming. Paper presented at the Proceedings of the 2005 Winter Simulation Conference. [PDF]
  • Gershenson, C. (2005). Self-Organizing Traffic Lights. Complex Systems, 16(1):29–53. [PDF]
  • Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist (second ed.). Milton, UK: Open University Press.
  • Gobert, J., Buckley, B., Dede, C., Levy, S., Slotta, J., & Wilensky, U. (2005). Technology features that support research through logging of student interactions with models. Paper presented at the Winter Text Conference, Jackson Hole, WY, January 20-23.
  • Gulyas, L., Bartha, S., Kozsik, T., Szalai, R., Korompai, A., & Tatai, G. (2005). The Multi-Agent Simulation Suite (MASS) and the Functional Agent-Based Language of Simulation (FABLES). Paper presented at Swarmfest2005, Torino, Italy, June 5-7, 2005. [PDF]
  • Hoffer, L. (2005). Using Agent-based Modeling to Better Understand Local Heroin Dealing. Organizations and Drug Markets, Washington University.
  • Janota, A., Rastocny, K., Zahradnik, J. (2005). Multi-agent approach to traffic simulation in NetLogo environment - Level Crossing model. Paper presented at the 5th International Conference Transport Systems Telematics TST '05, Silesian University of Technology. [PDF]
  • Janota, A., Spalek, J., & Hrbče J. (2005). NetLogo – prostredie na tvorbu multiagentových systémov a jeho využitie na simuláciu riadenia železničného priecestia. AT&P Journal, PLUS7.
  • Keshtkar, F., Gueaieb, W., & White, A. (2005). An Agent-based Model for Image Segmentation. Paper presented at the 13th Multi-disciplinary Iranian Researchers Conference in Europe (IRCE'2005), Leeds, United Kingdom, July 2005.
  • Khouadjia, M., Khanfouf, H., & Meshoul, S. (2005). Une Approche adaptative pour la segmentation dÕimages: Implementation sur la plate-forme Multi-agents NetLogo. Working Paper, Laboratoire LIRE, Université Mentouri, Constantine, Algérie.
  • Koehler, M., Tivnan, B., & Bloedorn, E. (2005). Generating Fraud: Agent Based Financial Network Modeling. Paper presented at the NAACSOS Conference 2005, South Bend, IN, June 26-28. [PDF]
  • Koehler, M., Tivnan, B., & Upton, S. (2005). Clustered Computing with NetLogo and Repast J: Beyond Chewing Gum and Duct Tape. Paper presented at the Agent2005 conference, Chicago, IL. [PDF]
  • Koper, R. (2005). Increasing learner retention in a simulated learning network using indirect social interaction. Journal of Artificial Societies and Social Simulation, 8(2).
  • Kopf, S., Scheele, N., Winschel, L., & Effelsberg, W. (2005). Improving Activity and Motivation of Students with Innovative Teaching and Learning Technologies. International Conference on Methods and Technologies for Learning (ICMTL). [PDF]
  • Kuhl, M. E., Steiger, N. M., Armstrong, F. B., & Joines, J. A. (2005). MARINE CORPS APPLICATONS OF DATA FARMING.
  • Lauberte, I. (2005). Using of cellular automata in agent-based simulation for regional development. Paper presented at the 6th Conference on Baltic Studies in Europe, Valmiera, June 17-19, pp. 99-104. [HTML]
  • Laver, M. (2005). Policy and the dynamics of political competition. American Political Science Review, 99(2), 263-281. [PDF]
  • Le, Q.B. (2005). Multi-agent system for simulation of land-use and land-cover change: a theoretical framework and its first implementation for an upland watershed in the Central Coast of Vietnam. Ecology and Development Series 29. Göttingen: Cuvillier Verlag.
  • Levy, S.T., & Wilensky, U. (2005). Students' patterns in exploring NetLogo models, embedded in the Connected Chemistry curriculum. In J. Gobert (Chair) and J. Pellegrino (Discussant), "Logging students' learning in complex domains: Empirical considerations and technological solutions." Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 11 - 15. [PDF]
  • Maroulis, S., & Wilensky, U. (2005). Modeling school districts as complex adaptive systems: A simulation of market-based reform. Paper presented at the 3rd Lake Arrowhead Conference on Human Complex Systems. Lake Arrowhead, CA, May 18-22.
  • Merks, R. & Glazier, J. (2005). A cell-centered approach to developmental biology. Physica A. 352(1), 1 July 2005, 113–130. [HTML]
  • Michel, F., Beurier, G., & Ferber, J. (2005). The TurtleKit Simulation Platform: Application to Multi-Level Emergence. First International Conference on Signal-Image Technology & Internet-Based Systems (Workshop Sessions), Hilton Hotel, Yaoundé, Cameroon, November 27th - December 1st, 2005. [PDF]
  • Morrison, D., & Dennis, B. (2005). MetaLab: supporting social grounding and group task management in CSCL environments through social translucence. Proceedings of the Proceedings of the 2005 conference on Diversity in computing (pp. 20-22). Albuquerque, New Mexico, USA: ACM. [PDF]
  • Muscalagiu, I., Horia-Emil, P., & Panoiu, M. (2005). Determining the Number of Messages Transmitted for the Temporary Links in the Case of ABT Family Techniques. Paper presented at the 7th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05), Timisoara, Romania September 25-29, 2005.
  • Nan, N., Johnston, E., Olson, J., & Bos, N. (2005). Beyond being in the lab: using multi-agent modeling to isolate competing hypotheses. Paper presented at the Conference on Human Factors in Computing Systems, Portland, OR. [HTML]
  • Ohshima, Y. (2005). Kedama: A GUI-Based Interactive Massively Parallel Particle Programming System. Visual Languages and Human-Centric Computing, 2005 IEEE Symposium on, 91-98.
  • Pereira, G. M. (2005). The effects of functional diversity in spatially distributed geographic domains. Paper presented at the Geocomputation 2005 conference. [PDF]
  • Rand, W., Brown, D., Riolo, R. & Robinson, D. (2005). Toward a graphical ABM toolkit with GIS integration. Paper presented at the Agent2005 Conference, Chicago, IL, October 13-14. [PDF]
  • Robertson, D. A. (2005). Agent-Based Modeling Toolkits NetLogo, RePast, and Swarm. Academy of Management Learning and Education, 4(4), 525-527. [PDF]
  • Schellinck, J., & White, T. (2005). Use of Netlogo as a rapid prototyping tool for the creation of more rigorous spatially explicit individual-based biological models. In First Open International Conference on Modeling & Simulation.
  • Sengupta, P. & Wilensky, U. (2005). N.I.E.L.S: An emergent multi-agent based modeling environment for learning physics. Paper presented at the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), Utrecht, Netherlands.
  • Shaker, A., Reeves, D.S. (2005). Self-Stabilizing Structured RingTopology P2P System. Paper presented at the Fifth IEEE International Conference on Peer-to-Peer Computing (P2P'05). [PDF]
  • Stieff, M., Bateman Jr., R. C., Uttal, D. (2005). Teaching and Learning with Three-dimensional Representations. In J. K. Gilbert (Ed.) Models and Modeling in Science Education (Vol 1, 93-120). Springer: Netherlands. [PDF]
  • Stirling, D. (2005). Modeling complex systems. Paper submitted to the Advanced International Colloquium on Building the Scientific Mind. [PDF]
  • Vidal, J. M. (2005). NetLogo for Building Prototype Multiagent Systems. [PDF]
  • Wheeler, S. (2005). It pays to be popular: A study of civilian assistance and guerilla warfare. Journal of Artificial Societies and Social Simulation, 8(4).
  • Wheeler, S. (2005). On the Suitability of NetLogo for the Modelling of Civilian Assistance and Guerrilla Warfare. DSTO Science Systems Laboratory. [PDF]
  • Xie, C. (2005). Molecular Dynamics Simulations Beyond the Lennard-Jones Particles. Submitted to the American Journal of Physics. [PDF]

2004

  • Abrahamson, D. (2004). Embodied spatial articulation: A gesture perspective on student negotiation between kinesthetic schemas and epistemic forms in learning mathematics. In D. E. McDougall and J. A. Ross (Eds.), Proceedings of the Twenty Sixth Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education Vol. 2 (pp. 791 - 797). Windsor, Ontario: Preney. [PDF]
  • Abrahamson, D., & Wilensky (2004). ProbLab: A computer-supported unit in probability and statistics. In M.J. Hoines & A.B. Fuglestad (Eds.), Proceedings of the 28th Annual Meeting of the International Group for the Psychology of Mathematics Education Vol. 1 (p. 369). Bergen: Bergen University College. [PDF]
  • Abrahamson, D., & Wilensky, U. (2004). SAMPLER: Collaborative interactive computer-based statistics learning environment. In the Proceedings of the 10th International Congress on Mathematical Education, Copenhagen, July 4 - 11, 2004. http://www.icme-organisers.dk/tsg11/. [PDF]
  • Abrahamson, D., & Wilensky, U. (2004). S.A.M.P.L.E.R.: Statistics As Multi-Participant Learning-Environment Resource. In U. Wilensky (Chair) and S. Papert (Discussant), "Networking and complexifying the science classroom: Students simulating and making sense of complex systems using the HubNet networked architecture." Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12 - 16. [PDF]
  • Abrahamson, D., Berland, M.W., Shapiro, R.B., Unterman, J.W., & Wilensky, U. (2004). Leveraging epistemological diversity through computer-based argumentation in the domain of probability. In Y. B. Kafai, W. A. Sandoval, N. Enyedy, A. S. Nixon, & F. Herrera (Eds.), Proceedings of The Sixth International Conference of the Learning Sciences (pp. 28 - 35). Mahwah NJ: Lawrence Erlbaum Associates. [PDF]
  • Achorn, E. (2004). Integrating Agent-Based Models with Quantitative and Qualitative Research Methods. [PDF]
  • Agar, M. (2004). An Anthropological Problem, A Complex Solution. Human Organization, 63(4), 411-418. [PDF]
  • Agar, M. (2004). Agents in Living Color: Towards Emic Agent-Based Models. Journal of Artificial Societies and Social Simulation (JASSS), 8 (1): 4. [HTML]
  • An, G. (2004). In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling. Critical care medicine, 32(10), 2050-2060.
  • Aschwanden, P. (2004). Spatial Simulation Model for Infectious Viral Diseases with Focus on SARS and the Common Flu. In Proceedings of the 37th Hawaii International Conference on System Sciences, January 2004. [PDF]
  • Barry, P., & Koehler, M. (2004). Simulation in context; using data farming for decision support. Simulation Conference, 2004. Proceedings of the 2004 Winter, 1. [PDF]
  • Beauchemin, C., & Liao, L. (2004). Tutorial on agent-based models in NetLogo.
  • Berland, M., & Wilensky, U. (2004). Virtual robotics in a collaborative constructionist learning environment. In U. Wilensky (Chair) and S. Papert (Discussant), "Networking and complexifying the science classroom: Students simulating and making sense of complex systems using the HubNet networked architecture." Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12 - 16.
  • Blikstein, P., & Wilensky, U. (2004). MaterialSim: an agent-based simulation toolkit for Materials Science learning. Paper presented at the International Conference on Engineering Education, Gainesville, Florida. [PDF]
  • Bloomquist, K. (2004). Modeling Taxpayers Response to Compliance Improvement Alternatives. Paper presented at the Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), Pittsburgh, PA.
  • Bourjot, C., & Chevrier, V. (2004). A Platform for the analysis of artificial self-organized systems. 2004 IEEE International Conference on Advances in Intelligent Systems-Theory and Applications-AISTA. [PDF]
  • Bryson, J. (2004). Action Selection and Individuation in Agent Based Modelling. [PDF]
  • Buckley, B.C., Gobert, J.D., Kindfield, A., Horwitz, P., Tinker, R., Gerlits, B., Wilensky, U., Dede, C., & Willett, J. (2004). Model-based Teaching and Learning with BioLogica™: What do they learn? How do they learn? How do we know? Journal of Science Education and Technology, 13(1), 23-41. [PDF]
  • Cacciaguerra, S., Roccetti, M., Roffilli, M., & Lomi, A. (2004). A Wireless Software Architecture for Fast 3D Rendering of Agent-Based Multimedia Simulations on Portable Devices. Paper presented at the First Consumer Communications and Networking Conference (CCNC), IEEE Communications Society, Las Vegas, Nevada (USA), January 2004. [PDF]
  • Chitnis, A.B. & Itoh, M. (2004). Exploring alternative models of rostral-caudal patterning in the zebrafish neurectoderm with computer simulations. Current Opinion in Genetics & Development 14(4), 415-421.
  • Densmore, O. (2004). NetLogo 2.0: Graphs, Nodes, and Edges Oh My! January 12, 2004, O'Reilly.com.
  • Friedman, D., & Abraham, R. (2004). Landscape Dynamics and Conspicuous Consumption. Paper presented at the 2004 Proceedings of the Society for Dynamic Games. [PDF]
  • Goldstone, R. L. (2004). The complex systems see-change in education. Journal of the Learning Sciences, 15 (1), pp 35 - 43. [PDF]
  • Gross, T. S., Poliachik, S. L., Ausk, B. J., Sanford, D. A., Becker, B. A., & Srinivasan, S. (2004). Why rest stimulates bone formation: a hypothesis based on complex adaptive phenomenon. Exerc Sport Sci Rev, 32(1), 9-13. [PDF]
  • Henein, C.M., & White, T. (2004). Agent-based modelling of forces in crowds. In Lecture Notes in Computer Science Vol. 3415, 2005. [PDF]
  • Ingalls, R. G., Rossetti, M. D., Smith, J. S., & Peters, B. A. (2004). Simulation in context: Using data farming for decision support. Paper presented at the 2004 Winter Simulation Conference. [PDF]
  • Kottonau, J. (2006, based on 2004 paper). Simulation einer Ameisenstraße mit NetLogo. Lehrer-online. [HTML]
  • Le, Q. B., Park, S., & Vlek, P. L. G. (2004). Simulating Spatial Patterns of Land-use and Land-cover Change: A Multi-agent Model and its Application to an Upland Watershed in Central Vietnam. In K. J. Peters, D. Kirschke, W. Manig, A. Bürkert, R. Schultze-Kraft, L. Bharati, C. Bonte-Friedheim, A. Deininger, N. Bhandari, H. Weitkamp (Eds.), "Rural Poverty Reduction through Research for Development and Transformation" - Proceedings of the Deutscher Tropentag 2004 (p. 67). Berlin: Humboldt-Universität. [PDF]
  • Lechner, T., Watson, B., Wilensky, U., & Felsen, M. (2004). Procedural modeling of land use in cities. Technical report NWU-CS-04-38. Evanston, IL: Northwestern University, Computer Science department. [PDF]
  • Levy, S.T., & Wilensky, U. (2004). Making sense of complexity: Patterns in forming causal connections between individual agent behaviors and aggregate group behaviors. In U. Wilensky (Chair) and S. Papert (Discussant), "Networking and complexifying the science classroom: Students simulating and making sense of complex systems using the HubNet networked architecture." Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12 - 16.
  • Levy, S.T., Kim, H., & Wilensky, U. (2004). Connected Chemistry - A study of secondary students using agent-based models to learn chemistry. In J. Gobert (Chair) and N. H. Sabelli (Discussant), "Modeling Across the Curriculum (MAC): Technology, Pedagogy, Assessment, & Research." Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA, April 12 - 16. [PDF]
  • Miller, J.H., & Page, S.E. (2004). The Standing Ovation Problem. Complexity 9(5), pp 8-16. [HTML]
  • Newman, Chris. "Revolution by Osmosis: A Case Study of West Florida, Texas, California and Hawaii." Proceedings of the Fifth Annual International Conference on Complex Systems, May, 2004.
  • Prodromou, T. (2004). Distribution as emergent phenomena. Proceedings of the British Society for Research into Learning Mathematics, 24(1), 49-54. [PDF]
  • Sallez, Y., Berger, T., & Tahon, C. (2004). Simulating intelligent routing in flexible manufacturing systems using NetLogo. Industrial Technology, 2004 IEEE International Conference.
  • Sancho-Royo, A., Pelta, D. A., Verdegay, J. L., & Gonzalez, J. R. (2004). Evaluation of Cooperative Strategies in Optimization Problems. [PDF]
  • Schellinck, J., & White, T. (2004). Use of NetLogo as a rapid prototyping tool for the creation of more rigorous spatially explicit individual-based biological models. First Open International Conference on Modeling & Simulation.
  • Singh, A., & Haahr, M. (2004). Topology Adaptation in P2P Networks Using Schelling's Model. Proceedings of the Workshop on Games and Emergent Behaviors in Distributed Computing Environments (Birmingham, UK, Sept. 2004). [PDF]
  • Solberg, J. (2004). Assemble time for self-assembling square tiles. Project for MECH 448. [PDF]
  • Tisue, S., & Wilensky, U. (2004). NetLogo: Design and Implementation of a Multi-Agent Modeling Environment. Paper presented at the Agent2004 Conference, Chicago, IL. (This is a combined, revised, and updated version of our ICCS and SwarmFest papers from earlier this year.) [PDF]
  • Tisue, S., & Wilensky, U. (2004). NetLogo: A simple environment for modeling complexity. Paper presented at the International Conference on Complex Systems, Boston, May 16 - 21. [PDF]
  • Tisue, S., & Wilensky, U. (2004). NetLogo: Design and implementation of a multi-agent modeling environment. Paper presented at SwarmFest, Ann Arbor, MI, May 9 - 11. [PDF]
  • Tobias, R. & Hofmann, C. (2004). Evaluation of free Java-libraries for social-scientific agent based simulation. Journal of Artificial Societies and Social Simulation, 7(1).
  • Vag, A. (2004). First generation multi-agent models and their upgrades. Interdisciplinary Description of Complex Systems, 2(1), 95-103. [PDF]
  • Vidal, J. M., Buhler, P., & Goradia, H. (2004). The Past and Future of Multiagent Systems. Paper presented at the AAMAS Workshop on Teaching Multi-Agent Systems. [PDF]
  • Winschel, L., & Kopf, S. (2004). Entwicklung einer Börsensimulation mit der multiagentenbasierten Entwicklungsumgebung NetLogo. Universität Mannheim / Institut für Informatik. [PDF]
  • Wokoma, I., Sacks, L., & Marshall, I. (2004). A Self-Organising Clustering Algorithm for Wireless Sensor Networks. University College, London. [DOC]

2003

  • Abrahamson, D., & Wilensky, U. (2003). The quest of the bell curve: A constructionist approach to learning statistics through designing computer-based probability experiments. In M. A. Mariotti (Ed.), Proceedings of the Third Conference of the European Society for Research in Mathematics Education. Pisa, Italy: University of Pisa. Retrieved June 1, 2009, from http://www.dm.unipi.it/didattica/CERME3/proceedings/Groups/TG5/TG5_abrahamson_cerme3.pdf. [PDF]
  • Agar, M. (2003). My Kingdom for a Function: Modelling Misadventures of the Innumerate. Journal of Artificial Societies and Social Simulation, 6(3). [HTML]
  • Ausk, B. J., Poliachik, S. L., Gross, T. S., & Srinivasan, S. (2003). Synchronous signaling within the osteocyte cell network underlies the osteogenic potency of rest-inserted loading. In 27th Meeting of the American Society for Biomechanics, Toledo, OH.
  • Bouquet, P., Busetta, P., Adami, G., Bonifacio, M., & Palmieri, F. (2003). K-Trek: A Peer-to-Peer Infrastructure for Distributing and Using Knowledge in Large Environments. Paper presented at the WOA 2003: dagli Oggetti agli Agenti Sistemi Intelligenti e Computazione Pervasiva, Cagliari, Italy. [PDF]
  • Driscoll, M.P. (2003). How people learn (and what technology might have to do with It). ERIC Digest. [PDF]
  • Evans, D., Heuvelink, A., & Nettle, D. (2003). The evolution of optimism: A multi-agent based model of adaptive bias in human judgement. Proceedings of the AISB'03 Symposium on Scientific Methods for the Analysis of Agent-Environment Interaction, University of Wales, pp.20-25. [PDF]
  • Fell, A. (2003). A Study of Modeling Crowd Dynamics. Final year project in Compter Science, Carleton University. [PDF]
  • Gobert, J., Horwitz, P., Tinker, R., Buckley, B., Wilensky, U., Levy, S. T. & Dede, C. (2003). Modeling across the curriculum: Scaling up modeling using technology. Paper presented at the Twenty-Fifth Annual Meeting of the Cognitive Science Society, Boston, MA, July 31 - August 2. [PDF]
  • Goldstone, R. (2003). "Complex Adaptive Systems" psychology course at Indiana University. Spring 2003. Syllabus: [HTML]
  • Hills, L. (2003). A Thin Red Line Between Red and Green: Emerging Patterns in Group Preference an Exploration at Bryn Mawr College. Senior Thesis, Dartmouth College.
  • Horwitz, P., Gobert, J., Wilensky, U., & Dede, C. (2003). MAC: A longitudinal study of modeling technology in science classrooms. Paper presented at the National Educational Computing Conference (NECC), Seattle, WA.
  • InfoWorld. (2003). Experts: U.S. needs better terror models. Investigative report. [HTML]
  • Kline, G. (2003). Campers at UI have fun, G.A.M.E.S. News-Gazette. August 23, 2003. [HTML]
  • Korman, M. J. (2003). LiveLetters: Writing with Emergence. [HTML]
  • Lomi, A., & Cacciaguerra, S. (2003). Decision Chemistry Part II: The Emergence of Routines. [HTML]
  • Lomi, A., & Cacciaguerra, S. (2003, April). Organizational decision chemistry on a lattice. In proc. of the 7th Annual Swarm Users/Researchers Conference.
  • Longo, D., Peirce, S., Skalak, T., Marsden, M., Davidson, L., Dzamba, B., et al. (2003). Computational automata simulation of blastocoel roof thinning in the Xenopu laevis embryo. Systems and Information Engineering Design Symposium, 2003 IEEE, 127-131. [PDF]
  • Manhart, K. (2003). Schildkrštenwelten - Multi-Agenten-Simulationen mit NetLogo und StarLogo. C'T, 2003(25), 232-237.
  • Stieff, M., & Wilensky, U. (2003). Connected Chemistry - incorporating interactive simulations into the chemistry classroom. Journal of Science Education and Technology, 12(3), 285-302. [HTML]
  • Turcsanyi-Szabo, M. (2003). Practical Teacher Training Through Implementation of Capacity Building Internet Projects. Proceedings of Society for Information Technology and Teacher Education Conference, Albuquerque, 2003, 1564-1571. [PDF]
  • Wilensky, U. (2003). Statistical mechanics for secondary school: The GasLab modeling toolkit. International Journal of Computers for Mathematical Learning, 8(1), 1-41 (special issue on agent-based modeling). [PDF]
  • Wilensky, U., & Shapiro, B. (2003). Networked Participatory Simulations: Classroom Collaboration in Exploring the Dynamics of Complex Systems. Proceedings of the International Conference of the Learning Sciences, Oslo, Norway.
  • Wilensky, U., & Stroup, W. (2003). Participatory Simulations guide for Computer-HubNet. Evanston, IL, Center for Connected Learning and Computer Based Modeling, Northwestern University. (Updated 2004, 2005) [PDF]
  • Wilensky, U., & Stroup, W. (2003). Embedded complementarity of object-based and aggregate reasoning in students developing understanding of dynamic systems. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL, April 1-5.

2002

  • Abrahamson, D., & Wilensky, U. (2002). Statistics as situated probability: The design and implementation of S.A.M.P.L.E.R. Unpublished manuscript.
  • Abrahamson, D., Berland, M., Shapiro, R.B., Unterman, J., & Wilensky, U. (2002). Collaborative interpretive argumentation as a phenomenological-mathematical negotiation: A case of statistical analysis of a computer simulation of complex probability. Unpublished manuscript. [PDF]
  • Bull, G., Bell, R., Garofalo, J., & Sigmon, T. (2002). Learner-Based Tools: The Case for Open Source Educational Software. Learning & Leading with Technology, 30(2), 10-17.
  • Fazio, C., Sperandeo-Mineo, R. M., & Tarantino, G. (2002). Mathematical representation of real systems: Two modelling environments involving different learning strategies. [PDF]
  • Gilbert, N. (2002). Varieties of Emergence In M. J. North, C. M. Macal & D. L. Sallach (Eds.), Proceedings of the Agent 2002 Conference on Complex Interaction and Social Emergence (pp. 41-50). IL: Argonne National Laboratory and Northwestern University. [PDF]
  • Gilbert, N. a. B., S. (2002). Platforms and methods for agent-based modeling Paper presented at the National Academy of Sciences of the United States of America.
  • Impullitti, G., & Rebmann, C. M. (2002). An agent-based model of wealth distribution (No. 2002-15). Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
  • Johnson, N. L. (2002). The development of collective structure and its response to environmental change.
  • Lehmann, H., Wang, J., & Bryson, J. (2002). Tolerance and sexual attraction is despotic societies: A replication and analysis of Hemelrijk. Artificial models of natural Intelligence. UK. [PDF]
  • MSCP Project Summary. [HTML]
  • North, M.J., & Burkhart, R.M. (2002). Agent-Based Methods, Toolkits, and Techniques In M. J. North, C. M. & D. L. Sallach (Eds.), Proceedings of the Agent 2002 Conference on Complex Interaction and Social Emergence (pp. 3-10). IL: Argonne National Laboratory and University of Chicago. [PDF]
  • Ratto, M., Shapiro, R.B., Truong, T., & Griswold, W. (2002). The ActiveClass Project: Experiments in encouraging classroom participation. Unpublished manuscript.
  • Stieff, M., & Wilensky, U. (2002). ChemLogo: An emergent modeling environment for teaching and learning chemistry. Paper presented at the Fifth Biannual International Conference of the Learning Sciences, Seattle, WA, October. [HTML]
  • Stroup, W., Kaput, J., Ares, N., & Wilensky, U. (2002). The nature and future of classroom connectivity: The dialectics of mathematics in the social space. Paper presented at the Psychology of Mathematics Education conference, Atlanta, GA, October. [PDF]
  • Wilensky, U., & Stroup, W. (2002). Participatory Simulations: Envisioning the networked classroom as a way to support systems learning for all. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA, April 13. [PDF]
  • Wilensky, U., & Stroup, W. (2002). Participatory Simulations guide for Calculator-HubNet. Evanston, IL, Center for Connected Learning and Computer Based Modeling, Northwestern University. (Updated 2003, 2004, 2005)
  • Yang, C. K., & Wilensky, U. (2002). School Effects Reinterpreted from the Bottom-Up: Merging Statistical Methods and Agent-based Modeling.

2001

  • An, G. (2001). Agent-based computer simulation and sirs: building a bridge between basic science and clinical trials. Shock, 16(4), 266-273.
  • Kahn, K. (2001). ToonTalk and Logo. Paper presented at Eurologo 2001. [PDF]
  • Neuwirth, E. (2001). Turtle Ballet: Simulating Parallel Turtles in a Nonparallel LOGO Version. Paper presented at the Eurologo 2001. [PDF]
  • Tam, P-W. (2001). "E-Commerce (A Special Report): The Classroom - Tools of the future: Thanks to Technology, K-12 will never look the same." Wall Street Journal (Eastern edition). New York, New York. March 12, 2001. p. R28.
  • Wilensky, U. (2001). Emergent Entities and Emergent Processes: Constructing Emergence through Multi-agent programming. Paper presented at the annual meeting of the American Educational Research Association. Seattle, WA. [HTML]
  • Wilensky, U. (2001). Modeling nature's emergent patterns with multi-agent languages. Paper presented at EuroLogo 2001. Linz, Austria. [HTML]
  • Wilensky, U. (2001). Embodied Learning: Students Enacting Complex Dynamic Phenomena with the HubNet Architecture. Paper presented at the annual meeting of the American Educational Research Association. Seattle, WA. [PDF]

2000

  • Centola D., McKenzie E., & Wilensky U. (2000). Survival of the groupiest: Facilitating students' understanding of multi-level evolution through multi-agent modeling - The EACH Project. In Proceedings of the Fourth International Conference on Complex Systems. Nashua, NH: New England Complex Systems Institute, and InterJournal Complex Systems, 377 [PDF]
  • Centola D., Wilensky U., & McKenzie E. (2000). A hands-on modeling approach to evolution: Learning about the evolution of cooperation and altruism through multi-agent modeling - The EACH Project. Proceedings of the Fourth Annual International Conference of the Learning Sciences, Ann Arbor, MI, June 14-17. [PDF]
  • Gilbert, N. T., P. (2000). How to build and use agent-based models in social science. Mind and Society, 1(1), 57-72.
  • Wilensky, U., Hazzard, E., & Longenecker, S. (2000). A Bale of Turtles: A Case Study of a middle school science class studying complexity using StarLogoT. Paper presented at the meeting of the Spencer Foundation, New York, New York, October 11-13, 2000.
  • Wilensky, U., Stroup, W. (2000). Networked gridlock: Students enacting complex dynamic phenomena with the HubNet architecture. Proceedings of the Fourth Annual International Conference of the LearningSciences, Ann Arbor, MI, June 14 - 17. [HTML]
  • Wilensky, U., & Centola, D. (2007, based on 2000 paper). Simulated Evolution: Facilitating Students' Understanding of the Multiple Levels of Fitness through Multi-Agent Modeling. Paper presented at the Evolution Challenges conference, Phoenix, AZ, November 1-4. [PDF]
  • Wilensky, U. (2000) Modeling Emergent Phenomena with StarLogoT. @CONCORD.org, Winter 2000.

1999

  • Berland, M., & Charniak, E. (1999). Finding parts in very large corpora. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL-99), College Park, MD, 57-64.
  • Stroup, W., & Wilensky, U. (1999). Assessing learning as emergent phenomena: Moving constructivist statistics before the individual and beyond the Bell-curve. In A.E. Kelly & R. Lesh (Eds.), Research in Mathematics and Science Education. Englewood, NJ: Lawrence Erlbaum Associates.
  • Wilensky, U. (1999). GasLab: An extensible modeling toolkit for exploring micro- and macro- views of gases. In N. Roberts, W. Feurzeig, & B. Hunter (Eds.), Computer modeling and simulation in science education (pp. 151-178). Berlin: Springer Verlag. [HTML]
  • Wilensky, U., & Resnick, M. (1999). Thinking in Levels: A Dynamic Systems Perspective to Making Sense of the World. Journal of Science Education and Technology, 8(1). [PDF]
  • Wilensky, U., & Stroup, W. (1999). Learning through participatory simulations: Network-based design for systems learning in classrooms. Proceedings of Computer Supported Collaborative Learning (CSCL'99). Stanford, CA, December 12 - 15. [PDF]
  • Wilensky, U., & Stroup, W. (1999). Participatory Simulations: Networked-based Design for Systems Learning in Classrooms. In R. Nikolov, E. Sendova, & I. Nikolova (Eds.). Presented at the PI meeting of the National Science Foundation, EHR division, June 3 - 4. [PDF]
  • Wilensky, U., Hazzard, E & Froemke, R. (1999). GasLab: An Extensible Modeling Toolkit for Exploring Statistical Mechanics. Paper presented at the Seventh European Logo Conference - EUROLOGO '99, Sofia, Bulgaria. [PDF]