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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.
  • Wagh, A., Levy, S., Horn, M., Guo, B., & Wilensky, U. (In Review) Learning to Reason About Natural Selection as a Computational Process by Programming Agent-Based Models in a Code-First Environment.

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.
  • Abdo, J. B., & Hossain, L. (2024). Modeling the Invisible Internet. In Complex Networks & Their Applications XII: Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3 (p. 359). Springer Nature.
  • 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
  • Baccile, S., Cornacchia, G., & Pappalardo, L. (2024). Measuring the Impact of Road Removal on Vehicular CO2 Emissions. In Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference. CEUR-WS.
  • 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).
  • Becher, M. A., Twiston‐Davies, G., Osborne, J. L., & Lander, T. A. (2024). Resource gaps pose the greatest threat for bumblebees during the colony establishment phase. Insect Conservation and Diversity.
  • 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.
  • Bernigaud, N., Bondeau, A., Guiot, J., Bertoncello, F., Ouriachi, M. J., Bouby, L., ... & Isoardi, D. (2024). The impact of climate change on the agriculture and the economy of Southern Gaul: New perspectives of agent-based modelling. Plos one, 19(3), e0298895.
  • 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.
  • Blee, J. A., Gorochowski, T. E., & Hauert, S. (2024). Optimization of periodic treatment strategies for bacterial biofilms using an agent-based in silico approach. Journal of the Royal Society Interface, 21(213), 20240078.
  • Boetttcher, L., Fonseca, L. L., & Laubenbacher, R. (2024). Control of Medical Digital Twins with Artificial Neural Networks. bioRxiv, 2024-03.
  • Bommel, P., & Le Page, C. (2024). Rapport de mission à Parakou, Bénin, du 2 au 12 janvier 2024. Formation à la modélisation multi-agent pour des applications à la gestion intégrée des ressources naturelles (Doctoral dissertation, CIRAD (Montpellier; France)).
  • 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.
  • Brown, T. L., Morgan, E. R., & Caplaet, P. Nemasim: Using an Agent Based Model to Explore the Role of Landscape Fragmentation and Deer Ecology in Transmitting Drug Resistant Nematodes between Sheep Pastures. Available at SSRN 4783624.
  • 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.
  • Cabuya-Padilla, D. E., & Castaneda-Marroquin, C. A. (2024). Marco de referencia para el modelamiento y simulación de la ciberdefensa marítima-MARCIM: estado del arte y metodología. DYNA, 91(231), 169-179.
  • 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.
  • Chang, X., Chen, J., & Ye, L. (2024). Trend prediction of farmers’ spontaneous land transfer behavior: evidence from China. Applied Economics, 1-15.
  • Chao, S., Tao, Y., & Zhou, H. (2024). Technological Progress and its Job Market Impacts: A Basic Simulation Framework for Human-Technology Collaboration. Available at SSRN 4747459.
  • 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.
  • Chen, Y., Zhao, Y., & Wu, Y. (2024). Recent progress in air traffic flow management: A review. Journal of Air Transport Management, 116, 102573.
  • Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., ... & He, X. (2024). Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects. arXiv preprint arXiv:2401.03428.
  • Coates, G., Dugdale, J., & Hanachi, C. (2024). Simulation for crisis and disaster management. SIMULATION, 100(4), 333-334.
  • 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.
  • Cottineau, C., Batty, M., Benenson, I., Delloye, J., Hatna, E., Pumain, D., ... & Ubarevičienė, R. (2024). The role of analytical models and their circulation in urban studies and policy. Urban Studies, 00420980241237410.
  • 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.
  • Dieguez Cameroni, F. J., Varela Casadey, F., Boscana, M., Schinatto, F., & Bussoni, A. (2024). Advancing carbon neutrality in Silvopastoral systems: a case study applying agent-based modeling. Agroforestry Systems, 1-16.
  • 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.
  • Dong, Q., Yan, R., Xiong, R., Xie, W., Pang, M., & Liang, L. (2024). A study on the decision-making mechanism and promotion policy of urban residents’ domestic waste-classification behavior: evidence from Sichuan province. Environment, Development and Sustainability, 1-37.
  • Ehret, M., Johnston, W. J., & Ritter, T. (2024). From buying centers to buying ecosystems: Advancing the B2B research journey. Industrial Marketing Management.
  • Ekanayake‐Weber, M., Mathew, N., Cunha, D., Payen, N., Grimm, V., & Koenig, A. (2024). It's about time: Feeding competition costs of sociality are affected more by temporal characteristics than spatial distribution. Ecology and Evolution, 14(4), e11209.
  • Ekström, H., Droste, N., & Brady, M. (2024). Modelling forests as social-ecological systems: A systematic comparison of agent-based approaches. Environmental Modelling & Software, 105998.
  • Elsheikh, A. (2024). Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences. arXiv preprint arXiv:2403.04417.
  • Emami, S., Dehghanisanij, H., & Hajimirzajan, A. (2024). Agent-based simulation model to evaluate government policies for farmers’ adoption and synergy in improving irrigation systems: A case study of Lake Urmia basin. Agricultural Water Management, 294, 108730.
  • Emami, S., & Dehghanisanij, H. (2024). Fault Tree Analysis of Trade-Offs between Environmental Flows and Agricultural Water Productivity in the Lake Urmia Sub-Basin Using Agent-Based Modeling. Water, 16(6), 844.
  • 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.
  • Fabrin, B. H., Ferrari, D. B., Arraut, E. M., & Neumann, S. (2024). Towards balancing efficiency and customer satisfaction in airplane boarding: An agent-based approach. Operations Research Perspectives, 100301.
  • Fan, W., Shan, C., Wang, S., Liu, T., Wang, F., Meng, X., ... & Liu, Y. (2024). Major Research Fruits. In Unconventional Emergency Management Research (pp. 41-135). Springer, Singapore.
  • 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.
  • Frydenlund, E., Martínez, J., Padilla, J. J., Palacio, K., & Shuttleworth, D. (2024). Modeler in a box: how can large language models aid in the simulation modeling process?. SIMULATION, 00375497241239360.
  • Gajewski, Z., McElmurray, P., Wojdak, J., McGregor, C., Zeller, L., Cooper, H., ... & Hopkins, S. (2024). Nonrandom foraging and resource distributions affect the relationships between host density, contact rates and parasite transmission. Ecology Letters, 27(3), e14385.
  • Garcia, J. M. V. (2024). Modelagem baseada em agentes (ABM) para estudo dos efeitos da fecundidade e da longevidade na diversidade genética de populações biológicas (Doctoral dissertation, Universidade de São Paulo).
  • Gavidia-Calderon, C., Kordoni, A., Bennaceur, A., Levine, M., & Nuseibeh, B. (2024). The IDEA of Us: An Identity-Aware Architecture for Autonomous Systems. ACM Transactions on Software Engineering and Methodology.
  • Gervasi, V., & Guberti, V. (2024). The Effect of Partial and Temporary Vaccination on African Swine Fever Eradication Rates. Transboundary and Emerging Diseases, 2024.
  • 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.
  • Giabbanelli, P. J. (2024). Fuzzy Cognitive Maps: Best Practices and Modern Methods. Springer Nature.
  • Golmaryami, S., Nunes, M. L., & Ferreira, P. (2024). The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach. Smart Energy, 100138.
  • 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.
  • Greybosh, C. (2024). Bringing Computational Modeling into the Classroom with Custom Block-Based Programming Languages in StarLogo Nova (Doctoral dissertation, Massachusetts Institute of Technology).
  • Groeneveld, J., Martinovič, T., Rossi, T., Salamon, O., & Grimm, V. (2024). Prototype Biodiversity Digital Twin: Honey Bees in Agricultural Landscapes. ARPHA Preprints, 5, e124639.
  • Hahn, U., Merdes, C., & von Sydow, M. (2024). Knowledge through social networks: Accuracy, error, and polarisation. Plos one, 19(1), e0294815.
  • 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.
  • Hatlauf, J., Kunz, F., Griesberger, P., Sachser, F., & Hackländer, K. (2024). A stage-based life cycle implementation for individual-based population viability analyses of grey wolves (Canis lupus) in Europe. Ecological Modelling, 491, 110700.
  • Hazari, T. A., Khalil Ullah, M. I., Pareno, S. I., & Assuad, C. S. A. (2024). A Study on Circular Fashion: Profitability and Environmental Impact Analysis. In EcoDesign for Sustainable Products, Services and Social Systems II (pp. 59-73). Singapore: Springer Nature Singapore.
  • Higuera, J. R. B., Higuera, J. B., Montalvo, J. A. S., & Crespo, R. G. (2024). Unsupervised Approaches in Anomaly Detection. In Machine Learning for Cyber Physical System: Advances and Challenges (pp. 57-83). Cham: Springer Nature Switzerland.
  • 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.
  • Iapăscurtă, V. (2024). Agent-based modeling: revolutionizing tissue engineering. Cells and tissues transplantation. Actualities and perspectives. The 2-nd edition. Chisinau, March 29-30th 2024.
  • Imanian Ardabily, M., Noghani Dokht Bahmani, M., & Asgharpour Masouleh, A. R. (2024). science production. Ferdowsi University of Mashhad Journal of Social Sciences.
  • Ismail, S., Mostafa, S. A., Baharum, Z., Erianda, A., Jaber, M. M., Jubair, M. A., & Adiya, M. H. (2024). Software Agent Simulation Design on the Efficiency of Food Delivery. JOIV: International Journal on Informatics Visualization, 8(1), 190-197.
  • Ivanjek, L., Perl-Nussbaum, D., Solvang, L., Yerushalmi, E., & Pospiech, G. (2024). Enhancing Mathematization in Physics Education by Digital Tools. In Physics Education Today: Innovative Methodologies, Tools and Evaluation (pp. 35-53). Cham: Springer Nature Switzerland.
  • Josserand, M., Allassonnière‐Tang, M., Pellegrino, F., Dediu, D., & de Boer, B. (2024). How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents. Cognitive Science, 48(4), e13439.
  • 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.
  • Kang, B., Kumar, H., Lee, M., Chakraborty, B., & Mukhopadhyay, S. (2024). Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction. arXiv preprint arXiv:2404.06460.
  • Kaur, M., & Verma, V. K. (2024). Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things. Journal of Network and Computer Applications, 103872.
  • Kheirinejad, S., Bozorg-Haddad, O., Savic, D., Singh, V. P., & Loáiciga, H. A. (2024). Developing a National-Scale Hybrid System Dynamics, Agent-Based, Model to Evaluate the Effects of Dietary Changes on the Water, Food, and Energy Nexus. Water Resources Management, 1-26.
  • 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.
  • Kirac, E., Shaltayev, D., & Wood, N. (2024). Evaluating the Impact of Citizen Collaboration with Government Agencies in Disaster Response Operations: An Agent-Based Simulation Study. International Journal of Disaster Risk Reduction, 104469.
  • Kooijman, S. A. L. M. (2024). Ways to reduce or avoid juvenile-driven cycles in individual-based population models. Ecological Modelling, 490, 110649.
  • Kopels, M. C., & Ullah, I. I. (2024). Modeling post-Pleistocene megafauna extinctions as complex social-ecological systems. Quaternary Research, 1-14.
  • 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.
  • Lemmen, C., Hokamp, S., Örey, S., & Scheffran, J. (2024). Viable North Sea (ViNoS): A NetLogo Agent-based Model of German Small-scale Fisheries. Journal of Open Source Software, 9(95), 5731.
  • Leonard-Duke, J., Agro, S. M., Csordas, D. J., Bruce, A. C., Eggertsen, T. G., Tavakol, T. N., ... & Peirce, S. M. (2024). Multi-Scale Computational Model of Microvascular Remodeling in Idiopathic Pulmonary Fibrosis. bioRxiv, 2024-03.
  • Liang, L., Phan, H., & Giabbanelli, P. J. (2024). Experimental evaluation of a machine learning approach to improve the reproducibility of network simulations. SIMULATION, 00375497241229753.
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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.
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  • נורית ברגר-גיל .(2023) למידת מושגים בכימיה באמצעות בניית מודלים חישוביים ומעוגני גוף בגישת מערכות מורכבות (Doctoral dissertation, University of Haifa, Israel).
  • Безбородова, О. Е. (2023). ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ ВЗАИМОДЕЙСТВИЯ ЧЕЛОВЕКА И ОБЪЕКТА ТЕХНОСФЕРЫ В ИНФОРМАЦИОННО-ИЗМЕРИТЕЛЬНЫХ И УПРАВЛЯЮЩИХ СИСТЕМАХ ОБЕСПЕЧЕНИЯ ЭКОЛОГИЧЕСКОГО БЛАГОПОЛУЧИЯ ЧЕЛОВЕКА. Модели, системы, сети в экономике, технике, природе и обществе, (1 (45)), 164-177.
  • Макареня, Т. А., Маннаа, А. С., Калиниченко, А. И., & Петренко, С. В. (2023). Когнитивное моделирование социально-экономических систем: ретроспективный анализ инструментов и информационных систем. Вестник ВГУ. Серия: Системный анализ и информационные технологии, (3), 84-94.
  • Алексеева, Е. А. (2023). Проекты компьютерной эпистемологии. Философия науки и техники Philosophy of Science and Technology, 28(2), 88-101.
  • Μπενίση, Α., Γκιόλμας, Α., Στούμπα, Α., Χαλκίδης, Ά., Μπόικος, Η., Ψωμά, Β., ... & Παπαναγιώτου, Α. Τ. (2023). Εξελιγμένη μορφή του μοντέλου της NetLοgo για τη φωτιά στο δάσος: Μία διδακτική προσέγγιση σε μαθητές Δημοτικού. 13o Πανελλήνιο Συνέδριο της Διδακτικής των Φυσικών Επιστημών και Νέων Τεχνολογιών στην Εκπαίδευση, 13.
  • Каталевский, Д. (2023). Новые управленческие подходы для предотвращения краха сложных социальноэкономических систем. Форсайт, 17(3), 56-67.
  • Белолуцкая, А. К., Вачкова, С. Н., & Патаракин, Е. Д. Связь цифрового компонента обучения и развития детей дошкольного и школьного возраста: обзор исследований и международных образовательных практик, 18(2), 37-55.
  • Гнатчук, Є. Г., Засорнова, І. О., & Рей, К. С. (2023). СИСТЕМА ПІдТРИМКИ ПРИйНяТТя РІшЕНь ПРО МОЖлИВІСТь ВАКцИНАцІЇ ВІд COVID-19. ВЧЕНІ ЗАПИСКИ, 1202376.
  • Тимофеев, Г. А. (2023). Поиск подходящей архитектуры для разработки цифрового двойника гибридных энергетических систем в изолированных от сетевых энергосистем средах с использованием ТРИЗ-эволюционного подхода. Вестник НГУ. Серия: Информационные технологии, 20(4), 76-99.
  • Антонова, Е. М (2023). Медицинская информатика: влияние времени, проблемы и возможные пути решения. ЦИТИСЭ, 3, 18-27.
  • АНДРИАНОВА, Л., ПАВЛОВА, З., ХАКИМЬЯНОВ, М., & ХАЗИЕВА, Р. ИНФОРМАТИВНОЕ ПРЕДСТАВЛЕНИЕ ДИСЦИПЛИНЫ «МУЛЬТИАГЕНТНЫЕ СИСТЕМЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА» В ВОПРОСАХ И ОТВЕТАХ. Международный центр научного партнерства" Новая Наука" КОНФЕРЕНЦИЯ: ПРЕПОДАВАТЕЛЬ ГОДА 2023 Петрозаводск, 13 декабря 2023 года Организаторы: Международный центр научного партнерства" Новая Наука".
  • Дудко, В. В., & Патаракин, Е. Д. (2023). Исследование научных школ университета средствами библиометрического картирования. Территория новых возможностей. Вестник Владивостокского государственного университета экономики и сервиса, 15(1 (65)), 150-167.
  • Шепель, А. С. ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ. ТЕОРИЯ И ПРАКТИКА (ИММОД-2023). Издательство АН РТ КОНФЕРЕНЦИЯ: ОДИННАДЦАТАЯ ВСЕРОССИЙСКАЯ НАУЧНО-ПРАКТИЧЕСКАЯ КОНФЕРЕНЦИЯ ПО ИМИТАЦИОННОМУ МОДЕЛИРОВАНИЮ И ЕГО ПРИМЕНЕНИЮ В НАУКЕ И ПРОМЫШЛЕННОСТИ «ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ. ТЕОРИЯ И ПРАКТИКА» ИММОД-2023 Казань, 18–20 октября 2023 года Организаторы: Казанский государственный энергетический университет.
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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.
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2021

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  • Ambrosius, F. H., Kramer, M. R., Spiegel, A., Bokkers, E. A., Bock, B. B., & Hofstede, G. J. (2022). Diffusion of organic farming among Dutch pig farmers: An agent-based model. Agricultural Systems, 197, 103336.
  • Ambrosius, F. H., Kramer, M. R., Spiegel, A., Bokkers, E. A., Bock, B. B., & Hofstede, G. J. UNDERSTANDING DIFFUSION OF ORGANIC FARMING AMONG DUTCH PIG FARMERS: AN AGENT-BASED MODEL. Transition through markets, 93.
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  • Anokhin, A., Burov, S., Parygin, D., Rent, V., Sadovnikova, N., & Finogeev, A. (2021). Development of Scenarios for Modeling the Behavior of People in an Urban Environment. In Society 5.0: Cyberspace for Advanced Human-Centered Society (pp. 103-114). Springer, Cham.
  • Antczak, T., Skorupa, B., Szurlej, M., Weron, R., & Zabawa, J. (2021). Simulation modeling of epidemic risk in supermarkets: Investigating the impact of social distancing and checkout zone design (No. WORMS/21/05). Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
  • Anvari, S., Nambiar, S., Pang, J., & Maftoon, N. (2021). Computational Models and Simulations of Cancer Metastasis. Archives of Computational Methods in Engineering, 1-23.
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2020

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  • 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.
  • Coronel, A. R., & Alatriste, F. R. (2019, March). Turning caregivers into informed agents as a strategy to disseminate scientific information about cancer. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 938-944). Springer, Cham.
  • Cotfas, L. A., Delcea, C., Milne, R. J., Salari, M., Crăciun, L., & Molănescu, A. G. (2019). Testing new methods for boarding a partially occupied airplane using apron buses. Symmetry, 11(8), 1044.
  • Crabtree, S., Harris, K., Davies, B., & Romanowska, I. (2019, July 6). Outreach in Archaeology with Agent-Based Modeling: Part 3 of 3. https://doi.org/10.1017/aap.2019.4
  • Cruz, A., Carneiro, E., Fontes, X., Kokkinogenis, Z., & Rossetti, R. J. (2019, October). Hermes: a tool for mesoscopic simulation of advanced traveller information systems. In 2019 IEEE International Smart Cities Conference (ISC2) (pp. 638-643). IEEE.
  • Cruz, E. G. A. (2019). Modeling Social Learning: An Agent-Based Approach (Doctoral dissertation, Old Dominion University).
  • Da Costa, L., & Rajotte, J. F. (2019, May). Crowd Prediction Under Uncertainty. In Canadian Conference on Artificial Intelligence (pp. 308-319). Springer, Cham.
  • Dabholkar, S. & Wilensky, U. (2019). Designing ESM-mediated collaborative activity systems for science learning. Poster to be presented at International Conference of Computer Supported Collaborative Learning 2019, Lyon, France.
  • Dabholkar, S., Wilensky, U., & Horn, M. (2019) Supporting a teacher’s integration of Computational Thinking (CT) in a biology class by co-designing an ESM- (Emergent Systems Microworlds) based curricular unit, Poster presented at Inaugural symposium on Computer Science and Learning Science, Evanston, USA
  • Dabholkar, S. (2019) Designing Emergent Systems Microworlds to learn computational thinking in the context of synthetic biology. Poster presented at Learn.Design.Compute with Bio 2019, Philadelphia, USA
  • Dabholkar, S., Swanson, H., & Wilensky, U. (2019). Epistemic considerations for modeling: Understanding the usefulness and limitations of models with Emergent Systems Microworlds. In a Related Paper Set, Using Technology to Promote Students’ Modeling Practice and Complex Systems Thinking. The Annual Meeting of the National Association of Research in Science Teaching (NARST), Baltimore, MD, USA.
  • Dalle Nogare, D., & Chitnis, A. B. (2019, December). NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium. In Seminars in Cell & Developmental Biology. Academic Press.
  • Davies, B., Romanowska, I., Harris, K., & Crabtree, S. A. (2019). Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3. Advances in Archaeological Practice, 7(2), 185-193.
  • Davis, P., O'Mahony, A. & Pfautz, J. (2019).Social-Behavioral Modeling for Complex Systems. John Wiley & Sons.
  • Davydenko, I. Y., & Fransen, R. W. (2019). Conceptual agent based model simulation for the Port Nautical Services. IFAC-PapersOnLine, 52(3), 19-24.
  • DeMarco, K., Squires, E., Day, M., & Pippin, C. (2019). Simulating collaborative robots in a massive multi-agent game environment (scrimmage). In Distributed Autonomous Robotic Systems (pp. 283-297). Springer, Cham.
  • Delcea, C., & Cotfas, L. A. (2019). Increasing awareness in classroom evacuation situations using agent-based modeling. Physica A: Statistical Mechanics and its Applications, 523, 1400-1418.
  • Delcea, C., Milne, R. J., Cotfas, L. A., Crăciun, L., & Molănescu, A. G. (2019). Methods for Accelerating the Airplane Boarding Process in the Presence of Apron Buses. IEEE Access, 7, 134372-134387.
  • DeLuca, C. (2019). Keyword Response: Out of Step. In Key Concepts in Curriculum Studies (pp. 47-49). Routledge.
  • Dhou, K. (2019). An innovative design of a hybrid chain coding algorithm for bi-level image compression using an agent-based modeling approach. Applied Soft Computing, 79, 94-110.
  • Dhou, K., & Cruzen, C. (2019). An innovative chain coding technique for compression based on the concept of biological reproduction: an agent-based modeling approach. IEEE Internet of Things Journal, 6(6), 9308-9315.
  • Dickes, A.C., Kamarainen, A., Metcalf, S.J., Gün‐Yildiz, S., Brennan, K., Grotzer, T., & Dede, C. (2019). Scaffolding ecosystems science practice by blending immersive environments and computational modeling. British Journal of Educational Technology, 50(5), 2181-2202. https://doi.org/10.1111/bjet.12806
  • Ding, F., & Pan, W. (2019). Simulation Research on Large Passenger Flow Guidance of Urban Rail Transit Based on Multi-Agent. Academic Journal of Computing & Information Science, 2(1).
  • Ding, Feng, and Wenjie Pan. "Simulation Research on Large Passenger Flow Guidance of Urban Rail Transit Based on Multi-Agent." Academic Journal of Computing & Information Science 2.1 (2019).
  • Dobaria, R., & Chilka, A. (2019). IoT Smart Waste Monitoring and Collection Framework. International Journal of Distributed Computing and Technology, 5(1), 27-32.
  • Dragoni, A. F. (2019). An Agent-Swarm Simulator for Dynamic Vehicle Routing Problem Empirical Analysis. In Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection: 17th International Conference, PAAMS 2019, Ávila, Spain, June 26-28, 2019, Proceedings (Vol. 11523, p. 246). Springer.
  • Dragotă, V., & Delcea, C. (2019). How long does it last to systematically make bad decisions? An agent-based application for dividend policy. Journal of Risk and Financial Management, 12(4), 167.
  • D’Souza, M., & Kashi, R. N. (2019, January). Avionics Self-adaptive Software: Towards Formal Verification and Validation. In International Conference on Distributed Computing and Internet Technology (pp. 3-23). Springer, Cham.
  • Egbert, M. (2019, July). Real-Time Visualization and Interaction with Computational Artefacts. In MethAL workshop with The Conference on Artificial Life 2019.
  • Elfakir, A., Tkiouat, M., & Allam, K. (2019). Entrepreneurial financing under uncertainty: Performance comparison between ROMCA and conventional microloans using agent based simulation.
  • Elfakir, A., & Tkiouat, M. (2019). Profit and loss Sharing Negotiations involving a VC and an entrepreneur: A Game Theoretic Approach with Agent Based Simulation.
  • El-dosuky, M. (2019). Taming the Sharing Economy Flood: Modelling the Imposing of Sharing Economy Regulation. Available at SSRN 3372792.
  • Esmaeili Bidhendi, M. (2019). The Study of CO Symptoms' Impacts on Individuals, Using GIS and Agent-based Modeling (ABM). Pollution, 5(3), 463-471.
  • Falcionelli, N., et al. (2019)."An Agent-Swarm Simulator for Dynamic Vehicle Routing Problem Empirical Analysis." Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection Lecture Notes in Computer Science, 26 June 2019, pp. 246–250., doi:10.1007/978-3-030-24209-1_23.
  • Falletta, J., & Mukheibir, P. (2019). Demand Forecasting: Review of processes and methodologies [prepared for the Water Corporation].a
  • Farris, A. V. (2019, June). The Sensing Bridge Between Perceptuomotor Experience and Scientific Investigation. In Proceedings of the 18th ACM International Conference on Interaction Design and Children (pp. 648-651).
  • Febriandini, I. F., Sutopo, W., & Hisjam, M. (2019, May). Analysis daily newspaper distribution in Solo by Agent Based Simulation. In IOP Conference Series: Materials Science and Engineering (Vol. 528, No. 1, p. 012033). IOP Publishing.
  • França da Silva, T., Alves Leite, J. L., Campos Ferro Junior, R. J., Ferreira da Costa, L., Pinheiro de Souza, R., Bernardino Andrade, J. P., & Lima de Campos, G. A. (2019, May). Smart targets to avoid observation in cto problem. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1958-1960).
  • Frahm, E., Kandel, A.W. & Gasparyan, B. (2019). Upper Palaeolithic Settlement and Mobility in the Armenian Highlands: Agent-Based Modeling, Obsidian Sourcing, and Lithic Analysis at Aghitu-3 Cave. Journal of Paleolithic Archaeology 2, 418–465. https://doi.org/10.1007/s41982-019-00025-5
  • Freelan, D., Spagnuolo, C., Scarano, V., Cordasco, G., & Cioffi-Revilla, C. (2019, June). The MASON Simulation Toolkit: Past, Present, and Future. In Multi-Agent-Based Simulation XIX: 19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers (Vol. 11463, p. 75). Springer.
  • Fuchs, M., & Neumayr, R. (2019, September). Agent-Based Semiology for Simulation and Prediction of Contemporary Spatial Occupation Patterns. In Design Modelling Symposium Berlin (pp. 648-661). Springer, Cham.
  • Fulop, S. A., & Scott, H. (2019). Vowel System Sandbox: Complex System Modelling of Language Change. Journal of Open Research Software, 7(1).
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2018

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  • Opiyo, N. (2018). Modelling different PV-based communal grids architectures for rural developing communities. In Proceedings of the 35th EU PVSEC 2018 (pp. 1859-1864).
  • Opiyo, N. (2018, September). How Subsidies Impact on Temporal Diffusion of PV-Based Minigrids. In 35th European Photovoltaic Solar Energy Conference.
  • Opiyo, N. (2018, September). Modelling Different PV-Based Minigrids Architectures. In 35th European Photovoltaic Solar Energy Conference.
  • Ornelas, N. O. An Ecosystem: Computational Thinking, Project-Based Learning [PDF]Logo
  • Paris, T., Ciarletta, L., & Chevrier, V. (2018, October). Co-simulation à base d'outils multi-agents: un cas d'étude avec NetLogo.
  • Pele, M., Deneubourg, J. L., & Sueur, C. (2018). Decision-making processes underlying pedestrian behaviours at signalised crossings: Part 2. Do pedestrians show cultural herding behaviour?. arXiv preprint arXiv:1805.11834.[PDF]
  • Peng, Y., Li, Q. X., & Bao, H. J. (2018). Conflict Analysis of Concentrated Rural Settlement Development During Post-disaster Reconstruction in China: A Multi-agent Simulation. In Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate (pp. 491-502). Springer, Singapore.[PDF]
  • Petrosino, A., Sherard, M., Harron, J., & Kohl, M. (2018, March). Using Collaborative Agent-based Modeling to Explore Complex Phenomena in Pre-and In-service Teacher Education. In Society for Information Technology & Teacher Education International Conference (pp. 1669-1671). Association for the Advancement of Computing in Education (AACE).
  • Polater, A. (2018). Managing airports in non-aviation related disasters: A systematic literature review. International Journal of Disaster Risk Reduction.[PDF]
  • PONZIANI, F., TINABURRI, A., & RICCI, V. (2018). A Multi Agent Approach To Analyse Shift In People Behaviour Under Critical Conditions. International Journal of Safety and Security Engineering, 8(1), 1-9.[PDF]
  • Ponziani, F. A., Tinaburri, A., & Ricci, V. (2018). A multi agent approach to analyse shift in people behavior under critical conditions. International Journal of Safety and Security Engineering, 8(1), 1-9.
  • Pour, F. S. A., Tatar, U., & Gheorghe, A. (2018, April). Agent-based model of sand supply governance employing blockchain technology. In Proceedings of the Annual Simulation Symposium (p. 14). Society for Computer Simulation International.[PDF]
  • Proctor, C., Blikstein, P.(2018). Unfold.studio: Supporting critical literacies of text and code Stanford Graduate School of Education, (p. 1-35).[PDF]
  • Proietti, C. & Franco, A. Social norms and the dominance of low-doers. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3524
  • Quan-en, M. A., & Juan, Z. H. A. N. G. (2018). Research on Propagation Principle of Wechat Official Account in Complex Network by Modeling Based on SIR and Simulation. Information Science.
  • Raglin, A., Metu, S., & Howard, C. (2018, April). Understanding theoretical human information interaction, the development of a standard model using an agent based modeling framework. In Next-Generation Analyst VI (Vol. 10653, p. 1065302). International Society for Optics and Photonics.
  • Raimbault, J. An Urban Morphogenesis Model Capturing Interactions between Networks and Territories.[PDF]
  • Rajib, Md. Design Considerations for Intermittently Connected Energy Harvesting Wireless Sensor Networks. ProQuest.[PDF]
  • Ramírez-Ávila, G. M., Kurths, J., & Deneubourg, J. L. (2018). Fireflies: a paradigm in synchronization. In Chaotic, Fractional, and Complex Dynamics: New Insights and Perspectives (pp. 35-64). Springer, Cham.[PDF]
  • Rashid, K. I., Nan, D., Tahir, M., & Ahmed, A. An Adaptive Cruise Control Model based on PDLCA for Efficient Lane Selection and Collision Avoidance. International Journal of Advanced Computer Science and Applications (9).[PDF]
  • Raya, K., Gaxiola, C. G., & Castanon, M. (2018). Agent-based model for self management of network flows using negotiation. IEEE Latin America Transactions, 16(1), 210-215.
  • Reinhardt, O., Hilton, J., Warnke, T., Bijak, J., & Uhrmacher, A. M. (2018). Streamlining simulation experiments with agent-based models in demography. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3784
  • Rosales, C., Whipple, J. M., & Blackhurst, J. (2018). The Impact of Out-of-Stocks and Supain Design on Manufacturers: Insights from an Agent-Based Model. Transportation Journal, 57(2), 137-162.[PDF]
  • Rossetti, G., Milli, L., Rinzivillo, S., Sîrbu, A., Pedreschi, D., & Giannotti, F. (2018). NDlib: a python library to model and analyze diffusion processes over complex networks. International Journal of Data Science and Analytics, 5(1), 61-79.[PDF]
  • Salman, M. A., & Al Essa, H. A. (2018). A Distributed Approach for Disk Defragmentation. Journal of University of Babylon for Pure and Applied Sciences, 26(3), 1-5.
  • Santos, F., Nunes, I., & Bazzan, A. L. (2018). Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain. Simulation Modelling Practice and Theory, 83, 162-187.[PDF]
  • Saputra, G. W., Irawan, B., & Kusuma, P. D. (2018). Pemodelan Dan Simulasi Penyebaran Penyakit Tuberkulosis Berbasis Sistem Agen. eProceedings of Engineering, 5(3).
  • Schools, S. (2018). A Genetic Algorithm for Sorting Lists Implemented in the Programming Language Net Logo (Doctoral dissertation, Morgan State University)
  • Secchi, D. & Cowley, S. J. (2018). Modeling organizational cognition: The case of impact factor. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3628
  • SHEN, Y. W., & LU, Z. J. (2018). Research on the Evolution of Entrepreneurial Ecosystem Based on Innovation Network. In Economic Forum.
  • Simpkins, C. E., Dennis, T. E., Etherington, T. R., & Perry, G. L. (2018). Assessing the performance of common landscape connectivity metrics using a virtual ecologist approach. Ecological Modelling, 367, 13-23.[PDF]
  • Snitker, G. (2018). Identifying natural and anthropogenic drivers of prehistoric fire regimes through simulated charcoal records. Journal of Archaeological Science, 95, 1-15.[PDF]
  • Soheilypour, M., & Mofrad, M. R. (2018). Agent‐based modeling in molecular systems biology. BioEssays, 40(7), 1800020.
  • Stamatovic, B. (2018, February). Implementation of CA algorithm for labeling of 26-connected components in 3D binary lattices. In 2018 23rd International Scientific-Professional Conference on Information Technology (IT) (pp. 1-3). IEEE.
  • Sturley, C., Newing, A., & Heppenstall, A. (2018). Evaluating the potential of agent-based modelling to capture consumer grocery retail store choice behaviours. The Ionternatinal Review of Retail, Distribution and Consumer Research, 28(1), 27-46.[HTML]
  • Sukarno, S. A., Atitallah, R. B., & Djemai, M. (2018, October). Approximation Algorithm for 3-Dimensional Vehicle Routing Problem for Fleet of Multi-Agents. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT) (pp. 1-6). IEEE
  • Sullivan, A., An, L., & York, A. (2018). Which perspective of institutional change best fits empirical data? An agent-based model comparison of rational choice and cultural diffusion in invasive plant management. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3611
  • Suyamto, D., Prasetyo, L., & Setiawan, Y. (2018, August). A voxel-based model of LiDAR point cloud for estimating forest canopy closure. In Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018) (Vol. 10773, p. 107730Q). International Society for Optics and Photonics.
  • Syuhada, K. E. Pengaruh Formasi Penyimpanan Metode Volume Based Terhadap Unjuk Kerja Strategi Routing Order Picking Menggunakan Strategi Midpoint dan Largesgap. Jurnal TIN Universitas Tanjungpura, 2(2).
  • Taherian, M., Mousavi, S. M., & Chamani, H. (2018). An agent-based simulation with NetLogo platform to evaluate forward osmosis process (PRO Mode). Chinese journal of chemical engineering, 26(12), 2487-2494.
  • Thakur, S., Bhautik, P., Sangore, V., Singh, K. (2018).A Review on Treatment of Sewage Water & Biogas Purification by Algae. Internatinal Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 1, Page No pp.639-642, January 2018, [PDF]
  • Thiriot, S. (2018). Word-of-mouth dynamics with information seeking: Information is not (only) epidemics. Physica A: Statistical Mechanics and its Applications, 492, 418-430.[PDF]
  • Trivedi, A., & Pandey, M. (2018). Agent-based modelling and simulation of religious crowd gatherings in India. In Advanced Computational and Communication Paradigms (pp. 465-472). Springer, Singapore.
  • Tufféry, C., Fernandes, P., Delvigne, V., & Morala, A. (2018). Combinaison d’un SMA et d’un SIG pour aider à la prospection pétroarchéologique. Exploration d’une approche multi-agents dans la modélisation des parcours naturels du silex. Archéologies numériques, 2(1).
  • Tyson, M. (2018). Managing Distributed Information: Implications for Energy Infrastructure Co-production (Doctoral dissertation, Arizona State University).[PDF]
  • Vanhée, L. & Dignum, F. (2018). Explaining the emerging influence of culture, from individual influences to collective phenomena. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3881
  • Vidaković, M., Ivanović, M., Stantić, D., & Vidaković, J. (2018, June). How Research Achievements Can Influence Delivering of a Course-Siebog Agent Middleware. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 110-120). Springer, Cham.[HTML]
  • Voinov, A., Jenni, K., Gray, S., Kolagani, N., Glynn, P. D., Bommel, P., ... & Smajgl, A. (2018). Tools and methods in participatory modeling: Selecting the right tool for the job. Environmental Modelling & Software, 109, 232-255.
  • Wagh, A., & Wilensky, U. (2018). EvoBuild: A quickstart toolkit for programming agent-based models of evolutionary processes. Journal of Science Education and Technology, 27(2), 131-146.
  • Waight, N., & Abd-El-Khalick, F. (2018). Technology, Culture, and Values: Implications for Enactment of Technological Tools in Precollege Science Classrooms. In Cognition, Metacognition, and Culture in STEM Education (pp. 139-165). Springer, Cham.[PDF]
  • Walbert, H. J., Caton, J. L., & Norgaard, J. R. (2018). Countries as agents in a global-scale computational model. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3717
  • Wang, X. M., He, C. C., & Li, X. K. (2018). Interaction Optimization Among Multi-agent of Green Dwelling Market. Journal of Civil Engineering and Management, 35(2), 1-7.
  • Wang, Y., Wen, S., Farnon Ellwood, M. D., Miller, A. D., & Chu, C. (2018). Temporal effects of disturbance on community composition in simulated stage‐structured plant communities. Ecology and evolution, 8(1), 120-127.[PDF]
  • Wang, Z., Zhang, H., Hu, M., Qiu, Q., & Liu, H. (2018). Analysis of safety characteristics of flight situation in complex low-altitude airspace. Advances in Mechanical Engineering, 10(5), 1687814018774656.[PDF]
  • Wilkerson, M. H., Shareff, R., Laina, V., & Gravel, B. (2018). Epistemic gameplay and discovery in computational model-based inquiry activities. Instructional Science, 1-26.[PDF]
  • Weintrop, D., & Wilensky, U. (2018). How block-based, text-based, and hybrid block/text modalities shape novice programming practices. International Journal of Child-Computer Interaction.[PDF]
  • Weisbuch, G. (2018). Lattice dynamics of inequality. Journal of Artificial Societies and Social Simulation, 21(1). doi.org/10.18564/jasss.3635
  • West, T. A., Grogan, K. A., Swisher, M. E., Caviglia-Harris, J. L., Sills, E., Harris, D., ... & Putz, F. E. (2018). A hybrid optimization-agent-based model of REDD+ payments to households on an old deforestation frontier in the Brazilian Amazon. Environmental Modelling & Software, 100, 159-174.[PDF]
  • Yarbrough, B., & Wagner, N. (2018, April). Assessing security risk for wireless sensor networks under cyber attack. In Proceedings of the Annual Simulation Symposium (p. 1). Society for Computer Simulation International.[PDF]
  • Xu, X., Sahnoun, M., Abdelaziz, F., Baudry, D., Louis, A.(2018). Multi-objective Flexible Job Shop Scheduling Problem: Simulation Approach [PDF]
  • YACHOU, N., & ABOULAICH, R. (2018). Agent Based Modeling and Simulation for Home Financing. Application in Netlogo Platform. Journal of Applied Economic Sciences, 13(3).
  • Yahyaoui, F., & Tkiouat, M. (2018). Agent-based co-modeling of information society and wealth distribution. International Journal of Advanced Computer Science and Applications, 9(11), 201-206.
  • Yan, J., Liu, R., & Zhang, G. (2018). Task structure, individual bounded rationality and crowdsourcing performance: An agent-based simulation approach. Journal of Artificial Societies and Social Simulation, 21(4). doi.org/10.18564/jasss.3854
  • Yaşar, O. (2018). A new perspective on computational thinking. Communications of the ACM, 61(7), 33-39.
  • YIN, G., LI, G., ZHU, T., & CHEN, J. (2018). Study on Multi-Agent System of Energy Managements for 4WD Electric Vehicles. China Mechanical Engineering, 29(15), 1765.
  • Yousefi, M., Yousefi, M., Ferreira, R. P. M., Kim, J. H., & Fogliatto, F. S. (2018). Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artificial intelligence in medicine.[PDF]
  • Yoo, E. (2018). Dynamics of Information Distribution on Social Media Platforms during Disasters (Doctoral dissertation, Arizona State University).[PDF]
  • Yoon, S. A., Goh, S. E., & Park, M. (2018). Teaching and Learning About Complex Systems in K–12 Science Education: A Review of Empirical Studies 1995–2015. Review of Educational Research, 88(2), 285-325.[PDF]
  • Yunming, W., Si, C., Chengsheng, P., & Bo, C. (2018). Measure of invulnerability for command and control network based on mission link. Information Sciences, 426, 148-159.[PDF]
  • Zeppini, P. & Frenken, K. (2018). Networks, percolation, and consumer demand. Journal of Artificial Societies and Social Simulation, 21(3). doi.org/10.18564/jasss.3658
  • Zhang, X., He, Junhui.(2018). Nature-Inspired Computational Model of Population Desegregation Under Group Leaders Influence. Proceedings of the Fifth International Forum on Decision Sciences.[PDF]
  • Zhao, C., Li, S., Wang, W., Li, X., & Du, Y. (2018). Advanced parking space management strategy design: an agent-based simulation optimization approach. Transportation Research Record, 2672(8), 901-910.
  • ZHENG, Y., ZHANG, G., MA, R., & SHU, H. (2018). Research on Influence of Mobile Social Network on College Students' Campus Life: from Perspective of Multi-Agent Evolution Simulation. China Educational Technology & Equipment, 04.
  • ZHOU, X. N., WANG, J. S., & Yin, Z. H. U. (2018). Simulation Research of Vehicle Lane-changing Behavior Evolutionary Game Model Based on NetLogo. DEStech Transactions on Engineering and Technology Research, (ecar).
  • Zhu, M., Panorkou, N., Lal, P., Etikyala, S., Germia, E., Iranah, P., ... & Basu, D. (2018, March). Integrating interactive computer simulations into K-12 earth and environmental science. In 2018 IEEE Integrated STEM Education Conference (ISEC) (pp. 220-223). IEEE.
  • Zia, K., Saini, D. K., Muhammad, A., & Ferscha, A. (2018). Customer Participation in the Internet of Things: A Bayesian Game Model. IEEE Transactions on Computational Social Systems.[PDF]
  • Zinelli Jr, M. (2018, October). Multi-agent Simulation of a Real Evacuation Scenario: Kiss Nightclub and the Panic Factor. In Multi-Agent Systems and Agreement Technologies: 15th European Conference, EUMAS 2017, and 5th International Conference, AT 2017, Evry, France, December 14-15, 2017, Revised Selected Papers (Vol. 10767, p. 268). Springer.
  • Zoto, E., Kowalski, S., Lopez-Rojas, E. A., & Kianpour, M. Using a socio-technical systems approach to design and support systems thinking in cyber security education.[PDF]

2017

  • Abbott, R., & Hadžikadić, M. (2017).Complex Adaptive Systems, Systems Thinking, and Agent-Based Modeling. In Advanced Technologies, Systems, and Applications (pp. 1-8). Springer International Publishing. [PDF]
  • ACOSTA, C., BORGESIUS, F., & VAN HATTUM, J. E. S. S. I. E. Facilitating Collective Action for an Integrated Community Energy System.[PDF]
  • Alzaeemi, S. A. S., Sathasivam, S., & Adebayo, S. A. (2017). Analysis of Performance of Various Activation Functions for doing the logic programming in Hopfield Network. International Journal of Computational Bioinformatics and In Silico Modeling, 6(2), 911-921. [ PDF]
  • Alzahrani, E., Richmond, P., & Simons, A. J. (2017, August). A formula-driven scalable benchmark model for ABM, applied to FLAME GPU. In European Conference on Parallel Processing (pp. 703-714). Springer, Cham.[ PDF]
  • Ampatzidis, G., & Ergazaki, M. (2017). Toward an “Anti-Balance of Nature” Learning Environment for Non-Biology Major Students: Learning Objectives and Design Criteria. Natural Sciences Education, 46(1).[HTML]
  • Anderson, J. H., Downs, J. A., Loraamm, R., & Reader, S. (2017). Agent-based simulation of Muscovy duck movements using observed habitat transition and distance frequencies. Computers, Environment and Urban Systems, 61, 49-55. [PDF]
  • Aydin, M. E., & Fellows, R. (2017). A reinforcement learning algorithm for building collaboration in multi-agent systems. arXiv preprint arXiv:1711.10574.[PDF]
  • Auerbach, S., & Dix, R. (2017). Competition and spatial efficiency.[PDF]
  • Badham, J., Jansen, C., Shardlow, N., & French, T. (2017). Calibrating with multiple criteria: A demonstration of dominance. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3212
  • Balev, S., Dutot, A., & Olivier, D. (2017). Networking, Networks and Dynamic Graphs. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 85-116).[HTML]
  • Ballet, P., Rivière, J., Pothet, A., Theron, M., Pichavant, K., Abautret, F., ... & Rodin, V. (2017).Modelling and Simulating Complex Systems in Biology: Introducing NetBioDyn–A Pedagogical and Intuitive Agent-Based Software. In Multi-Agent-Based Simulations Applied to Biological and Environmental Systems (pp. 128-158). IGI Global. [ HTML]
  • Banos, A., Corson, N., Daudé, É., Gaudou, B., & Coyrehourcq, S. R. (2017). Macro Models, Micro Models and Network-based Coupling. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 63-84).[ HTML]
  • Banati, H., Bhattacharyya, S., Mani, A., & Köppen, M. (Eds.). (2017). Hybrid Intelligence for Social Networks. Springer International Publishing.[PDF]
  • Barker, A. K., Alagoz, O., & Safdar, N. (2017). Interventions to reduce the incidence of hospital-onset Clostridium difficile infection: An agent-based modeling approach to evaluate clinical effectiveness in adult acute care hospitals. Clinical Infectious Diseases.[PDF]
  • Barrientos, A. H. (2017). The Evolutionary Dynamics of the Mixe Language. In Sociolinguistics-Interdisciplinary Perspectives. InTech.[PDF]
  • Barrientos, A. H., & Andrade, Y. D. (2017). Modelling and Simulation of Complex Adaptive System: The Diffusion of Socio-Environmental Innovation in the RENDRUS Network. In Cvetkovic, D (Ed.)Computer Simulation. InTech. [HTML]
  • Bastien-Olvera, B., Bautista-Gonzalez, E., & Gay-Garcia, C. An agent-based model of food-borne diseases under climate change scenarios in Mexico City.[PDF]
  • Belete, G.F., Voinov, A., Morales, J. (2017). Environmental Modelling & Software. Volume 94, (pp 112–126). [HTML]
  • Bent, J. (2017). Autonomous UAV Path Planning for Wildfire Data Collection. Computer Science Thesis. [PDF]
  • Bezzout, H., Hsaini, S., Azzouzi, S., & El Faylali, H. (2017). Simulation of electromagnetic waves propagation in free space using Netlogo multi-agent approach. In Proceedings of the 2nd international Conference on Big Data, Cloud and Applications (p. 112). ACM.[PDF]
  • Binmad, R., & Li, M. (2017). Improving the Efficiency of an Online Marketplace by Incorporating Forgiveness Mechanism. ACM Transactions on Internet Technology (TOIT), 17(1), 9. [PDF]
  • Birks, D., & Davies, T. (2017). STREET NETWORK STRUCTURE AND CRIME RISK: AN AGENT‐BASED INVESTIGATION OF THE ENCOUNTER AND ENCLOSURE HYPOTHESES. Criminology, 55(4), 900-937.[PDF]
  • Bogatzky, N. (2017). A "gung-ho" Approach Towards Sophic Economy. Economic Alternatives, (1), 160-186. [PDF]
  • Brown, A. J. (2017). DEVELOPMENT OF A SUPPLIER SEGMENTATION METHOD FOR INCREASED RESILIENCE AND ROBUSTNESS: A STUDY USING AGENT BASED MODELING AND SIMULATION. Theses and Dissertations--Mechanical Engineering. 100.[PDF]
  • Brown, A., & Badurdeen, F. (2017). Supplier Segmentation Method for Selection of Resilience-Enabling Procurement Strategies. In IIE Annual Conference. Proceedings (pp. 656-661). Institute of Industrial and Systems Engineers (IISE).[PDF]
  • Bruya, B. (2017). Ethnocentrism and Multiculturalism in Contemporary Philosophy. Philosophy East and West, 67(4), 991-1018.[PDF]
  • Bui, H., Pence, J., Mohaghegh, Z., Reihani, S., & Kee, E. (2017). Spatio-temporal socio-technical risk analysis methodology: an application in emergency response. In American nuclear society (ANS) international topical meeting on probabilistic safety assessment and analysis (PSA). American Nuclear Society Pittsburgh, PA.[PDF]
  • Burrows, A. C. (2017). Teaching Teachers to Think Like Engineers Using NetLogo. Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio.
  • Buurma, J., Hennen, W., & Verwaart, T. (2017). How social unrest started innovations in a food supply chain. Journal of Artificial Societies and Social Simulation, 20(1). doi.org/10.18564/jasss.3350
  • Calvaresi, D., Marinoni, M., Lustrissimini, L., Appoggetti, K., Sernani, P., Dragoni, A. F., ... & Buttazzo, G. Local Scheduling in Multi-Agent Systems: getting ready for safety-critical scenarios. In Proceedings of 15th European Conference on Multi-Agent Systems. Springer (Dec 2017).[ PDF]
  • Caillou, P., Coyrehourq, S. R., Marilleau, N., & Banos, A. (2017). Exploring Complex Models in NetLogo. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 173-208).[HTML]
  • Carver, S., & Quincey, D. (2017). A Conceptual Design of Spatio-Temporal Agent-Based Model for Volcanic Evacuation. Systems, 5(4), 53.[PDF]
  • à Campo, S. Agent-Based Modelling for Online Community Designers. Paper presented at the Conference on Humana Factors in Computing Systems.[PDF]
  • Carely, K., Dobson, G. (2017). Cyber-FIT: An Agent-Based Modelling Approach to Simulating Cyber Warfare. Lecture Notes in Computer Science book series, 10354. Springer, Cham[PDF]
  • Cervantes, D., Flores, D. L., Gutiérrez, E., & Chacón, M. A. (2017). Ce, Tb-Doped Y2SiO5 Phosphor Luminescence Emissions Modeling and Simulation. In Properties and Characterization of Modern Materials (pp. 145-156). Springer Singapore. [PDF]
  • Chavira, M. A. L., & Marcelín-Jiménez, R. (2017). Distributed rewiring model for complex networking: The effect of local rewiring rules on final structural properties. PloS one, 12(11), e0187538.[PDF]
  • Colosimo, A. (2018). Multi-agent Simulations of Population Behavior: A Promising Tool for Systems Biology. In Systems Biology (pp. 307-326). Humana Press, New York, NY.[PDF]
  • Darr, Y. R., & Niazi, M. A. (2017). Towards Self-organized Large-Scale Shape Formation: A Cognitive Agent-Based Computing Approach. arXiv preprint arXiv:1711.06426.[PDF]
  • David, N., Fachada, N., & Rosa, A. C. (2017). Verifying and validating simulations. In Simulating Social Complexity (pp. 173-204). Springer, Cham.[HTML]
  • Davidsson, P., & Verhagen, H. (2017). Types of simulation. In Simulating Social Complexity (pp. 23-37). Springer, Cham.[PDF]
  • de la Fuente, D., Gómez, A., Ponte, B., & Costas, J.Agent-Based Prototyping for Business Management: An Example Based on the Newsvendor Problem.[PDF]
  • de Lima Corrêa, L., Inostroza-Ponta, M., & Dorn, M. (2017, June). An evolutionary multi-agent algorithm to explore the high degree of selectivity in three-dimensional protein structures. In Evolutionary Computation (CEC), 2017 IEEE Congress on (pp. 1111-1118). IEEE. [PDF]
  • Deividi Moreira, Fernando Santos, Matheus Barbieri, Ingrid Nunes, Ana L. C BazzanABStractme: Modularized Environment Modeling in Agent-based Simulations[PDF]
  • dos Santos, T. R. E., & Nakane, M. I. (2017). Dynamic Bank Runs: an agent-based approach (No. 465).[PDF]
  • Dubovi, I., Dagan, E., Nasar, L., Mazbar, O. S., & Levy, S. T. (2017). Follow the Glucose Molecule: Learning Pharmacology by Exploring Multi-Scale Agent-Based Computer Models of Cellular Biochemical Processes and their Interactions Between Organs. [HTML]
  • Fagiolo, G. & Roventini, A. (2017). Macroeconomic policy in DSGE and agent-based models redux: New developments and challenges ahead. Journal of Artificial Societies and Social Simulation, 20(1). doi.org/10.18564/jasss.3280
  • Falcone, R., & Sapienza, A. (2017, November). Using Sources Trustworthiness in Weather Scenarios: The Special Role of the Authority. In Conference of the Italian Association for Artificial Intelligence (pp. 3-16). Springer, Cham.[PDF]
  • Fan, S., Chen, X., & Sun, Q. (2017). Emergent Research of Employee Safety Awareness Based on Multi-agent Model. International Conference on Applied Human Factors and Ergonomics, 17(5), 320-327). [PDF]
  • Feliciani, T., Flache, A., & Tolsma, J. (2017). How, when and where can spatial segregation induce opinion polarization? Two competing models. Journal of Artificial Societies and Social Simulation, 20(2). doi.org/10.18564/jasss.3419
  • Fernando, T., & Rupasinghe, T. D. Simulation of Data Plans for Operate Revenues In Telco Industry.[PDF]
  • Frank, K., Xu, R., & Penuel, W. R. (2017). The Micro-Dynamics of Network Leverage: Implications for Change Agents External to an Organization.[PDF]
  • García-Magariño, I., Lombas, A. S., Plaza, I., & Medrano, C. (2017). ABS-SOCI: An Agent-Based Simulator of Student Sociograms. Applied Sciences, 7(11), 1126.[PDF]
  • Gaudou, B., Lang, C., Marilleau, N., Savin, G., Coyrehourcq, S. R., & Nicod, J. M. (2017). NetLogo, an Open Simulation Environment. In Agent-based Spatial Simulation with NetLogo, Volume 2 (pp. 1-36).[HTML]
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  • 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.
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  • 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]
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  • 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]
  • Xu, B., Liu, R., & Liu, W. (2014). Individual Bias and Organizational Objectivity: An Agent-Based Simulation. Journal of Artificial Societies and Social Simulation (JASSS), 17 (2): 2. [HTML] (March 2014)
  • 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]
  • Al-Roomi, M., Salman, A., & Ahmad, I. (2013, November). Analyzing MBSA performance using NetLogo. In 2013 European Modelling Symposium (pp. 67-72). IEEE.
  • 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).
  • Bezirgiannis, N. (2013). Improving Performance of Simulation Software Using Haskell's Concurrency & Parallelism. Universiteit Utrecht. [HTML] (Sept 2013)
  • 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.
  • Biondo, A.E., Pluchino, A., & Rapisarda, A. (2013). Return Migration After Brain Drain: A Simulation Approach. Journal of Artificial Societies and Social Simulation (JASSS), 16(2), 11. [HTML]
  • 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.
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