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Agent Based Modeling and Simulation: An Informatics Perspective

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Abstract

The term computer simulation is related to the usage of a computational model in order to improve the understanding of a system's behavior and/or to evaluate strategies for its operation, in explanatory or predictive schemes. There are cases in which practical or ethical reasons make it impossible to realize direct observations: in these cases, the possibility of realizing 'in-machina' experiments may represent the only way to study, analyze and evaluate models of those realities. Different situations and systems are characterized by the presence of autonomous entities whose local behaviors (actions and interactions) determine the evolution of the overall system; agent-based models are particularly suited to support the definition of models of such systems, but also to support the design and implementation of simulators. Agent-Based models and Multi-Agent Systems (MAS) have been adopted to simulate very different kinds of complex systems, from the simulation of socio-economic systems to the elaboration of scenarios for logistics optimization, from biological systems to urban planning. This paper discusses the specific aspects of this approach to modeling and simulation from the perspective of Informatics, describing the typical elements of an agent-based simulation model and the relevant research.

Suggested Citation

  • Stefania Bandini & Sara Manzoni & Giuseppe Vizzari, 2009. "Agent Based Modeling and Simulation: An Informatics Perspective," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-4.
    • Handle: RePEc:jas:jasssj:2009-69-1
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    1. Masih Akhbari & Neil Grigg, 2013. "A Framework for an Agent-Based Model to Manage Water Resources Conflicts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4039-4052, September.
    2. Giuseppe Vizzari & Thomas Cecconello, 2022. "Pedestrian Simulation with Reinforcement Learning: A Curriculum-Based Approach," Future Internet, MDPI, vol. 15(1), pages 1-25, December.
    3. Yamamoto, Hiroki & Yanagisawa, Daichi & Feliciani, Claudio & Nishinari, Katsuhiro, 2019. "Body-rotation behavior of pedestrians for collision avoidance in passing and cross flow," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 486-510.
    4. Masih Akhbari & Neil Grigg, 2015. "Managing Water Resources Conflicts: Modelling Behavior in a Decision Tool," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5201-5216, November.
    5. Pavlović, Boban & Ivezić, Dejan & Živković, Marija, 2022. "Transition pathways of household heating in Serbia: Analysis based on an agent-based model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    6. Lu, Peng & Wen, Feier & Li, Yan & Chen, Dianhan, 2021. "Multi-agent modeling of crowd dynamics under mass shooting cases," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    7. Jorge Faleiro, 2018. "A Language for Large-Scale Collaboration in Economics: A Streamlined Computational Representation of Financial Models," Papers 1809.06471, arXiv.org.
    8. Habtamu Tkubet Ebuy & Hind Bril El Haouzi & Riad Benelmir & Remi Pannequin, 2023. "Occupant Behavior Impact on Building Sustainability Performance: A Literature Review," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    9. Chareunsy, Andrea K., 2018. "Diffusion of development initiatives in a southern Lao community: An agent based evaluation," Journal of Asian Economics, Elsevier, vol. 54(C), pages 53-68.
    10. Mattia Pellegrino & Gianfranco Lombardo & Stefano Cagnoni & Agostino Poggi, 2022. "High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation," Future Internet, MDPI, vol. 14(3), pages 1-23, March.
    11. Crick, Florence & Jenkins, Katie & Surminski, Swenja, 2018. "Strengthening insurance partnerships in the face of climate change: insights from an agent-based model of flood insurance in the UK," LSE Research Online Documents on Economics 87669, London School of Economics and Political Science, LSE Library.
    12. Aparajita Jaiswal & Tugba Karabiyik, 2022. "Characterizing Undergraduate Students’ Systems-Thinking Skills through Agent-Based Modeling Simulation," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
    13. Amir Ali Safaei Pirooz & Mohammad J. Sanjari & Young-Jin Kim & Stuart Moore & Richard Turner & Wayne W. Weaver & Dipti Srinivasan & Josep M. Guerrero & Mohammad Shahidehpour, 2023. "Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation," Energies, MDPI, vol. 16(8), pages 1-16, April.
    14. Farhadi, Saber & Nikoo, Mohammad Reza & Rakhshandehroo, Gholam Reza & Akhbari, Masih & Alizadeh, Mohammad Reza, 2016. "An agent-based-nash modeling framework for sustainable groundwater management: A case study," Agricultural Water Management, Elsevier, vol. 177(C), pages 348-358.
    15. Christophe Le Page & Nicolas Becu & Pierre Bommel & François Bousquet, 2012. "Participatory Agent-Based Simulation for Renewable Resource Management: The Role of the Cormas Simulation Platform to Nurture a Community of Practice," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-10.
    16. Abbas Mirzaei & Mansour Zibaei, 2021. "Water Conflict Management between Agriculture and Wetland under Climate Change: Application of Economic-Hydrological-Behavioral Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 1-21, January.
    17. Tommaso Venturini & Pablo Jensen & Bruno Latour, 2015. "Fill in the Gap: A New Alliance for Social and Natural Sciences," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-11.
    18. Dimitris Kremmydas, 2012. "Agent based modeling for agricultural policy evaluation: A review," Working Papers 2012-3, Agricultural University of Athens, Department Of Agricultural Economics.

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