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Agent Based Modeling and Simulation: An Informatics Perspective




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.

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  • 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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    Cited by:

    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. 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.
    3. Christophe Le Page & Nicolas Becu & Pierre Bommel & Fran├žois 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.
    4. 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.
    5. 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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