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An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs).[1] A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science.[2] Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.[2]
Agent-based models are a kind of microscale model[3] that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence, which some express as "the whole is greater than the sum of its parts". In other words, higher-level system properties emerge from the interactions of lower-level subsystems. Or, macro-scale state changes emerge from micro-scale agent behaviors. Or, simple behaviors (meaning rules followed by agents) generate complex behaviors (meaning state changes at the whole system level).
Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status,[4] using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.[5]
Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) an environment. ABMs are typically implemented as computer simulations, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior.
- ^ Grimm, Volker; Railsback, Steven F. (2005). Individual-based Modeling and Ecology. Princeton University Press. p. 485. ISBN 978-0-691-09666-7.
- ^ a b Niazi, Muaz; Hussain, Amir (2011). "Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey" (PDF). Scientometrics. 89 (2): 479–499. arXiv:1708.05872. doi:10.1007/s11192-011-0468-9. hdl:1893/3378. S2CID 17934527. Archived from the original (PDF) on October 12, 2013.
- ^ Gustafsson, Leif; Sternad, Mikael (2010). "Consistent micro, macro, and state-based population modelling". Mathematical Biosciences. 225 (2): 94–107. doi:10.1016/j.mbs.2010.02.003. PMID 20171974.
- ^ "Agent-Based Models of Industrial Ecosystems". Rutgers University. October 6, 2003. Archived from the original on July 20, 2011.
- ^ Bonabeau, E. (May 14, 2002). "Agent-based modeling: Methods and techniques for simulating human systems". Proceedings of the National Academy of Sciences of the United States of America. 99 (Suppl 3): 7280–7. Bibcode:2002PNAS...99.7280B. doi:10.1073/pnas.082080899. PMC 128598. PMID 12011407.