Editing Agent-based model (section)

{{Short description|Type of computational models}}
{{distinguish|Microsimulation}}
{{Use mdy dates|date=October 2013}}
{{Multi-agent system}}
An '''agent-based model''' ('''ABM''') is a [[computational models|computational model]] for [[computer simulation|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 system]]s, and [[evolutionary programming]]. [[Monte Carlo method]]s are used to understand the [[Stochastic process|stochasticity]] of these models.  Particularly within ecology, ABMs are also called '''individual-based models''' ('''IBMs''').<ref>{{cite book |last1=Grimm |first1=Volker |first2=Steven F. |last2=Railsback |title=Individual-based Modeling and Ecology |publisher=Princeton University Press |year=2005 |pages=485 |isbn=978-0-691-09666-7}}</ref> 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]].<ref name="Niazi-Hussain">{{cite journal |first1=Muaz |last1=Niazi |first2=Amir |last2=Hussain |year=2011 |title=Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey |journal=Scientometrics |volume=89 |issue=2 |pages=479–499 |doi=10.1007/s11192-011-0468-9 |url=http://cecosm.yolasite.com/resources/Accepted_Scientometrics_ABM_Website.pdf |archive-url=https://web.archive.org/web/20131012005027/http://cecosm.yolasite.com/resources/Accepted_Scientometrics_ABM_Website.pdf |archive-date=October 12, 2013 |url-status=dead|arxiv=1708.05872 |hdl=1893/3378 |s2cid=17934527 }}</ref> Agent-based modeling is related to, but distinct from, the concept of '''[[multi-agent system]]s''' 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.<ref name="Niazi-Hussain"/>

Agent-based models are a kind of [[Microscale and macroscale models|microscale model]]<ref>{{cite journal |first1=Leif |last1=Gustafsson |first2=Mikael |last2=Sternad |year=2010 |title=Consistent micro, macro, and state-based population modelling |journal=Mathematical Biosciences |volume=225 |issue=2 |pages=94–107 |doi=10.1016/j.mbs.2010.02.003 |pmid=20171974 }}</ref> 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 [[bounded rationality|boundedly rational]], presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status,<ref>{{cite web |url=http://policy.rutgers.edu/andrews/projects/abm/abmarticle.htm |title=Agent-Based Models of Industrial Ecosystems |publisher=[[Rutgers University]] |date=October 6, 2003 |archive-url=https://web.archive.org/web/20110720041914/http://policy.rutgers.edu/andrews/projects/abm/abmarticle.htm |archive-date=July 20, 2011 |url-status=dead}}</ref> using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.<ref name="Bonabeau 2002 ABM">{{cite journal |title=Agent-based modeling: Methods and techniques for simulating human systems |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=99 |pages=7280–7 |date=May 14, 2002 |doi=10.1073/pnas.082080899 |pmid=12011407 |pmc=128598 |last1=Bonabeau |first1=E. |issue=Suppl 3 |bibcode=2002PNAS...99.7280B |doi-access=free }}</ref>

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 [[network topology|interaction topology]]; and (5) an environment. ABMs are typically implemented as [[computer simulation]]s, 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.