Agent-based model: Difference between revisions

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{{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).

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===Early developments===

The history of the agent-based model can be traced back to the [[Von Neumann universal constructor|Von Neumann machine]], a theoretical machine capable of reproduction. The device [[John von Neumann|von Neumann]] proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then built upon by von Neumann's friend [[Stanislaw Ulam]], also a mathematician; Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The idea intrigued von Neumann, who drew it up—creating the first of the devices later termed [[cellular automata]].

Another advance was introduced by the mathematician [[John Horton Conway|John Conway]]. He constructed the well-known [[Conway's Game of Life|Game of Life]]. Unlike von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional [[checkerboard]].

[[File:Поле игры "Жизнь".png|thumb|upright|Conway's Game of Life]]

Another advance was introduced by the mathematician [[John Horton Conway|John Conway]]. He constructed the well-known [[Conway's Game of Life|Game of Life]]. Unlike von Neumann's machine, the Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional [[checkerboard]].

The [[Simula]] programming language, developed in the mid 1960s and widely implemented by the early 1970s, was the first framework for automating step-by-step agent simulations.

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In the late 1970s, [[Paulien Hogeweg]] and Bruce Hesper began experimenting with individual models of [[ecology]]. One of their first results was to show that the social structure of bumble-bee colonies emerged as a result of simple rules that govern the behaviour of individual bees.<ref name="hogeweg">{{cite journal |last=Hogeweg |first=Paulien |title=The ontogeny of the interaction structure in bumble bee colonies: a MIRROR model |year=1983 |journal=Behavioral Ecology and Sociobiology |volume=12 |issue=4 |pages=271–283 |doi=10.1007/BF00302895 |bibcode=1983BEcoS..12..271H |s2cid=22530183 }}</ref>

They introduced the ToDo principle, referring to the way agents "do what there is to do" at any given time.

In the early 1980s, [[Robert Axelrod (political scientist)|Robert Axelrod]] hosted a tournament of [[Prisoner's Dilemma]] strategies and had them interact in an agent-based manner to determine a winner. Axelrod would go on to develop many other agent-based models in the field of political science that examine phenomena from [[ethnocentrism]] to the dissemination of culture.<ref name="Axelrod_1997">{{Cite book |last=Axelrod |given=Robert |author-link=Robert Axelrod (political scientist) |year=1997 |title=The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration |publisher=Princeton: Princeton University Press |isbn=978-0-691-01567-5 }}</ref>

[[File:Rule cohesion.gif|thumb|Flocking behavior model]]

By the late 1980s, [[Craig Reynolds (computer graphics)|Craig Reynolds]]' work on [[flocking behavior|flocking]] models contributed to the development of some of the first biological agent-based models that contained social characteristics. He tried to model the reality of lively biological agents, known as [[artificial life]], a term coined by [[Christopher Langton]].

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In the late 1990s, the merger of TIMS and ORSA to form [[INFORMS]], and the move by INFORMS from two meetings each year to one, helped to spur the CMOT group to form a separate society, the North American Association for Computational Social and Organizational Sciences (NAACSOS). Kathleen Carley was a major contributor, especially to models of social networks, obtaining [[National Science Foundation]] funding for the annual conference and serving as the first President of NAACSOS. She was succeeded by David Sallach of the [[University of Chicago]] and [[Argonne National Laboratory]], and then by Michael Prietula of [[Emory University]]. At about the same time NAACSOS began, the European Social Simulation Association (ESSA) and the Pacific Asian Association for Agent-Based Approach in Social Systems Science (PAAA), counterparts of NAACSOS, were organized. As of 2013, these three organizations collaborate internationally. The First World Congress on Social Simulation was held under their joint sponsorship in Kyoto, Japan, in August 2006.{{citation needed|date=April 2012}} The Second World Congress was held in the northern Virginia suburbs of Washington, D.C., in July 2008, with [[George Mason University]] taking the lead role in local arrangements.

===2000s and later===

More recently, [[Ron Sun]] developed methods for basing agent-based simulation on models of human cognition, known as [[cognitive social simulation]].<ref>{{cite book |editor1-last=Sun |editor1-first=Ron |editor1-link=Ron Sun |title=Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation |date=March 2006 |publisher=[[Cambridge University Press]] |isbn=978-0-521-83964-8 |url=https://www.cambridge.org/0521839645}}</ref> Bill McKelvey, Suzanne Lohmann, Dario Nardi, Dwight Read and others at [[UCLA]] have also made significant contributions in organizational behavior and decision-making. Since 1991, UCLA has arranged a conference at Lake Arrowhead, California, that has become another major gathering point for practitioners in this field.<ref name="Regents of the University of California">{{cite web |title=UCLA Lake Arrowhead Symposium: History |url=https://www.uclaarrowheadsymposium.org/history/ |website=uclaarrowheadsymposium.org |publisher=UCLA Institute of Transportation Studies |access-date=11 February 2024 |ref=arrowhead}}</ref>

=== 2020 and later ===

After the advent of [[large language model]]s, researchers began applying interacting language models to agent based modeling. In one widely cited paper, agentic language models interacted in a sandbox environment to perform activities like planning birthday parties and holding elections.<ref>{{Cite arXiv |last1=Park |first1=Joon Sung |last2=O'Brien |first2=Joseph |last3=Cai |first3=Carrie |last4=Morris |first4=Meredith |last5=Liang |first5=Percey |last6=Bernstein |first6=Michael |title=Generative Agents: Interactive Simulacra of Human Behavior |date=2023 |class=cs.HC |eprint=2304.03442 }}</ref>

==Theory==

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===Framework===

Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models.<ref>{{cite journal |author=Aditya Kurve |author2=Khashayar Kotobi |author3=George Kesidis |title=An agent-based framework for performance modeling of an optimistic parallel discrete event simulator |journal=Complex Adaptive Systems Modeling |volume=1 |pagesarticle-number=12 |doi=10.1186/2194-3206-1-12 |year=2013 |doi-access=free }}</ref><ref>{{cite journal |first=Muaz A. K. |last=Niazi |title=Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems |hdl=1893/3365 |date=2011-06-30 }} (PhD Thesis)</ref><ref>Niazi, M.A. and Hussain, A (2012), Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods [https://www.springer.com/biomed/neuroscience/book/978-94-007-3851-5 Cognitive Agent-based Computing] {{Webarchive|url=https://web.archive.org/web/20121224084838/http://www.springer.com/biomed/neuroscience/book/978-94-007-3851-5 |date=December 24, 2012 }}</ref> describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:

# Complex Network Modeling Level for developing models using interaction data of various system components.

# Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. This can e.g. be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers.

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{{main|Agent-based model in biology}}

Agent-based modeling has been used extensively in biology, including the analysis of the spread of [[epidemics]],<ref>{{cite arXiv |eprint=nlin/0403035 |last1=Situngkir |first1=Hokky |title=Epidemiology Through Cellular Automata: Case of Study Avian Influenza in Indonesia |year=2004 }}</ref> and the threat of [[biowarfare]], [[Agent-based model in biology|biological applications]] including [[population dynamics]],<ref>{{cite journal |last=Caplat |first=Paul |author2=Anand, Madhur |author3=Bauch, Chris |title=Symmetric competition causes population oscillations in an individual-based model of forest dynamics |journal=Ecological Modelling |date=March 10, 2008 |volume=211 |issue=3–4 |pages=491–500 |doi=10.1016/j.ecolmodel.2007.10.002|bibcode=2008EcMod.211..491C }}</ref> stochastic gene expression,<ref>{{Cite journal|last=Thomas|first=Philipp|date=December 2019|title=Intrinsic and extrinsic noise of gene expression in lineage trees|journal=Scientific Reports|volume=9|issue=1|pagesarticle-number=474|doi=10.1038/s41598-018-35927-x|issn=2045-2322|pmc=6345792|pmid=30679440|bibcode=2019NatSR...9..474T}}</ref> plant-animal interactions,<ref>Fedriani JM, T Wiegand, D Ayllón, F Palomares, A Suárez-Esteban and V. Grimm. 2018. Assisting seed dispersers to restore old-fields: an individual-based model of the interactions among badgers, foxes, and Iberian pear trees. Journal of Applied Ecology 55: 600–611.</ref> vegetation ecology,<ref name="Ch'ng, E. (2009)">Ch'ng, E. (2009) An Artificial Life-Based Vegetation Modelling Approach for Biodiversity Research, in Nature-Inspired informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science and Engineering, R. Chiong, Editor. 2009, IGI Global: Hershey, PA. http://complexity.io/Publications/NII-alifeVeg-eCHNG.pdf {{Webarchive|url=https://web.archive.org/web/20131113173223/http://complexity.io/Publications/NII-alifeVeg-eCHNG.pdf |date=November 13, 2013 }}</ref> migratory ecology,<ref>{{cite report|first1=F.G.|last1=Weller|first2=E.B.|last2=Webb|first3=W.S.|last3=Beatty|first4=S.|last4=Fogenburg|first5=D.|last5=Kesler|first6=R.H.|last6=Blenk|first7=J.M.|last7=Eadie|first8=K.|last8=Ringelman |first9=M. L.|last9=Miller|year= 2022|title=Agent-based modeling of movements and habitat selection by mid-continent mallards|publisher=U.S. Department of Interior, Fish and Wildlife Service|series=Cooperator Science Series|id=FWS/CSS-143-2022|___location=Washington, D. C|doi=10.3996/css47216360}}</ref> landscape diversity,<ref>{{Cite journal |last1=Wirth |first1=E. |last2=Szabó |first2=Gy. |last3=Czinkóczky |first3=A. |date=2016-06-07 |journal=ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |volume=III-8 |pages=145–151 |doi=10.5194/isprs-annals-iii-8-145-2016 |bibcode=2016ISPAnIII8..145W|title=Measure of Landscape Heterogeneity by Agent-Based Methodology |doi-access=free }}</ref> [[sociobiology]],<ref name=Lima2009>{{cite journal

|first1=Francisco W.S. |last1=Lima

|first2=Tarik |last2=Hadzibeganovic |first3=Dietrich |last3=Stauffer.

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}}</ref> the growth and decline of ancient civilizations, evolution of ethnocentric behavior,<ref>{{cite journal |last1=Lima |first1=Francisco W. S. |last2=Hadzibeganovic |first2=Tarik |last3=Stauffer |first3=Dietrich |year=2009 |title=Evolution of ethnocentrism on undirected and directed Barabási–Albert networks |journal=Physica A |volume=388 |issue=24 |pages=4999–5004 |doi=10.1016/j.physa.2009.08.029 |bibcode=2009PhyA..388.4999L |arxiv=0905.2672 |s2cid=18233740 }}</ref> forced displacement/migration,<ref>{{cite book |title=The Chaos of Forced Migration: A Modeling Means to an Humanitarian End |first=Scott |last=Edwards |date=June 9, 2009 |publisher=[[VDM Verlag]] |pages=168 |isbn=978-3-639-16516-6}}</ref> language choice dynamics,<ref>{{cite journal |last1=Hadzibeganovic |first1=Tarik |last2=Stauffer |first2=Dietrich |last3=Schulze |first3=Christian |year=2009 |title=Agent-based computer simulations of language choice dynamics |journal=Annals of the New York Academy of Sciences |volume=1167 |issue=1|pages=221–229 |doi=10.1111/j.1749-6632.2009.04507.x |pmid=19580569 |bibcode=2009NYASA1167..221H |s2cid=32790067 }}</ref> [[Cognitive model#Dynamical systems|cognitive modeling]], and biomedical applications including modeling 3D breast tissue formation/morphogenesis,<ref>{{cite journal |last1=Tang |first1=Jonathan|author2-link=Heiko Enderling |last2=Enderling |first2=Heiko |last3=Becker-Weimann |first3=Sabine |last4=Pham |first4=Christopher |last5=Polyzos |first5=Aris |last6=Chen |first6=Charlie |last7=Costes |first7=Sylvain |year=2011 |title=Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling |journal=Integrative Biology |volume=3 |issue=4 |pages=408–21 |doi=10.1039/c0ib00092b |pmid=21373705 |pmc=4009383 }}</ref> the effects of ionizing radiation on mammary stem cell subpopulation dynamics,<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Fernando-Garcia |first2=Ignacio |last3=Vijayakumar |first3=Sangeetha |last4=Martinez-Ruis |first4=Haydeliz |last5=Illa-Bochaca |first5=Irineu |last6=Nguyen |first6=David |last7=Mao |first7=Jian-Hua |last8=Costes |first8=Sylvain |last9=Barcellos-Hoff |first9=Mary Helen |year=2014 |title=Irradiation of juvenile, but not adult, mammary gland increases stem cell self-renewal and estrogen receptor negative tumors |journal=Stem Cells |volume=32 |issue=3 |pages=649–61 |doi=10.1002/stem.1533 |pmid=24038768 |s2cid=32979016 |doi-access=free }}</ref> inflammation,<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Ley |first2=Klaus |last3=Hunt |first3=C. Anthony |year=2007 |title=Dynamics of in silico leukocyte rolling, activation, and adhesion |journal=BMC Systems Biology |volume=1 |issue=14 |pages=14 |doi=10.1186/1752-0509-1-14 |pmid=17408504 |pmc=1839892 |doi-access=free }}</ref>

<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Hunt |first2=C. Anthony |year=2010 |title=Identifying the rules of engagement enabling leukocyte rolling, activation, and adhesion |journal=PLOS Computational Biology |volume=6 |issue=2 |pages=e1000681 |doi=10.1371/journal.pcbi.1000681 |pmid=20174606 |pmc=2824748 |bibcode=2010PLSCB...6E0681T |doi-access=free }}</ref>

and the human [[immune system]],<ref>{{cite book |last1=Castiglione |first1=Filippo |first2=Franco |last2=Celada |url=http://www.crcpress.com/product/isbn/9781466597488 |title=Immune System Modeling and Simulation |publisher=CRC Press, Boca Raton |year=2015 |pages=274 |isbn=978-1-4665-9748-8 |access-date=December 17, 2017 |archive-date=February 4, 2023 |archive-url=https://web.archive.org/web/20230204160700/https://www.routledge.com/Immune-System-Modelling-and-Simulation/Castiglione-Celada/p/book/9781466597488 |url-status=live }}</ref> and the evolution of foraging behaviors.<ref>{{cite journal |last1=Liang |first1=Tong |last2=Brinkman |first2=Braden A. W. |title=Evolution of innate behavioral strategies through competitive population dynamics |journal=PLOS Computational Biology |date=14 March 2022 |volume=18 |issue=3 |pages=e1009934 |doi=10.1371/journal.pcbi.1009934 |doi-access=free |pmid=35286315 |bibcode=2022PLSCB..18E9934L |language=en |issn=1553-7358|pmc=8947601 }}</ref> Agent-based models have also been used for developing decision support systems such as for breast cancer.<ref>{{Cite book |doi=10.1109/ICICT.2009.5267202 |chapter-url=http://www.cs.stir.ac.uk/~man/papers/ICICT_Cameraready_June20_09.pdf |url-status=dead |archive-url=https://web.archive.org/web/20110614051810/http://www.cs.stir.ac.uk/~man/papers/ICICT_Cameraready_June20_09.pdf |archive-date=June 14, 2011 |df=mdy-all |chapter=A new hybrid agent-based modeling & simulation decision support system for breast cancer data analysis |title=2009 International Conference on Information and Communication Technologies |pages=134–139 |year=2009 |last1=Siddiqa |first1=Amnah |last2=Niazi |first2=Muaz |last3=Mustafa |first3=Farah |last4=Bokhari |first4=Habib |last5=Hussain |first5=Amir |last6=Akram |first6=Noreen |last7=Shaheen |first7=Shabnum |last8=Ahmed |first8=Fouzia |last9=Iqbal |first9=Sarah |isbn=978-1-4244-4608-7 |s2cid=14433449 }} (Breast Cancer DSS)</ref> Agent-based models are increasingly being used to model pharmacological systems in early stage and pre-clinical research to aid in drug development and gain insights into biological systems that would not be possible ''a priori''.<ref name=CPT>{{cite journal |last1=Butler |first1=James |last2=Cosgrove |first2=Jason |last3=Alden |first3=Kieran |last4=Read |first4=Mark |last5=Kumar |first5=Vipin |last6=Cucurull-Sanchez |first6=Lourdes |last7=Timmis |first7=Jon |last8=Coles |first8=Mark |title=Agent-Based Modeling in Systems Pharmacology |journal=CPT: Pharmacometrics & Systems Pharmacology |date=2015 |volume=4 |issue=11 |pages=615–629 |doi=10.1002/psp4.12018 |pmid = 26783498|pmc=4716580 }}</ref> Military applications have also been evaluated.<ref>{{cite book |title=Engineering Principles of Combat Modeling and Distributed Simulation |first1=Gnana |last1=Barathy |first2=Levent |last2=Yilmaz |first3=Andreas |last3=Tolk |___location=Hoboken, NJ |publisher=[[John Wiley & Sons|Wiley]] |pages=669–714 |date=March 2012 |doi=10.1002/9781118180310.ch27 |chapter=Agent Directed Simulation for Combat Modeling and Distributed Simulation |isbn=9781118180310}}</ref> Moreover, agent-based models have been recently employed to study molecular-level biological systems.<ref>{{Cite journal |last1=Azimi |first1=Mohammad |last2=Jamali |first2=Yousef |last3=Mofrad |first3=Mohammad R. K. |title=Accounting for Diffusion in Agent Based Models of Reaction-Diffusion Systems with Application to Cytoskeletal Diffusion |journal=PLOS ONE |volume=6 |issue=9 |pages=e25306 |doi=10.1371/journal.pone.0025306 |pmc=3179499 |pmid=21966493 |year=2011 |bibcode=2011PLoSO...625306A|doi-access=free }}</ref><ref>{{Cite journal |last1=Azimi |first1=Mohammad |last2=Mofrad |first2=Mohammad R. K. |title=Higher Nucleoporin-Importinβ Affinity at the Nuclear Basket Increases Nucleocytoplasmic Import |journal=PLOS ONE |volume=8 |issue=11 |pages=e81741 |doi=10.1371/journal.pone.0081741 |pmc=3840022 |pmid=24282617 |year=2013 |bibcode=2013PLoSO...881741A|doi-access=free }}</ref><ref>{{Cite journal |last1=Azimi |first1=Mohammad |last2=Bulat |first2=Evgeny |last3=Weis |first3=Karsten |last4=Mofrad |first4=Mohammad R. K. |date=2014-11-05 |title=An agent-based model for mRNA export through the nuclear pore complex |journal=Molecular Biology of the Cell |volume=25 |issue=22 |pages=3643–3653 |doi=10.1091/mbc.E14-06-1065 |pmc=4230623 |pmid=25253717}}</ref> Agent-based models have also been written to describe ecological processes at work in ancient systems, such as those in dinosaur environments and more recent ancient systems as well.<ref>{{Cite journal |last1=Pahl |first1=Cameron C. |last2=Ruedas |first2=Luis |title=Carnosaurs as Apex Scavengers: Agent-based simulations reveal possible vulture analogues in late Jurassic Dinosaurs |journal=Ecological Modelling |volume=458 |doi=10.1016/j.ecolmodel.2021.109706|year=2021|pagearticle-number=109706 |bibcode=2021EcMod.45809706P }}</ref><ref>{{Cite journal |last1=Volmer |display-authors=etal |date=2017 |title=Did Panthera pardus (Linnaeus, 1758) become extinct in Sumatra because of competition for prey? Modeling interspecific competition within the Late Pleistocene carnivore guild of the Padang Highlands, Sumatra |journal=Palaeogeography, Palaeoclimatology, Palaeoecology |volume=487 |pages=175–186 |doi=10.1016/j.palaeo.2017.08.032|bibcode=2017PPP...487..175V }}</ref><ref>{{Cite journal |last1=Hagen |first1=Oskar |last2=Flück |first2=Benjamin |last3=Fopp|first3=Fabian |last4=Cabral |first4=Juliano C. |last5=Hartig |first5=Florian |last6=Pontarp |first6=Mikael |last7=Rangel |first7=Thiago F. |last8=Pellissier |first8=Loïc |title=gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity |journal=PLOS Biology |volume=19|doi=10.1371/journal.pbio.3001340|year=2021|issue=7 |page=e3001340 |pmid=34252071 |pmc=8384074 |s2cid=235807562 |doi-access=free }}</ref>

=== In epidemiology ===

Agent-based models now complement traditional [[Compartmental models in epidemiology|compartmental]] models, the usual type of epidemiological models. ABMs have been shown to be superior to compartmental models in regard to the accuracy of predictions.<ref>{{Cite journal|last1=Eisinger|first1=Dirk|last2=Thulke|first2=Hans-Hermann|date=2008-04-01|title=Spatial pattern formation facilitates eradication of infectious diseases|journal=The Journal of Applied Ecology|volume=45|issue=2|pages=415–423|doi=10.1111/j.1365-2664.2007.01439.x|issn=0021-8901|pmc=2326892|pmid=18784795|bibcode=2008JApEc..45..415E }}</ref><ref>{{Cite book|url=https://press.princeton.edu/books/hardcover/9780691190822/agent-based-and-individual-based-modeling|title=Agent-Based and Individual-Based Modeling|date=2019-03-26|isbn=978-0-691-19082-2|language=en|last1=Railsback|first1=Steven F.|last2=Grimm|first2=Volker|publisher=Princeton University Press |access-date=October 19, 2020|archive-date=October 24, 2020|archive-url=https://web.archive.org/web/20201024163738/https://press.princeton.edu/books/hardcover/9780691190822/agent-based-and-individual-based-modeling|url-status=live}}</ref> Recently, ABMs such as [[CovidSim]] by epidemiologist [[Neil Ferguson (epidemiologist)|Neil Ferguson]], have been used to inform public health (nonpharmaceutical) interventions against the spread of [[Severe acute respiratory syndrome coronavirus 2|SARS-CoV-2]].<ref>{{Cite journal|last=Adam|first=David|date=2020-04-02|title=Special report: The simulations driving the world's response to COVID-19|journal=Nature|language=en|volume=580|issue=7803|pages=316–318|doi=10.1038/d41586-020-01003-6|pmid=32242115|bibcode=2020Natur.580..316A|s2cid=214771531|doi-access=}}</ref> Epidemiological ABMs have been criticized for simplifying and unrealistic assumptions.<ref>{{Cite journal|last1=Sridhar|first1=Devi|last2=Majumder|first2=Maimuna S.|date=2020-04-21|title=Modelling the pandemic|url=https://www.bmj.com/content/369/bmj.m1567|journal=BMJ|language=en|volume=369|pages=m1567|doi=10.1136/bmj.m1567|issn=1756-1833|pmid=32317328|s2cid=216074714|doi-access=free|access-date=October 19, 2020|archive-date=May 16, 2021|archive-url=https://web.archive.org/web/20210516061544/https://www.bmj.com/content/369/bmj.m1567|url-status=live|url-access=subscription}}</ref><ref>{{Cite journal|last1=Squazzoni|first1=Flaminio|last2=Polhill|first2=J. Gareth|last3=Edmonds|first3=Bruce|last4=Ahrweiler|first4=Petra|last5=Antosz|first5=Patrycja|last6=Scholz|first6=Geeske|last7=Chappin|first7=Émile|last8=Borit|first8=Melania|last9=Verhagen|first9=Harko|last10=Giardini|first10=Francesca|last11=Gilbert|first11=Nigel|date=2020|title=Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action|url=http://jasss.soc.surrey.ac.uk/23/2/10.html|journal=Journal of Artificial Societies and Social Simulation|volume=23|issue=2|pages=10|doi=10.18564/jasss.4298|s2cid=216426533|issn=1460-7425|doi-access=free|access-date=October 19, 2020|archive-date=February 24, 2021|archive-url=https://web.archive.org/web/20210224024334/http://jasss.soc.surrey.ac.uk/23/2/10.html|url-status=live|hdl=10037/19057|hdl-access=free}}</ref> Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated.<ref>{{Cite journal|last1=Maziarz|first1=Mariusz|last2=Zach|first2=Martin|date=2020|title=Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal|url= |journal=Journal of Evaluation in Clinical Practice|language=en|volume=26|issue=5|pages=1352–1360|doi=10.1111/jep.13459|issn=1365-2753|pmc=7461315|pmid=32820573}}</ref> The ABMs for such simulations are mostly based on [[synthetic population]]s, since the data of the actual population is not always available.<ref>{{cite journal |last1=Manout |first1=O. |last2=Ciari |first2=F. |title=Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach |journal=Frontiers in Built Environment |date=2021 |volume=7 |article-number=654279 |doi=10.3389/fbuil.2021.654279 |url=https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/pt/covidwho-1346397 |language=en|doi-access=free }}</ref>

{| class="wikitable"

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|-

! Program !! Year !! Citation !! Description

|-

| EpiCast || 2021 ||<ref>{{Citation

| last1 = Del Valle

| first1 = Sara Y.

| last2 = Germann

| first2 = Timothy Clark

| last3 = Fairchild

| first3 = Geoffrey

| last4 = Manore

| first4 = Carrie Anna

| display-authors = 1

| title = EpiCast: Simulating Epidemics with Extreme Detail

| year = 2021

| type = Techreport

| publisher = Los Alamos National Laboratory (LANL), Los Alamos, NM, United States

| doi = 10.2172/1783478

| osti = 1783478

| doi-access = free

}}</ref> || Simulates the spread of disease throughout the population of the United States of America.

|-

| Covasim || 2021 ||<ref>{{Citation |last1=Kerr |first1=Cliff |last2=Stuart |first2=Robyn |display-authors=1 |year=2021 |title=Covasim: an agent-based model of COVID-19 dynamics and interventions |work=medRxiv |volume=17 |issue=7 |pages=e1009149 |doi=10.1371/journal.pcbi.1009149 |doi-access=free |pmid=34310589 |pmc=8341708 |bibcode=2021PLSCB..17E9149K }}</ref> || SEIR model implemented in Python with an emphasis on features for studying the effects of interventions.

Line 102 ⟶ 129:

| OpenABM-Covid19 || 2021 ||<ref>{{Citation |last1=Hinch |first1=Robert |last2=Probert |first2=William |display-authors=1 |year=2021 |title=OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing |journal=PLOS Computational Biology |volume=17 |issue=7 |pages=e1009146 |doi=10.1371/journal.pcbi.1009146 |pmid=34252083 |pmc=8328312 |bibcode=2021PLSCB..17E9146H |doi-access=free }}</ref> || Epidemic model of the spread of COVID-19, simulating every individual in a population with both R and Python interfaces but using C for heavy computation.

|-

| JUNE || 2021 || <ref>{{Citation

| last1 = Aylett-Bullock

| first1 = Joseph

| last2 = Cuesta-Lazaro

| first2 = Carolina

| last3 = Quera-Bofarull

| first3 = Arnau

| last4 = Icaza-Lizaola

| first4 = Miguel

| last5 = Sedgewick

| first5 = Aidan

| last6 = Truong

| first6 = Henry

| last7 = Curran

| first7 = Aoife

| last8 = Elliott

| first8 = Edward

| last9 = Caulfield

| first9 = Tristan

| last10 = Fong

| first10 = Kevin

| last11 = Vernon

| first11 = Ian

| last12 = Williams

| first12 = Julian

| last13 = Bower

| first13 = Richard

| last14 = Krauss

| first14 = Frank

| title = JUNE: open-source individual-based epidemiology simulation

| journal = Royal Society Open Science

| volume = 8

| issue = 7

| pages = 210506

| year = 2021

| doi = 10.1098/rsos.210506

| pmid = 34295529

| pmc = 8261230

| bibcode = 2021RSOS....810506A

}}</ref><ref>{{Citation

| last1 = Vernon

| first1 = I.

| last2 = Owen

| first2 = J.

| last3 = Aylett-Bullock

| first3 = J.

| last4 = Cuesta-Lazaro

| first4 = C.

| last5 = Frawley

| first5 = J.

| last6 = Quera-Bofarull

| first6 = A.

| last7 = Sedgewick

| first7 = A.

| last8 = Shi

| first8 = D.

| last9 = Truong

| first9 = H.

| last10 = Turner

| first10 = M.

| last11 = Walker

| first11 = J.

| last12 = Caulfield

| first12 = T.

| last13 = Fong

| first13 = K.

| last14 = Krauss

| first14 = F.

| title = Bayesian emulation and history matching of JUNE

| year = 2022

| journal = Philosophical Transactions of the Royal Society A

| volume = 380

| issue = 2233

| pages = 20220039

| doi = 10.1098/rsta.2022.0039

| pmid = 35965471

| doi-access = free

| pmc = 9376712

| bibcode = 2022RSPTA.38020039V

}}</ref> || Epidemic model used in the UK names after [[June Almeida]].

|-

| OpenCOVID || 2021 ||<ref>{{Citation |last1=Shattock |first1=Andrew |last2=Le Rutte |first2=Epke |last3=Duenner |first3=Robert |display-authors=2 |year=2021 |title=Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland |journal=Epidemics |volume=38 |issue=7 |pages=100535 |doi=10.1016/j.epidem.2021.100535 |pmid=34923396 |pmc=8669952 |bibcode=2021PLSCB..17E9146H }}</ref><ref>{{cite web |url=https://github.com/SwissTPH/OpenCOVID |title=Git-repository with open access source-code for OpenCOVID. |author=<!--Not stated--> |date=2022-01-31 |website=GitHub |publisher=Swiss TPH |access-date=2022-02-15 |archive-date=February 15, 2022 |archive-url=https://web.archive.org/web/20220215120617/https://github.com/SwissTPH/OpenCOVID |url-status=live }}</ref> || An individual-based transmission model of SARS-CoV-2 infection and COVID-19 disease dynamics, developed at the [[Swiss Tropical and Public Health Institute]].

|}

Line 107 ⟶ 215:

===In business, technology and network theory===

Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include [[marketing]],<ref name="Rand and Rust (2011) IJRM">{{cite journal |last1=Rand |first1=William |last2=Rust |first2=Roland T. |year=2011 |title=Agent-based modeling in marketing: Guidelines for rigor |journal=International Journal of Research in Marketing |volume=28 |issue=3 |pages=181–193 |doi=10.1016/j.ijresmar.2011.04.002}}</ref> [[organizational behaviour]] and [[cognition]],<ref name="Hughes et al (2012) JOOP">{{cite journal |last1=Hughes |first1=H. P. N. |last2=Clegg |first2=C. W. |last3=Robinson |first3=M. A. |last4=Crowder |first4=R. M. |year=2012 |title=Agent-based modelling and simulation: The potential contribution to organizational psychology |journal=Journal of Occupational and Organizational Psychology |volume=85 |issue=3 |pages=487–502 |doi=10.1111/j.2044-8325.2012.02053.x }}</ref> [[teamwork|team working]],<ref>{{cite journal |last1=Boroomand |first1=Amin |title=Hard work, risk-taking, and diversity in a model of collective problem solving. |journal=Journal of Artificial Societies and Social Simulation |date=2021 |volume=24 |issue=4 |article-number=10 |doi=10.18564/jasss.4704 |url=https://www.jasss.org/24/4/10.html#:~:text=When%20problems%20are%20simpler%2C%20risk,to%20the%20increase%20in%20diversity|doi-access=free }}</ref><ref name="Crowder et al (2012) IEEE TSMCA">{{cite journal |last1=Crowder |first1=R. M. |last2=Robinson |first2=M. A. |last3=Hughes |first3=H. P. N. |last4=Sim |first4=Y. W. |year=2012 |title=The development of an agent-based modeling framework for simulating engineering team work |journal=IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans |volume=42 |issue=6 |pages=1425–1439 |doi=10.1109/TSMCA.2012.2199304 |s2cid=7985332 }}</ref> [[supply chain optimization]] and logistics, modeling of [[consumer behavior]], including [[word of mouth]], [[social network]] effects, [[distributed computing]], [[workforce management]], and [[Investment management|portfolio management]]. They have also been used to analyze [[traffic congestion]].<ref>{{cite web |url=http://www.tfhrc.gov/advanc/agent.htm |title=Application of Agent Technology to Traffic Simulation |publisher=[[United States Department of Transportation]] |date=May 15, 2007 |access-date=October 31, 2007 |archive-url=https://web.archive.org/web/20110101034847/http://www.tfhrc.gov/advanc/agent.htm |archive-date=January 1, 2011 |url-status=dead |df=mdy-all }}</ref>

Recently, agent based modelling and simulation has been applied to various domains such as studying the impact of publication venues by researchers in the computer science ___domain (journals versus conferences).<ref>{{cite book |last1=Niazi |first1=M. |last2=Baig |first2=A. R. |last3=Hussain |first3=A. |last4=Bhatti |first4=S. |title=2008 Winter Simulation Conference |chapter=Simulation of the research process |year=2008 |editor1-first=S. |editor1-last=Mason |editor2-first=R. |editor2-last=Hill |editor3-first=L. |editor3-last=Mönch |editor4-first=O. |editor4-last=Rose |editor5-first=T. |editor5-last=Jefferson |editor6-first=J. W. |editor6-last=Fowler |pages=1326–1334 |chapter-url=http://www.informs-sim.org/wsc08papers/159.pdf |doi=10.1109/WSC.2008.4736206 |isbn=978-1-4244-2707-9 |hdl=1893/3203 |s2cid=6597668 |access-date=June 7, 2009 |archive-date=June 1, 2011 |archive-url=https://web.archive.org/web/20110601150006/http://www.informs-sim.org/wsc08papers/159.pdf |url-status=live }}</ref> In addition, ABMs have been used to simulate information delivery in ambient assisted environments.<ref>{{cite book |last=Niazi |first=Muaz A. |title=Proceedings of the third international workshop on Use of P2P, grid and agents for the development of content networks |chapter=Self-organized customized content delivery architecture for ambient assisted environments |year=2008 |pages=45–54 |chapter-url=http://www.cs.stir.ac.uk/~man/papers/upg106-niazi.pdf |url-status=dead |archive-url=https://web.archive.org/web/20110614051629/http://www.cs.stir.ac.uk/~man/papers/upg106-niazi.pdf |archive-date=June 14, 2011 |df=mdy-all |doi=10.1145/1384209.1384218 |isbn=9781605581552 |s2cid=16916130 }}</ref> A November 2016 article in [[arXiv]] analyzed an agent based simulation of posts spread in [[Facebook]].<ref>{{Cite arXiv |last1=Nasrinpour |first1=Hamid Reza |last2=Friesen |first2=Marcia R. |last3=McLeod |first3=Robert D. |date=2016-11-22 |title=An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network |eprint=1611.07454 |class=cs.SI}}</ref> In the ___domain of peer-to-peer, ad hoc and other self-organizing and complex networks, the usefulness of agent based modeling and simulation has been shown.<ref>{{cite journal |first1=Muaz |last1=Niazi |first2=Amir |last2=Hussain |title=Agent based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad-Hoc and other Complex Networks |journal=IEEE Communications Magazine |volume=47 |issue=3 |date=March 2009 |pages=163–173 |url=http://www.cs.stir.ac.uk/~man/papers/niaziCommmag.pdf |doi=10.1109/MCOM.2009.4804403 |arxiv=1708.01599 |bibcode=2009IComM..47c.166N |url-status=dead |archive-url=https://web.archive.org/web/20101204212920/http://www.cs.stir.ac.uk/~man/papers/niaziCommmag.pdf |archive-date=December 4, 2010 |df=mdy-all |hdl=1893/2423 |s2cid=23449913 }}</ref> The use of a computer science-based formal specification framework coupled with [[wireless sensor networks]] and an agent-based simulation has recently been demonstrated.<ref>{{cite journal |first1=Muaz |last1=Niazi |first2=Amir |last2=Hussain |year=2011 |title=A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments |journal=IEEE Sensors Journal |volume=11 |issue=2 |pages=404–412 |url=http://cs.stir.ac.uk/~man/papers/Accepted_IEEESensorsAug2010.pdf |doi=10.1109/JSEN.2010.2068044 |url-status=dead |archive-url=https://web.archive.org/web/20110725023733/http://cs.stir.ac.uk/~man/papers/Accepted_IEEESensorsAug2010.pdf |archive-date=July 25, 2011 |df=mdy-all |bibcode=2011ISenJ..11..404N |arxiv=1708.05875 |hdl=1893/3398 |s2cid=15367419 }}</ref>

Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.<ref>{{Cite book |last1=Sarker |first1=R. A. |last2=Ray |first2=T. |chapter=Agent Based Evolutionary Approach: An Introduction |doi=10.1007/978-3-642-13425-8_1 |title=Agent-Based Evolutionary Search |series=Adaptation, Learning, and Optimization |volume=5 |pages=1–11 |year=2010 |isbn=978-3-642-13424-1 }}</ref>

===In team science===

In the realm of team science, agent-based modeling has been utilized to assess the effects of team members' characteristics and biases on team performance across various settings.<ref>{{cite journal |last1=Boroomand |first1=Amin |last2=Smaldino |first2=Paul E. |title=Superiority bias and communication noise can enhance collective problem solving. |journal=Journal of Artificial Societies and Social Simulation |date=2023 |volume=26 |issue=3 |article-number=14 |doi=10.18564/jasss.5154|doi-access=free }}</ref> By simulating interactions between agents—each representing individual team members with distinct traits and biases—this modeling approach enables researchers to explore how these factors collectively influence the dynamics and outcomes of team performance. Consequently, agent-based modeling provides a nuanced understanding of team science, facilitating a deeper exploration of the subtleties and variabilities inherent in team-based collaborations.

===In economics and social sciences===

{{main|Agent-based computational economics|Agent-based social simulation}}

{{see also|Artificial financial market}}

Prior to, and in the wake ofduring the [[Financial crisis of 2007–2008|2008 financial crisis]], interest has grown in ABMs as possible tools for economic analysis.<ref>{{cite book |first=Scott E. |last=Page |year=2008 |title=Agent-Based Models |work=[[The New Palgrave Dictionary of Economics]] |edition=2 |url=http://www.dictionaryofeconomics.com/article?id=pde2008_A000218&edition=current&q=agent-based%20computational%20modeling&topicid=&result_number=1 |access-date=October 3, 2011 |archive-date=February 10, 2018 |archive-url=https://web.archive.org/web/20180210023520/https://www.springernature.com/gp/ |url-status=live }}</ref><ref>{{cite book |editor1-first=Leigh |editor1-last=Testfatsion |editor1-link=Leigh Tesfatsion |editor2-first=Kenneth |editor2-last=Judd |editor2-link=Kenneth Judd |date=May 2006 |title=Handbook of Computational Economics |volume=2 |publisher=[[Elsevier]] |pages=904 |url=http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description |isbn=978-0-444-51253-6 |access-date=January 29, 2012 |archive-url=https://web.archive.org/web/20120306100156/http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description |archive-date=March 6, 2012 |url-status=dead |df=mdy-all }} ([https://www.sciencedirect.com/science/journal/15740021/2 Chapter preview)]</ref> ABMs do not assume the economy can [[Economic equilibrium|achieve equilibrium]] and "[[representative agent]]s" are replaced by agents with [[Heterogeneity in economics#Economic models with heterogeneous agents|diverse, dynamic, and interdependent behavior]] including [[herding]]. ABMs take a [[Microfoundations|"bottom-up" approach]] and can generate extremely complex and volatile simulated economies. ABMs can represent unstable systems with crashes and booms that develop out of non-[[linear]] (disproportionate) responses to proportionally small changes.<ref name="agents">{{cite news |url=httphttps://www.economist.com/nodefinance-and-economics/166361212010/07/22/agents-of-change |title=Agents of change |date=July 22, 2010 |newspaper=The Economist |access-date=February 16, 2011 |archive-date=January 23, 2011 |archive-url=https://web.archive.org/web/20110123110948/http://www.economist.com/node/16636121 |url-status=live }}</ref> A July 2010 article in ''[[The Economist]]'' looked at ABMs as alternatives to [[Dynamic stochastic general equilibrium|DSGE]] models.<ref name="agents"/> The journal ''[[Nature (journal)|Nature]]'' also encouraged agent-based modeling with an editorial that suggested ABMs can do a better job of representing financial markets and other economic complexities than standard models<ref>{{cite journal |title=A model approach |journal=[[Nature (journal)|Nature]] |volume=460 |issue=7256 |page=667 |date=August 6, 2009 |doi=10.1038/460667a |pmid=19661863 |bibcode=2009Natur.460Q.667.|doi-access=free }}</ref> along with an essay by [[J. Doyne Farmer]] and Duncan Foley that argued ABMs could fulfill both the desires of Keynes to represent a complex economy and of Robert Lucas to construct models based on microfoundations.{{sfn|Farmer|Foley|2009|p=685}} Farmer and Foley pointed to progress that has been made using ABMs to model parts of an economy, but argued for the creation of a very large model that incorporates low level models.{{sfn|Farmer|Foley|2009|p=686}} By modeling a complex system of [[financial analyst|analysts]] based on three distinct behavioral profiles – imitating, anti-imitating, and indifferent – [[financial market]]s were simulated to high accuracy. Results showed a correlation between network morphology and the stock market index.<ref>Stefan, F., & Atman, A. (2015). Is there any connection between the network morphology and the fluctuations of the stock market index? Physica A: Statistical Mechanics and Its Applications, (419), 630-641.</ref> However, the ABM approach has been criticized for its lack of robustness between models, where similar models can yield very different results.<ref>{{cite journal |last1=Dawid |first1=Herbert |last2=Gatti |first2=Delli |date=January 2018 |title=Agent-based macroeconomics |journal=Handbook of Computational Economics |volume=4 |pages=63–156|doi=10.1016/bs.hescom.2018.02.006 }}</ref><ref>{{cite journal |last1=Rand |first1=William |last2=Rust |first2=Roland T. |date=July 2011 |title=Agent-based modeling in marketing: Guidelines for rigor |journal=International Journal of Research in Marketing |volume=28 |issue=3 |pages=181–193 |doi=10.1016/j.ijresmar.2011.04.002 }}</ref>

ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment<ref>{{cite journal |first1=G.D.P.A |last2=Wullschleger |first2=Tobias |last3=Müller |first3=Hanspeter |last4=Schmitt |first4=Gerhard |last1=Aschwanden |year=2009 |title=Evaluation of 3D city models using automatic placed urban agents |journal=Automation in Construction |volume=22 |pages=81–89 |doi=10.1016/j.autcon.2011.07.001}}</ref> and the examination of public policy applications to land-use.<ref>{{cite journal |first1=Daniel G. |last1=Brown |last2=Page |first2=Scott E. |last3=Zellner |first3=Moira |last4=Rand |first4=William |year=2005 |title=Path dependence and the validation of agent-based spatial models of land use |journal=International Journal of Geographical Information Science |volume=19 |issue=2 |pages=153–174 |doi=10.1080/13658810410001713399|doi-access=free |bibcode=2005IJGIS..19..153B }}</ref> There is also a growing field of socio-economic analysis of infrastructure investment impact using ABM's ability to discern systemic impacts upon a socio-economic network.<ref>{{cite report |first1=Paul |last2=Stiff |first2=David |last1=Smetanin |year=2015 |title=Investing in Ontario's Public Infrastructure: A Prosperity at Risk Perspective, with an analysis of the Greater Toronto and Hamilton Area |publisher=The Canadian Centre for Economic Analysis |url=http://www.cancea.ca/sites/economic-analysis.ca/files/reports/CANCEA%20Report%20-%20Investing%20in%20Ontario%27s%20Infrastructure%20FINAL%20Oct%202015%20Web.pdf |access-date=November 17, 2016 |archive-date=November 18, 2016 |archive-url=https://web.archive.org/web/20161118042407/http://www.cancea.ca/sites/economic-analysis.ca/files/reports/CANCEA%20Report%20-%20Investing%20in%20Ontario%27s%20Infrastructure%20FINAL%20Oct%202015%20Web.pdf |url-status=live }}</ref> Heterogeneity and dynamics can be easily built in ABM models to address wealth inequality and social mobility.<ref>{{Cite journal |last1=Yang |first1=Xiaoliang |last2=Zhou |first2=Peng |date=April 2022 |title=Wealth inequality and social mobility: A simulation-based modelling approach |journal=Journal of Economic Behavior & Organization |language=en |volume=196 |pages=307–329 |doi=10.1016/j.jebo.2022.02.012 |s2cid=247143315 |doi-access=free |hdl=10419/261231 |hdl-access=free }}</ref>

ABMs have also been proposed as applied educational tools for diplomats in the field of [[international relations]]<ref>{{Cite journal |last1=Butcher |first1=Charity |last2=Njonguo |first2=Edwin |date=2021-12-22 |title=Simulating Diplomacy: Learning Aid or Business as Usual? |url=|journal=Journal of Political Science Education |language=en |volume=17 |issue=sup1 |pages=185–203 |doi=10.1080/15512169.2020.1803080 |issn=1551-2169}}</ref> and for domestic and international policymakers to enhance their evaluation of [[public policy]].<ref>{{Cite journal |last1=Gilbert |first1=Nigel |last2=Ahrweiler |first2=Petra |last3=Barbrook-Johnson |first3=Pete |last4=Narasimhan |first4=Kavin Preethi |last5=Wilkinson |first5=Helen |date=2018 |title=Computational Modelling of Public Policy: Reflections on Practice |url=http://jasss.soc.surrey.ac.uk/21/1/14.html |journal=Journal of Artificial Societies and Social Simulation |language=en |volume=21 |issue=1 |article-number=14 |doi=10.18564/jasss.3669 |issn=1460-7425|hdl=10044/1/102075 |hdl-access=free }}</ref>

ABM is also becoming increasingly popular in the field of [[Energy modeling|energy systems analysis]], particularly in the context of [[Open energy system models#Electricity sector models|electricity market modelling]]. Notable examples of such models include [[Open energy system models#AMIRIS|AMIRIS]],<ref>{{Cite journal |last=Schimeczek |first=Christoph |last2=Nienhaus |first2=Kristina |last3=Frey |first3=Ulrich |last4=Sperber |first4=Evelyn |last5=Sarfarazi |first5=Seyedfarzad |last6=Nitsch |first6=Felix |last7=Kochems |first7=Johannes |last8=Ghazi |first8=A. Achraf El |date=2023-04-17 |title=AMIRIS: Agent-based Market model for the Investigation of Renewable and Integrated energy Systems |url=https://joss.theoj.org/papers/10.21105/joss.05041 |journal=Journal of Open Source Software |language=en |volume=8 |issue=84 |pages=5041 |doi=10.21105/joss.05041 |issn=2475-9066}}</ref> ASSUME,<ref>{{Cite journal |last=Harder |first=Nick |last2=Qussous |first2=Ramiz |last3=Weidlich |first3=Anke |date=2023-10-01 |title=Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning |url=https://www.sciencedirect.com/science/article/pii/S2666546823000678 |journal=Energy and AI |volume=14 |pages=100295 |doi=10.1016/j.egyai.2023.100295 |issn=2666-5468}}</ref> [[Open energy system models#EMLab-Generation|EMLab]],<ref>{{Cite journal |last=Jimenez |first=I. Sanchez |last2=Ribó-Pérez |first2=D. |last3=Cvetkovic |first3=M. |last4=Kochems |first4=J. |last5=Schimeczek |first5=C. |last6=de Vries |first6=L. J. |date=2024-04-15 |title=Can an energy only market enable resource adequacy in a decarbonized power system? A co-simulation with two agent-based-models |url=https://www.sciencedirect.com/science/article/pii/S0306261924000783 |journal=Applied Energy |volume=360 |pages=122695 |doi=10.1016/j.apenergy.2024.122695 |issn=0306-2619}}</ref> and PowerACE,<ref>{{Cite web |last=Fraunholz |first=Christoph |date=2021 |title=Market Design for the Transition to Renewable Electricity Systems |url=https://publikationen.bibliothek.kit.edu/1000133282 |access-date=2024-08-13 |website=publikationen.bibliothek.kit.edu |language=de |doi=10.5445/ir/1000133282}}</ref> which facilitate the analysis of [[Electricity market|electricity markets]] in the context of the ongoing [[Energy transition|renewable energy transition]].

=== In water management ===

ABMs have also been applied in water resources planning and management, particularly for exploring, simulating, and predicting the performance of infrastructure design and policy decisions,<ref>{{Cite journal|last=Berglund|first=Emily Zechman|date=November 2015|title=Using Agent-Based Modeling for Water Resources Planning and Management|url=http://ascelibrary.org/doi/10.1061/%28ASCE%29WR.1943-5452.0000544|journal=Journal of Water Resources Planning and Management|language=en|volume=141|issue=11|pagesarticle-number=04015025|doi=10.1061/(ASCE)WR.1943-5452.0000544|issn=0733-9496|access-date=September 18, 2021|archive-date=January 19, 2022|archive-url=https://web.archive.org/web/20220119081321/http://ascelibrary.org/doi/10.1061/(ASCE)WR.1943-5452.0000544|url-status=live|url-access=subscription}}</ref> and in assessing the value of cooperation and information exchange in large water resources systems.<ref>{{Cite journal|last1=Giuliani|first1=M.|last2=Castelletti|first2=A.|date=July 2013|title=Assessing the value of cooperation and information exchange in large water resources systems by agent-based optimization: MAS Framework for Large Water Resources Systems|journal=Water Resources Research|language=en|volume=49|issue=7|pages=3912–3926|doi=10.1002/wrcr.20287|s2cid=128659104 |doi-access=free}}</ref>

===Organizational ABM: agent-directed simulation===

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===Self-driving cars===

Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents.<ref>{{cite journal |last1=Hallerbach |first1=S. |last2=Xia |first2=Y. |last3=Eberle |first3=U. |last4=Koester |first4=F. |title=Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles |journal=SAE International Journal of Connected and Automated Vehicles |date=2018 |volume=1 |issue=2 |pages=93–106 |publisher=SAE International |doi=10.4271/2018-01-1066 |url=https://www.researchgate.net/publication/324194968}}</ref> [[Waymo]] has created a multi-agent simulation environment Carcraft to test algorithms for [[self-driving car]]s.<ref>{{cite news |last1=Madrigal |first1=Story by Alexis C. |title=Inside Waymo's Secret World for Training Self-Driving Cars |url=https://www.theatlantic.com/technology/archive/2017/08/inside-waymos-secret-testing-and-simulation-facilities/537648/ |access-date=14 August 2020 |work=The Atlantic |archive-date=August 14, 2020 |archive-url=https://web.archive.org/web/20200814195438/https://www.theatlantic.com/technology/archive/2017/08/inside-waymos-secret-testing-and-simulation-facilities/537648/ |url-status=live }}</ref><ref>{{cite journal |last1=Connors |first1=J. |last2=Graham |first2=S. |last3=Mailloux |first3=L. |title=Cyber Synthetic Modeling for Vehicle-to-Vehicle Applications |journal=International Conference on Cyber Warfare and Security |date=2018 |page=594-XI |publisher=Academic Conferences International Limited}}</ref> It simulates traffic interactions between human drivers, pedestrians and automated vehicles. People's behavior is imitated by artificial agents based on data of real human behavior. The basic idea of using agent-based modeling to understand self-driving cars was discussed as early as 2003.<ref>{{Cite book|last1=Yang|first1=Guoqing|last2=Wu|first2=Zhaohui|last3=Li|first3=Xiumei|last4=Chen|first4=Wei|title=Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems |chapter=SVE: Embedded agent based smart vehicle environment |date=2003|chapter-url=https://ieeexplore.ieee.org/document/1252782|volume=2|pages=1745–1749 vol.2|doi=10.1109/ITSC.2003.1252782|isbn=0-7803-8125-4|s2cid=110177067|access-date=August 19, 2021|archive-date=January 31, 2022|archive-url=https://web.archive.org/web/20220131211240/https://ieeexplore.ieee.org/document/1252782|url-status=live}}</ref>

==Implementation==

Many [[Comparison of agent-based modeling software|ABM frameworks]] are designed for serial [[Von Neumann architecture|von-Neumann computer architectures]], limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size,<ref name="Lysenko 2008 MegaScale">{{cite journal |last1=Lysenko |first1=Mikola |last2=D'Souza |first2=Roshan M. |title=A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units |journal=Journal of Artificial Societies and Social Simulation |date=2008 |volume=11 |issue=4 |pages=10 |url=http://jasss.soc.surrey.ac.uk/11/4/10.html |access-date=16 April 2019 |issn=1460-7425 |archive-date=April 26, 2019 |archive-url=https://web.archive.org/web/20190426210422/http://jasss.soc.surrey.ac.uk/11/4/10.html |url-status=live }}</ref> scalability restrictions may hinder model validation.<ref>{{cite journal |last1=Gulyás |first1=László |last2=Szemes |first2=Gábor |last3=Kampis |first3=George |last4=de Back |first4=Walter |title=A Modeler-Friendly API for ABM Partitioning |journal=Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2009 |date=2009 |volume=2 |pages=219–226 |url=https://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1649189 |___location=San Diego, California, US |access-date=April 16, 2019 |archive-date=April 16, 2019 |archive-url=https://web.archive.org/web/20190416181927/https://proceedings.asmedigitalcollection.asme.org/proceeding.aspx%3Farticleid%3D1649189 |url-status=live }}</ref> Such limitations have mainly been addressed using [[distributed computing]], with frameworks such as Repast HPC<ref>{{cite journal |last1=Collier |first1=N. |last2=North |first2=M. |title=Parallel agent-based simulation with Repast for High Performance Computing |journal=Simulation |volume=89 |issue=10 |pages=1215–1235 |doi=10.1177/0037549712462620 |year=2013 |s2cid=29255621 }}</ref> specifically dedicated to these typetypes of implementations. While such approaches map well to [[Computer cluster|cluster]] and [[Supercomputer architecture|supercomputer]] architectures, issues related to communication and synchronization,<ref>{{cite book |last1=Fujimoto |first1=R. |title=2015 Winter Simulation Conference (WSC) |chapter=Parallel and distributed simulation |date=2015 |pages=45–59 |doi=10.1109/WSC.2015.7408152 |___location=Huntington Beach, CA, US |isbn=978-1-4673-9743-8 |s2cid=264924790 |chapter-url=http://www.lib.ncsu.edu/resolver/1840.4/5268 |access-date=September 6, 2020 |archive-date=February 4, 2023 |archive-url=https://web.archive.org/web/20230204160704/https://repository.lib.ncsu.edu/handle/1840.4/5268 |url-status=live }}</ref><ref>{{cite journal |last1=Shook |first1=E. |last2=Wang |first2=S. |last3=Tang |first3=W. |title=A communication-aware framework for parallel spatially explicit agent-based models |journal=International Journal of Geographical Information Science |date=2013 |volume=27 |issue=11 |pages=2160–2181 |doi=10.1080/13658816.2013.771740 |publisher=Taylor & Francis|bibcode=2013IJGIS..27.2160S |s2cid=41702653 }}</ref> as well as deployment complexity,<ref>{{cite book |last1=Jonas |first1=E. |last2=Pu |first2=Q. |last3=Venkataraman |first3=S. |last4=Stoica |first4=I. |last5=Recht |first5=B. |title=Proceedings of the 2017 Symposium on Cloud Computing |chapter=Occupy the cloud: Distributed computing for the 99% |date=2017 |pages=445–451 |doi=10.1145/3127479.3128601 |arxiv=1702.04024 |publisher=ACM |isbn=978-1-4503-5028-0 |s2cid=854354 }}</ref> remain potential obstacles for their widespread adoption.

A recent development is the use of data-parallel algorithms on Graphics Processing Units [[GPU]]s for ABM simulation.<ref name="Lysenko 2008 MegaScale" /><ref>{{cite web |author=Isaac Rudomin |url=https://sites.google.com/site/rudominisaac/shader-agents |title=Large Crowds in the GPU |year=2006 |publisher=[[Monterrey Institute of Technology and Higher Education]] |display-authors=etal |url-status=dead |archive-url=https://web.archive.org/web/20140111054342/https://sites.google.com/site/rudominisaac/shader-agents |archive-date=January 11, 2014 }}</ref><ref>{{cite journal |first1=Paul |last1=Richmond |first2=Daniela M. |last2=Romano |url=http://www.dcs.shef.ac.uk/~daniela/Paul_abgpu_IWSV_2008.pdf |archive-url=https://wayback.archive-it.org/all/20090115220835/http://www.dcs.shef.ac.uk/~daniela/Paul_abgpu_IWSV_2008.pdf |url-status=dead |archive-date=January 15, 2009 |title=Agent Based GPU, a Real-time 3D Simulation and Interactive Visualisation Framework for Massive Agent Based Modelling on the GPU |journal=Proceedings International Workshop on Super Visualisation (IWSV08) |year=2008 |access-date=April 27, 2012 |df=mdy-all }}</ref> The extreme memory bandwidth combined with the sheer number crunching power of multi-processor GPUs has enabled simulation of millions of agents at tens of frames per second.

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===Integration with other modeling forms===

Since Agent-Based Modeling is more of a modeling framework than a particular piece of software or platform, it has often been used in conjunction with other modeling forms. For instance, agent-based models have also been combined with [[Geographic Information Systems]] (GIS). This provides a useful combination where the ABM serves as a process model and the GIS system can provide a model of pattern.<ref>{{cite journal |last1=Brown |first1=Daniel G. |last2=Riolo |first2=Rick |last3=Robinson |first3=Derek T. |last4=North |first4=Michael |last5=Rand |first5=William |title=Spatial Process and Data Models: Toward Integration of agent-based models and GIS |journal=Journal of Geographical Systems |date=2005 |volume=7 |issue=1 |pages=25–47 |doi=10.1007/s10109-005-0148-5 |publisher=Springer|bibcode=2005JGS.....7...25B |hdl=2027.42/47930 |s2cid=14059768 |hdl-access=free }}</ref> Similarly, [[Social Network Analysis]] (SNA) tools and agent-based models are sometimes integrated, where the ABM is used to simulate the dynamics on the network while the SNA tool models and analyzes the network of interactions.<ref>{{cite journal | last1=Zhang | first1=J. | last2=Tong | first2=L. | last3=Lamberson | first3=P.J. | last4=Durazo-Arvizu | first4=R.A. | last5=Luke | first5=A. | last6=Shoham | first6=D.A. | title=Leveraging social influence to address overweight and obesity using agent-based models: The role of adolescent social networks | journal=Social Science & Medicine | publisher=Elsevier BV | volume=125 | year=2015 | issn=0277-9536 | doi=10.1016/j.socscimed.2014.05.049 | pages=203–213| pmid=24951404 | pmc=4306600 }}</ref> Tools like [[GAMA Platform|GAMA]] provide a natural way to integrate [[system dynamics]] and [[Geographic information system|GIS]] with ABM.

==Verification and validation==

[[Verification and validation]] (V&V) of simulation models is extremely important.<ref>{{Cite book |last1=Sargent |first1=R. G. |doi=10.1109/WSC.2000.899697 |chapter=Verification, validation and accreditation of simulation models |title=2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165) |volume=1 |pages=50–59 |year=2000 |isbn=978-0-7803-6579-7 |citeseerx=10.1.1.17.438 |s2cid=57059217 }}</ref><ref>{{cite journal |title=Errors and Artefacts in Agent-Based Modelling |last1=Galán |first1=José Manuel |last2=Izquierdo |first2=Luis |last3=Izquierdo |first3=Segismundo S. |last4=Santos |first4=José Ignacio |last5=del Olmo |first5=Ricardo |last6=López-Paredes |first6=Adolfo |last7=Edmonds |first7=Bruce |journal=Journal of Artificial Societies and Social Simulation |volume=12 |issue=1 |pages=1 |year=2009 |url=http://jasss.soc.surrey.ac.uk/12/1/1.html |access-date=January 26, 2010 |archive-date=June 21, 2009 |archive-url=https://web.archive.org/web/20090621045033/http://jasss.soc.surrey.ac.uk/12/1/1.html |url-status=live }}</ref> Verification involves making sure the implemented model matches the conceptual model, whereas validation ensures that the implemented model has some relationship to the real-world. Face validation, sensitivity analysis, calibration, and statistical validation are different aspects of validation.<ref>{{Cite book |last1=Klügl |first1=F. |chapter=A validation methodology for agent-based simulations |doi=10.1145/1363686.1363696 |title=Proceedings of the 2008 ACM symposium on Applied computing - SAC '08 |pages=39–43 |year=2008 |isbn=9781595937537 |s2cid=9450992 }}</ref> A [[discrete-event simulation]] framework approach for the validation of agent-based systems has been proposed.<ref>{{cite journal |last1=Fortino |first1=G. |first2=A. |last2=Garro |first3=W. |last3=Russo |title=A Discrete-Event Simulation Framework for the Validation of Agent-Based and Multi-Agent Systems |year=2005 |url=http://www-lia.deis.unibo.it/books/woa2005/papers/11.pdf |access-date=September 27, 2009 |archive-date=June 26, 2011 |archive-url=https://web.archive.org/web/20110626211408/http://www-lia.deis.unibo.it/books/woa2005/papers/11.pdf |url-status=live }}</ref> A comprehensive resource on empirical validation of agent-based models can be found here.<ref>{{cite web |title=Empirical Validation: Agent-Based Computational Economics |url=https://www2.econ.iastate.edu/tesfatsi/empvalid.htm |last=Tesfatsion |first=Leigh |publisher=[[Iowa State University]] |access-date=June 24, 2020 |archive-date=June 26, 2020 |archive-url=https://web.archive.org/web/20200626090651/https://www2.econ.iastate.edu/tesfatsi/empvalid.htm |url-status=live }}</ref>

As an example of V&V technique, consider VOMAS (virtual overlay multi-agent system),<ref>{{cite journal |first1=Muaz |last1=Niazi |first2=Amir |last2=Hussain |first3=Mario |last3=Kolberg |title=Verification and Validation of Agent-Based Simulations using the VOMAS approach |journal=Proceedings of the Third Workshop on Multi-Agent Systems and Simulation '09 (MASS '09), as Part of MALLOW 09, Sep 7–11, 2009, Torino, Italy |url=http://www.cs.stir.ac.uk/~man/papers/VOMAS_CRV_aug_05_09_Muazv2.pdf |archive-url=https://web.archive.org/web/20110614052017/http://www.cs.stir.ac.uk/~man/papers/VOMAS_CRV_aug_05_09_Muazv2.pdf |archive-date=June 14, 2011 |url-status=dead}}</ref> a software engineering based approach, where a virtual overlay multi-agent system is developed alongside the agent-based model. Muazi et al. also provide an example of using VOMAS for verification and validation of a forest fire simulation model.<ref>{{cite journalbook |first1=Muaz |last1=Niazi |first2=Qasim |last2=Siddique |first3=Amir |last3=Hussain |first4=Mario |last4=Kolberg |title=Verification & ValidationProceedings of anthe Agent-Based2010 Forest FireSpring Simulation ModelMulticonference |journalchapter=ProceedingsVerification of& the Agent Directed Simulation Symposium 2010, as Partvalidation of thean ACMagent-based SCSforest Springfire Simulationsimulation Multiconferencemodel |pages=142–149 |date=April 11–15, 2010 |doi=10.1145/1878537.1878539 |isbn=978-1-4503-0069-8 |chapter-url=http://cs.stir.ac.uk/~man/papers/2_ADS_09_Final_Paper.pdf |archive-url=https://web.archive.org/web/20110725083937/http://cs.stir.ac.uk/~man/papers/2_ADS_09_Final_Paper.pdf |archive-date=July 25, 2011 |url-status=dead }}</ref><ref>{{cite journal |first=Muaz A. K. |last=Niazi |title=Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems |date=June 11, 2011 |publisher=[[University of Stirling]]|hdl=1893/3365 }} PhD Thesis</ref> Another software engineering method, i.e. [[test-driven development|Test-Driven Development]] has been adapted to for agent-based model validation.<ref>{{Cite journal |last1=Onggo |first1=B.S. |last2=Karatas |first2=M. |doi=10.1016/j.ejor.2016.03.050 |title=Test-driven simulation modelling: A case study using agent-based maritime search-operation simulation. |journal=European Journal of Operational Research |volume=254 |issue=2 |pages=517–531 |year=2016 |url= https://www.sciencedirect.com/science/article/abs/pii/S0377221716301965|archive-date=June 30, 2020 |archive-url=https://web.archive.org/web/20200630141434/https://www.sciencedirect.com/science/article/abs/pii/S0377221716301965 |url-status=live |url-access=subscription }}</ref> This approach has another advantage that allows an automatic validation using unit test tools.

==See also==

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* {{Cite book |last1=Murthy |first1=V. K. |last2=Krishnamurthy |first2=E. V. |chapter=Multiset of Agents in a Network for Simulation of Complex Systems |doi=10.1007/978-3-642-04227-0_6 |title=Recent Advances in Nonlinear Dynamics and Synchronization |series=Studies in Computational Intelligence |volume=254 |pages=153–200 |year=2009 |isbn=978-3-642-04226-3 }}

* {{cite book |last1=Naldi |first1=G. |last2=Pareschi |first2=L. |last3=Toscani |first3=G. |title=Mathematical modeling of collective behavior in socio-economic and life sciences |publisher=Birkhauser |year=2010 |url=https://www.springer.com/birkhauser/mathematics/book/978-0-8176-4945-6 |isbn=978-0-8176-4945-6 |access-date=August 28, 2017 |archive-date=September 1, 2012 |archive-url=https://web.archive.org/web/20120901141452/http://www.springer.com/birkhauser/mathematics/book/978-0-8176-4945-6 |url-status=live }}

* {{Cite journal |last1=Onggo |first1=B.S. |last2=Karatas |first2=M. |doi=10.1016/j.ejor.2016.03.050 |title=Test-driven simulation modelling: A case study using agent-based maritime search-operation simulation. |journal=European Journal of Operational Research |volume=254 |issue=2 |pages=517–531 |year=2016 |url= https://www.sciencedirect.com/science/article/abs/pii/S0377221716301965|archive-date=June 30, 2020 |archive-url=https://web.archive.org/web/20200630141434/https://www.sciencedirect.com/science/article/abs/pii/S0377221716301965 |url-status=live |url-access=subscription }}

* {{Cite journal |last1=O'Sullivan |first1=D. |last2=Haklay |first2=M. |doi=10.1068/a32140 |title=Agent-based models and individualism: Is the world agent-based? |journal=Environment and Planning A |volume=32 |issue=8 |pages=1409–1425 |year=2000 |bibcode=2000EnPlA..32.1409O |s2cid=14131066 |url=http://discovery.ucl.ac.uk/5244/ |type=Submitted manuscript |access-date=October 28, 2018 |archive-date=February 4, 2023 |archive-url=https://web.archive.org/web/20230204160708/https://discovery.ucl.ac.uk/id/eprint/5244/ |url-status=live }}

* {{Cite journal |last1=Preis |first1=T. |last2=Golke |first2=S. |last3=Paul |first3=W. |last4=Schneider |first4=J. J. |title=Multi-agent-based Order Book Model of financial markets |doi=10.1209/epl/i2006-10139-0 |journal=Europhysics Letters (EPL) |volume=75 |issue=3 |pages=510–516 |year=2006 |bibcode=2006EL.....75..510P |ssrn=2180442 |s2cid=56156905 }}

* {{Cite journal |last1=Rudomín |first1=I. |last2=Millán |first2=E. |last3=Hernández |first3=B. N. |doi=10.1016/j.simpat.2005.08.008 |title=Fragment shaders for agent animation using finite state machines |journal=Simulation Modelling Practice and Theory |volume=13 |issue=8 |pages=741–751 |date=November 2005 }}

* {{Cite book |last=Salamon |given=Tomas |author-link=Tomas Salamon |year=2011 |title=Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes |publisher=Bruckner Publishing |isbn=978-80-904661-1-1 |url=http://www.designofagentbasedmodels.info/ |access-date=October 22, 2011 |archive-date=March 17, 2012 |archive-url=https://web.archive.org/web/20120317102501/http://www.designofagentbasedmodels.info/ |url-status=live }}

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* [https://www2.econ.iastate.edu/tesfatsi/abmread.htm On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences]

* [http://www-unix.mcs.anl.gov/~leyffer/listn/slides-06/MacalNorth.pdf Introduction to Agent-based Modeling and Simulation]. [[Argonne National Laboratory]], November 29, 2006.

* {{usurped|1=[https://web.archive.org/web/20240826072145/http://www.abm-ecology.org/ Agent-based models in Ecology – Using computer models as theoretical tools to analyze complex ecological systems]{{dead link|date=December 2017 |bot=InternetArchiveBot |fix-attempted=yes }}

* [https://www.comses.net/about/faq/ Network for Computational Modeling in the Social and Ecological Sciences' Agent Based Modeling FAQ]

* [https://web.archive.org/web/20081015142701/http://www.irisel.com/~jmgomez/IT/doctorate/taller_resumen2.htm Multiagent Information Systems] – Article on the convergence of SOA, BPM and Multi-Agent Technology in the ___domain of the Enterprise Information Systems. Jose Manuel Gomez Alvarez, Artificial Intelligence, [[Technical University of Madrid]] – 2006