Agent-based model#In economics and social sciences

The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s.

The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction. The device 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 Conway. He constructed the well-known 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.

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.

One of the earliest agent-based models in concept was Thomas Schelling's segregation model,{{cite journal |last=Schelling |first=Thomas C. |title=Dynamic Models of Segregation |year=1971 |journal=Journal of Mathematical Sociology |volume=1 |issue=2 |pages=143–186 |url=http://zolaist.org/wiki/images/c/cf/Models_of_Segregation.pdf |doi=10.1080/0022250x.1971.9989794 |access-date=April 21, 2015 |archive-date=December 1, 2016 |archive-url=https://web.archive.org/web/20161201210328/http://zolaist.org/wiki/images/c/cf/Models_of_Segregation.pdf |url-status=live }} which was discussed in his paper "Dynamic Models of Segregation" in 1971. Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome.

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.{{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 }}

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 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.{{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 }}

By the late 1980s, Craig Reynolds' work on 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.

The first use of the word "agent" and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory",{{cite journal |last1=Holland |first1=J.H. |last2=Miller |first2=J.H. |title=Artificial Adaptive Agents in Economic Theory |year=1991 |journal=American Economic Review |volume=81 |issue=2 |pages=365–71 |url=http://zia.hss.cmu.edu/miller/papers/aaa.pdf |archive-url=https://web.archive.org/web/20051027152415/http://zia.hss.cmu.edu/miller/papers/aaa.pdf |url-status=dead |archive-date=2005-10-27 }} based on an earlier conference presentation of theirs. A stronger and earlier candidate is Allan Newell, who in the first Presidential Address of AAAI (published as The Knowledge Level{{Cite journal |last=Newell |first=Allen |date=January 1982 |title=The knowledge level |url=|journal=Artificial Intelligence |volume=18 |issue=1 |pages=87–127 |doi=10.1016/0004-3702(82)90012-1 |s2cid=40702643 |issn=0004-3702}}) discussed intelligent agents as a concept.

At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory (CMOT). This field grew as a special interest group of The Institute of Management Sciences (TIMS) and its sister society, the Operations Research Society of America (ORSA).{{Cite book |last1=Kohler |first1=Timothy |title=Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes |last2=Gumerman |first2=George |publisher=Santa Fe Institute and Oxford University Press |year=2000 |isbn=0-19-513167-3 |location=New York, New York}}

The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by

Joshua M. Epstein and Robert Axtell to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture.{{cite book |first1=Joshua M. |last1=Epstein |author-link1=Joshua M. Epstein |first2=Robert |last2=Axtell |author-link2=Robert Axtell |title=Growing artificial societies: social science from the bottom up |publisher=Brookings Institution Press |date=October 11, 1996 |pages=[https://archive.org/details/growingartificia00epst/page/224 224] |isbn=978-0-262-55025-3 |url-access=registration |url=https://archive.org/details/growingartificia00epst/page/224 }} Other notable 1990s developments included Carnegie Mellon University's Kathleen Carley ABM,{{cite web |url=http://www.casos.cs.cmu.edu/projects/construct/index.php |title=Construct |publisher=Computational Analysis of Social Organizational Systems |access-date=February 19, 2008 |archive-date=October 11, 2008 |archive-url=https://web.archive.org/web/20081011221558/http://www.casos.cs.cmu.edu/projects/construct/index.php |url-status=live }} to explore the co-evolution of social networks and culture. The Santa Fe Institute (SFI) was important in encouraging the development of the ABM modeling platform Swarm under the leadership of Christopher Langton. Research conducted through SFI allowed the expansion of ABM techniques to a number of fields including study of the social and spatial dynamics of small-scale human societies and primates. During this 1990s timeframe Nigel Gilbert published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established a journal from the perspective of social sciences: the Journal of Artificial Societies and Social Simulation (JASSS). Other than JASSS, agent-based models of any discipline are within scope of SpringerOpen journal Complex Adaptive Systems Modeling (CASM).{{Cite web |url=http://www.casmodeling.com/ |title=Springer Complex Adaptive Systems Modeling Journal (CASM) |access-date=July 1, 2012 |archive-date=June 18, 2012 |archive-url=https://web.archive.org/web/20120618154650/http://www.casmodeling.com/ |url-status=live }}

Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. CMOT—later renamed Computational Analysis of Social and Organizational Systems (CASOS)—incorporated more and more agent-based modeling. Samuelson (2000) is a good brief overview of the early history,{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2000/orms-12-00/Designing-Organizations |last=Samuelson |first=Douglas A. |title=Designing Organizations |journal=OR/MS Today |date=December 2000 |access-date=June 17, 2019 |archive-date=June 17, 2019 |archive-url=https://web.archive.org/web/20190617145006/https://www.informs.org/ORMS-Today/Archived-Issues/2000/orms-12-00/Designing-Organizations |url-status=live }} and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments.{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2005/orms-2-05/Agents-of-Change |last=Samuelson |first=Douglas A. |title=Agents of Change |journal=OR/MS Today |date=February 2005 |access-date=June 17, 2019 |archive-date=June 17, 2019 |archive-url=https://web.archive.org/web/20190617145007/https://www.informs.org/ORMS-Today/Archived-Issues/2005/orms-2-05/Agents-of-Change |url-status=live }}{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2006/orms-8-06/Agent-Based-Simulation-Comes-of-Age |last1=Samuelson |first1=Douglas A. |last2=Macal |first2=Charles M. |title=Agent-Based Modeling Comes of Age |journal=OR/MS Today |date=August 2006 |access-date=June 17, 2019 |archive-date=June 17, 2019 |archive-url=https://web.archive.org/web/20190617145008/https://www.informs.org/ORMS-Today/Archived-Issues/2006/orms-8-06/Agent-Based-Simulation-Comes-of-Age |url-status=live }}

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.

More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation.{{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}} 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.{{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}}

After the advent of large language models, 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.{{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 }}

Most computational modeling research describes systems in equilibrium or as moving between equilibria. Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior. The three ideas central to agent-based models are agents as objects, emergence, and complexity.

Agent-based models consist of dynamically interacting rule-based agents. The systems within which they interact can create real-world-like complexity. Typically agents are

situated in space and time and reside in networks or in lattice-like neighborhoods. The location of the agents and their responsive behavior are encoded in algorithmic form in computer programs. In some cases, though not always, the agents may be considered as intelligent and purposeful. In ecological ABM (often referred to as "individual-based models" in ecology), agents may, for example, be trees in a forest, and would not be considered intelligent, although they may be "purposeful" in the sense of optimizing access to a resource (such as water).

The modeling process is best described as inductive. The modeler makes those assumptions thought most relevant to the situation at hand and then watches phenomena emerge from the agents' interactions. Sometimes that result is an equilibrium. Sometimes it is an emergent pattern. Sometimes, however, it is an unintelligible mangle.

In some ways, agent-based models complement traditional analytic methods. Where analytic methods enable humans to characterize the equilibria of a system, agent-based models allow the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent-based modeling. Agent-based models can explain the emergence of higher-order patterns—network structures of terrorist organizations and the Internet, power-law distributions in the sizes of traffic jams, wars, and stock-market crashes, and social segregation that persists despite populations of tolerant people. Agent-based models also can be used to identify lever points, defined as moments in time in which interventions have extreme consequences, and to distinguish among types of path dependency.

Rather than focusing on stable states, many models consider a system's robustness—the ways that complex systems adapt to internal and external pressures so as to maintain their functionalities. The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions.

Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models.{{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 |pages=12 |doi=10.1186/2194-3206-1-12 |year=2013 |doi-access=free }}{{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)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 }} describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:

1. Complex Network Modeling Level for developing models using interaction data of various system components.
2. 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.
3. Descriptive Agent-based Modeling (DREAM) for developing descriptions of agent-based models by means of using templates and complex network-based models. Building DREAM models allows model comparison across scientific disciplines.
4. Validated agent-based modeling using Virtual Overlay Multiagent system (VOMAS) for the development of verified and validated models in a formal manner.

Other methods of describing agent-based models include code templates{{cite web |title=Swarm code templates for model comparison |url=http://www.swarm.org/index.php/Software_templates |publisher=Swarm Development Group |archive-url=https://web.archive.org/web/20080803125909/http://www.swarm.org/index.php/Software_templates |archive-date=August 3, 2008 |url-status=dead}} and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol.{{cite journal |author1=Volker Grimm |author2=Uta Berger |author3=Finn Bastiansen |author4=Sigrunn Eliassen |author5=Vincent Ginot |author6=Jarl Giske |author7=John Goss-Custard |author8=Tamara Grand |author9=Simone K. Heinz |author10=Geir Huse |author11=Andreas Huth |author12=Jane U. Jepsen |author13=Christian Jørgensen |author14=Wolf M. Mooij |author15=Birgit Müller |author16=Guy Pe'er |author17=Cyril Piou |author18=Steven F. Railsback |author19=Andrew M. Robbins |author20=Martha M. Robbins |author21=Eva Rossmanith |author22=Nadja Rüger |author23=Espen Strand |author24=Sami Souissi |author25=Richard A. Stillman |author26=Rune Vabø |author27=Ute Visser |author28=Donald L. DeAngelis |display-authors=3 |title=A standard protocol for describing individual-based and agent-based models |journal=Ecological Modelling |volume=198 |issue=1–2 |date=September 15, 2006 |pages=115–126 |doi=10.1016/j.ecolmodel.2006.04.023 |bibcode=2006EcMod.198..115G |s2cid=11194736 }} (ODD Paper)

The role of the environment where agents live, both macro and micro,Ch'ng, E. (2012) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, November 20–24, 2012, Kobe, Japan. [http://complexity.io/Publications/chng-MacroMicroEnv.pdf Macro and Micro Environment] {{Webarchive|url=https://web.archive.org/web/20131113173313/http://complexity.io/Publications/chng-MacroMicroEnv.pdf |date=November 13, 2013 }} is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generate diversity of behavior.Simon, Herbert A. The sciences of the artificial. MIT press, 1996.

One strength of agent-based modelling is its ability to mediate information flow between scales. When additional details about an agent are needed, a researcher can integrate it with models describing the extra details. When one is interested in the emergent behaviours demonstrated by the agent population, they can combine the agent-based model with a continuum model describing population dynamics. For example, in a study about CD4+ T cells (a key cell type in the adaptive immune system),{{Cite journal|last1=Wertheim|first1=Kenneth Y.|last2=Puniy|first2=Bhanwar Lal|last3=Fleur|first3=Alyssa La|last4=Shah|first4=Ab Rauf|last5=Barberis|first5=Matteo|last6=Helikar|first6=Tomáš|date=2021-08-03|title=A multi-approach and multi-scale platform to model CD4+ T cells responding to infections|journal=PLOS Computational Biology|language=en|volume=17|issue=8|pages=e1009209|doi=10.1371/journal.pcbi.1009209|pmid=34343169|pmc=8376204|bibcode=2021PLSCB..17E9209W|issn=1553-7358 |doi-access=free }} the researchers modelled biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). In the resulting modular model, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models, cell population dynamics are described by an agent-based model, and systemic cytokine concentrations by ordinary differential equations. In this multi-scale model, the agent-based model occupies the central place and orchestrates every stream of information flow between scales.

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

Agent-based modeling has been used extensively in biology, including the analysis of the spread of epidemics,{{cite arXiv |eprint=nlin/0403035 |last1=Situngkir |first1=Hokky |title=Epidemiology Through Cellular Automata: Case of Study Avian Influenza in Indonesia |year=2004}} and the threat of biowarfare, biological applications including population dynamics,{{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 }} stochastic gene expression,{{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|pages=474|doi=10.1038/s41598-018-35927-x|issn=2045-2322|pmc=6345792|pmid=30679440|bibcode=2019NatSR...9..474T}} plant-animal interactions,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. vegetation ecology,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 }} migratory ecology,{{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}} landscape diversity,{{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 }} sociobiology,{{cite journal

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

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

|year=2009

|title=Evolution of ethnocentrism on undirected and directed Barabási-Albert networks

|journal=Physica A

|volume=388

|pages=4999–5004

|doi=10.1016/j.physa.2009.08.029

|issue=24

|arxiv=0905.2672|bibcode=2009PhyA..388.4999L|s2cid=18233740

}} the growth and decline of ancient civilizations, evolution of ethnocentric behavior,{{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 }} forced displacement/migration,{{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}} language choice dynamics,{{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 }} cognitive modeling, and biomedical applications including modeling 3D breast tissue formation/morphogenesis,{{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 }} the effects of ionizing radiation on mammary stem cell subpopulation dynamics,{{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 }} inflammation,{{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 }}

{{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 }}

and the human immune system,{{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 }} and the evolution of foraging behaviors.{{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 }} Agent-based models have also been used for developing decision support systems such as for breast cancer.{{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) 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.{{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 }} Military applications have also been evaluated.{{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=Wiley |pages=669–714 |date=March 2012 |doi=10.1002/9781118180310.ch27 |chapter=Agent Directed Simulation for Combat Modeling and Distributed Simulation |isbn=9781118180310}} Moreover, agent-based models have been recently employed to study molecular-level biological systems.{{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 }}{{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 }}{{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}} 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.{{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|page=109706 |bibcode=2021EcMod.45809706P }}{{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 }}{{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 }}

Agent-based models now complement traditional 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.{{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 }}{{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}} Recently, ABMs such as CovidSim by epidemiologist Neil Ferguson, have been used to inform public health (nonpharmaceutical) interventions against the spread of SARS-CoV-2.{{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=}} Epidemiological ABMs have been criticized for simplifying and unrealistic assumptions.{{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}}{{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}} Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated.{{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}} The ABMs for such simulations are mostly based on synthetic populations, since the data of the actual population is not always available.{{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 |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 }}

Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include marketing,{{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}} organizational behaviour and cognition,{{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 }} team working,{{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 |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 }}{{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 }} supply chain optimization and logistics, modeling of consumer behavior, including word of mouth, social network effects, distributed computing, workforce management, and portfolio management. They have also been used to analyze traffic congestion.{{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 }}

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).{{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 }} In addition, ABMs have been used to simulate information delivery in ambient assisted environments.{{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 }} A November 2016 article in arXiv analyzed an agent based simulation of posts spread in Facebook.{{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}} 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.{{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 |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 }} The use of a computer science-based formal specification framework coupled with wireless sensor networks and an agent-based simulation has recently been demonstrated.{{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 }}

Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.{{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 }}

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.{{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 |doi=10.18564/jasss.5154|doi-access=free }} 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.

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

{{see also|Artificial financial market}}

Prior to, and in the wake of the 2008 financial crisis, interest has grown in ABMs as possible tools for economic analysis.{{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 }}{{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)] ABMs do not assume the economy can achieve equilibrium and "representative agents" are replaced by agents with diverse, dynamic, and interdependent behavior including herding. ABMs take a "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.{{cite news |url=http://www.economist.com/node/16636121 |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 }} A July 2010 article in The Economist looked at ABMs as alternatives to DSGE models. The 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{{cite journal |title=A model approach |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 }} 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 analysts based on three distinct behavioral profiles – imitating, anti-imitating, and indifferent – financial markets were simulated to high accuracy. Results showed a correlation between network morphology and the stock market index.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. However, the ABM approach has been criticized for its lack of robustness between models, where similar models can yield very different results.{{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 }}{{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 }}

ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment{{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}} and the examination of public policy applications to land-use.{{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 }} 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.{{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 }} Heterogeneity and dynamics can be easily built in ABM models to address wealth inequality and social mobility.{{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 }}

ABMs have also been proposed as applied educational tools for diplomats in the field of international relations{{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}} and for domestic and international policymakers to enhance their evaluation of public policy.{{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 |doi=10.18564/jasss.3669 |issn=1460-7425|hdl=10044/1/102075 |hdl-access=free }}

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,{{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|pages=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}} and in assessing the value of cooperation and information exchange in large water resources systems.{{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}}

The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems."{{cite web |url=http://www.eng.auburn.edu/~yilmaz/ADS.html |title=Agent-Directed Simulation |access-date=August 9, 2011 |archive-date=September 27, 2011 |archive-url=https://web.archive.org/web/20110927165246/http://www.eng.auburn.edu/~yilmaz/ADS.html |url-status=live }} Systems for Agents (sometimes referred to as agents systems) are systems implementing agents for the use in engineering, human and social dynamics, military applications, and others. Agents for Systems are divided in two subcategories. Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or enhancing cognitive capabilities. Agent-based systems focus on the use of agents for the generation of model behavior in a system evaluation (system studies and analyses).

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.{{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}} Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars.{{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 }}{{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}} 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.{{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}}

Many ABM frameworks are designed for serial von-Neumann computer architectures, limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size,{{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 }} scalability restrictions may hinder model validation.{{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 }} Such limitations have mainly been addressed using distributed computing, with frameworks such as Repast HPC{{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 }} specifically dedicated to these type of implementations. While such approaches map well to cluster and supercomputer architectures, issues related to communication and synchronization,{{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 }}{{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 }} as well as deployment complexity,{{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 }} remain potential obstacles for their widespread adoption.

A recent development is the use of data-parallel algorithms on Graphics Processing Units GPUs for ABM simulation.{{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 }}{{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 }} 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.

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.{{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 }} 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.{{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 }} Tools like GAMA provide a natural way to integrate system dynamics and GIS with ABM.

Verification and validation (V&V) of simulation models is extremely important.{{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 }} 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.{{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 }} A discrete-event simulation framework approach for the validation of agent-based systems has been proposed.{{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 }} A comprehensive resource on empirical validation of agent-based models can be found here.{{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 }}

As an example of V&V technique, consider VOMAS (virtual overlay multi-agent system),{{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}} 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.{{cite journal |first1=Muaz |last1=Niazi |first2=Qasim |last2=Siddique |first3=Amir |last3=Hussain |first4=Mario |last4=Kolberg |title=Verification & Validation of an Agent-Based Forest Fire Simulation Model |journal=Proceedings of the Agent Directed Simulation Symposium 2010, as Part of the ACM SCS Spring Simulation Multiconference |pages=142–149 |date=April 11–15, 2010 |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}}{{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 Another software engineering method, i.e. Test-Driven Development has been adapted to for agent-based model validation.{{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 }} This approach has another advantage that allows an automatic validation using unit test tools.

{{Div col|colwidth=15em}}

• Agent-based computational economics
• Agent-based model in biology
• Agent-based social simulation (ABSS)
• Artificial society
• Boids
• Comparison of agent-based modeling software
• Complex system
• Complex adaptive system
• Computational sociology
• Conway's Game of Life
• Dynamic network analysis
• Emergence
• Evolutionary algorithm
• Flocking
• Internet bot
• Kinetic exchange models of markets
• Multi-agent system
• Simulated reality
• Social complexity
• Social simulation
• Sociophysics
• Software agent
• Swarming behaviour
• Web-based simulation
• TOTREP

{{Div col end}}

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• {{cite journal |last1=Sallach |first1=David |first2=Charles |last2=Macal |title=The simulation of social agents: an introduction |journal=Social Science Computer Review |volume=19 |issue=33 |pages=245–248 |year=2001 |doi=10.1177/089443930101900301|s2cid=219971440 }}
• {{cite book |last1=Shoham |first1=Yoav |first2=Kevin |last2=Leyton-Brown |url=http://www.masfoundations.org/ |title=Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations |publisher=Cambridge University Press |year=2009 |pages=504 |isbn=978-0-521-89943-7 |access-date=February 25, 2009 |archive-date=May 1, 2011 |archive-url=https://web.archive.org/web/20110501210249/http://www.masfoundations.org/ |url-status=live }}
• {{cite web |url=http://jmvidal.cse.sc.edu/papers/mas.pdf |title=Fundamentals of Multiagent Systems Using NetLogo |last=Vidal |first=Jose |date=2010 |access-date=May 31, 2020 |archive-date=March 31, 2020 |archive-url=https://web.archive.org/web/20200331133542/http://jmvidal.cse.sc.edu/papers/mas.pdf |url-status=live }} Available online.
• {{Cite book |last1=Wilensky |first1=Uri |last2=Rand |first2=William |year=2015 |title=An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo |publisher=MIT Press |isbn=978-0-2627-3189-8 |url=https://mitpress.mit.edu/books/introduction-agent-based-modeling |access-date=May 3, 2020 |archive-date=June 8, 2020 |archive-url=https://web.archive.org/web/20200608111054/https://mitpress.mit.edu/books/introduction-agent-based-modeling |url-status=live }}
• {{cite journal |first1=Hossein |last1=Sabzian |first2=Mohammad Ali |last2=Shafia |title=A review of agent-based modeling (ABM) concepts and some of its main applications in management science |journal=Iranian Journal of Management Studies |volume=11 |issue=4 |pages=659–692 |year=2018 |url=https://ijms.ut.ac.ir/article_68135.html |access-date=April 7, 2021 |archive-date=April 24, 2021 |archive-url=https://web.archive.org/web/20210424011557/https://ijms.ut.ac.ir/article_68135.html |url-status=live }}

{{refend}}

• [https://web.archive.org/web/20110516072744/http://cmol.nbi.dk/models/ Agent-based models of social networks, java applets.]
• [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://abm-ecology.org/ Agent-based models in Ecology – Using computer models as theoretical tools to analyze complex ecological systems]}}
• [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
• [https://web.archive.org/web/20021121141843/http://artificiallife.org/ Artificial Life Framework]
• [https://archive.today/20130128163823/http://inderscience.metapress.com/openurl.asp?genre=article&eissn=1740-2131&volume=4&issue=3&spage=264 Article providing methodology for moving real world human behaviors into a simulation model where agent behaviors are represented]
• [https://web.archive.org/web/20110811053902/http://agent-based-models.com/ Agent-based Modeling Resources], an information hub for modelers, methods, and philosophy for agent-based modeling
• [http://ssrn.com/abstract=2336772 An Agent-Based Model of the Flash Crash of May 6, 2010, with Policy Implications], Tommi A. Vuorenmaa (Valo Research and Trading), Liang Wang (University of Helsinki - Department of Computer Science), October, 2013

• [http://ccl.northwestern.edu/netlogo/models/community/Updated%20Meeting%20Scheduling Multi-agent Meeting Scheduling System Model by Qasim Siddique]
• [http://www.economicswebinstitute.org/essays/fixedvar.htm Multi-firm market simulation by Valentino Piana]
• List of COVID-19 simulation models

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