Agent-based model
An agent-based model (ABM) is a type of computer simulation that shows how individual things act and interact to see how they affect the whole system.[1] It can be thought of as a video game where lots of characters are moving around and doing their own thing. Each character follows a few simple rules like “walk toward food,” “avoid danger,” or “follow the leader.” These characters are called agents, and they can be anything depending on what you are studying: people, animals, cars, robots, or even tiny cells in the body.[2] An agent-based model (ABM) is like running a giant computer simulation of these agents to see what happens when they all interact. Even though each one is just following simple rules, together they can create surprising and complicated patterns. This is called emergence, which means big patterns form from lots of small actions without anyone planning it.[3]
Scientists use ABMs to study all kinds of real-world situations. In nature, they can model how animals migrate, fight for resources, or react to changes in the environment, like guessing how a fish population might recover if fishing rules change.[4] In economics, ABMs can show how shoppers, businesses, and banks interact, sometimes leading to things like financial bubbles or inequality.[5] In epidemiology, they can help predict how diseases spread through a community, factoring in things like different social circles and immunity levels.[6] Even less obvious uses exist, like simulating traffic to prevent jams,[7] studying how people move in a crowd to design safer buildings,[8] or figuring out how ancient civilizations traded and grew.[9]
One of the most interesting things about ABMs is that not every agent has to be the same.[2] A forest fire simulation, for example, might have some trees that are dry and catch fire easily, while others are wet and harder to burn. The fire can spread tree-to-tree, while weather affects it on a larger scale. Sometimes these models include feedback loops, where agents change the system, and then the changed system affects the agents’ next actions.[10] This can cause unexpected events or “tipping points” where everything changes suddenly.[11]
Building an ABM takes a lot of careful planning. You have to decide what your agents are, what rules they follow, how they interact with each other and their surroundings, and how time works in the simulation.[12] Scientists often use special computer programs like NetLogo, Repast, or AnyLogic to run these models and watch what happens.[13] ABMs can be powerful tools for testing “what if” questions and spotting patterns that might be hard to see in the real world. Even though they can be tricky to set up and need a lot of data, they help researchers understand how simple actions can lead to complex, unpredictable results.[14]
References
[change | change source]- ↑ Bonabeau, Eric (2002-05-14). "Agent-based modeling: Methods and techniques for simulating human systems". Proceedings of the National Academy of Sciences. 99 (suppl_3): 7280–7287. doi:10.1073/pnas.082080899. PMC 128598. PMID 12011407.
- ↑ 2.0 2.1 Macal, C M; North, M J (2010-09-01). "Tutorial on agent-based modelling and simulation". Journal of Simulation. 4 (3): 151–162. doi:10.1057/jos.2010.3. ISSN 1747-7778.
- ↑ Epstein, Joshua M. (1999). "Agent-based computational models and generative social science". Complexity. 4 (5): 41–60. doi:10.1002/(SICI)1099-0526(199905/06)4:5<41::AID-CPLX9>3.0.CO;2-F. ISSN 1099-0526.
- ↑ Grimm, Volker; Railsback, Steven F. (2005). Individual-based modeling and ecology. Princeton series in theoretical and computational biology. Princeton: Princeton University Press. ISBN 978-0-691-09665-0.
- ↑ Tesfatsion, Leigh; Judd, Kenneth L., eds. (2006). Handbook of computational economics: volume 2, Agent-based computational economics. Handbooks in economics. Amsterdam New York: Elsevier. ISBN 978-0-444-51253-6.
- ↑ Eubank, Stephen; Guclu, Hasan; Anil Kumar, V. S.; Marathe, Madhav V.; Srinivasan, Aravind; Toroczkai, Zoltán; Wang, Nan (2004). "Modelling disease outbreaks in realistic urban social networks". Nature. 429 (6988): 180–184. doi:10.1038/nature02541. ISSN 1476-4687.
- ↑ Nagel, Kai; Schreckenberg, Michael (1992-12-01). "A cellular automaton model for freeway traffic". Journal de Physique I. 2 (12): 2221–2229. doi:10.1051/jp1:1992277. ISSN 1155-4304.
- ↑ Helbing, Dirk; Farkas, Illés; Vicsek, Tamás (2000). "Simulating dynamical features of escape panic". Nature. 407 (6803): 487–490. doi:10.1038/35035023. ISSN 1476-4687.
- ↑ Axtell, Robert L.; Epstein, Joshua M.; Dean, Jeffrey S.; Gumerman, George J.; Swedlund, Alan C.; Harburger, Jason; Chakravarty, Shubha; Hammond, Ross; Parker, Jon; Parker, Miles (2002-05-14). "Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley". Proceedings of the National Academy of Sciences. 99 (suppl_3): 7275–7279. doi:10.1073/pnas.092080799. PMC 128597. PMID 12011406.
- ↑ Levin, Simon A. (1998-09-01). "Ecosystems and the Biosphere as Complex Adaptive Systems". Ecosystems. 1 (5): 431–436. doi:10.1007/s100219900037. ISSN 1432-9840.
- ↑ Scheffer, Marten; Carpenter, Steve; Foley, Jonathan A.; Folke, Carl; Walker, Brian (2001). "Catastrophic shifts in ecosystems". Nature. 413 (6856): 591–596. doi:10.1038/35098000. ISSN 1476-4687.
- ↑ Railsback, Steven F.; Grimm, Volker (2019). Agent-based and individual-based modeling: a practical introduction (2nd ed.). Princeton Oxford: Princeton University Press. ISBN 978-0-691-19082-2.
- ↑ "NetLogo Home Page". ccl.northwestern.edu. Retrieved 2025-08-15.
- ↑ Epstein, Joshua M. (2011). Generative social science: studies in agent-based computational modeling. Princeton studies in complexity. Princeton and Oxford: Princeton University Press. ISBN 978-1-4008-4287-2.
