Encyclopedia of Animal Cognition and Behavior

Living Edition
| Editors: Jennifer Vonk, Todd Shackelford

Agent‐Based Modelling

  • Elizabeth M. GallagherEmail author
  • Joanna J. Bryson
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-47829-6_224-1

Synonyms

Individual-based modelling; Individual-orientated modelling; Multi-agent systems (has other meanings, occasionally used as synonym.)

Definition

Agent-based models are a type of model based on computer simulation, where the behavior of a system is determined by the activities of autonomous individuals and their interaction with and through an environment.

Introduction

Agent-based modelling (ABM) is a research method for understanding the collective effects of individual action selection. More generally, ABM allows the examination of macrolevel effects from microlevel behavior. Science requires understanding how an observed characteristic of a system (e.g., a solid) can be accounted for by its components (e.g., molecules). In ABM, we build models of both the components and the environment in which they exist, and then observe whether the over-all system-level behavior of the model matches that of the target (or subject) system. Constructing agent-based models (ABMs) can be seen...

This is a preview of subscription content, log in to check access.

References

  1. Andersen, H., & Hepburn, B. (2016). Scientific method. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Stanford: Metaphysics Research Lab, Stanford University. Summer 2016 edition.Google Scholar
  2. Axelrod, R. (1984). The evolution of cooperation. New York: Basic Books.Google Scholar
  3. Axelrod, R. (1997a). The dissemination of culture a model with local convergence and global polarization. Journal of Conflict Resolution, 41(2), 203–226.CrossRefGoogle Scholar
  4. Axelrod, R. M. (1997b). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton: Princeton University Press.Google Scholar
  5. Axtell, R., Axelrod, R., Epstein, J. M., & Cohen, M. D. (1996). Aligning simulation models: A case study and results. Computational & Mathematical Organization Theory, 1(2), 123–141.CrossRefGoogle Scholar
  6. Balci, O. (1998). Verification, validation, and testing (Vol. 10, pp. 335–393). New York: Wiley.Google Scholar
  7. Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379–406.CrossRefGoogle Scholar
  8. Berger, T. (2001). Agent-based spatial models applied to agriculture: A simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics, 25(2–3), 245–260.CrossRefGoogle Scholar
  9. Box, G. E. (1979). Robustness in the strategy of scientific model building. Robustness in Statistics, 1, 201–236.CrossRefGoogle Scholar
  10. Brown, D. G., Page, S., Riolo, R., Zellner, M., & Rand, W. (2005). Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science, 19(2), 153–174.CrossRefGoogle Scholar
  11. Bryson, J. J., Ando, Y., & Lehmann, H. (2007). Agent-based modelling as scientific method: A case study analysing primate social behaviour. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 362(1485), 1685–1699.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Bundy, A., Jamnik, M., & Fugard, A. (2005). What is a proof? Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 363(1835), 2377–2391.CrossRefGoogle Scholar
  13. Čače, I., & Bryson, J. J. (2007). Agent based modelling of communication costs: Why information can be free. In Emergence of communication and language (pp. 305–321). London: Springer.Google Scholar
  14. Choi, J.-K., & Bowles, S. (2007). The coevolution of parochial altruism and war. Science, 318(5850), 636–640.CrossRefPubMedGoogle Scholar
  15. Couzin, I. D., Ioannou, C. C., Demirel, G., Gross, T., Torney, C. J., Hart-nett, A., Conradt, L., Levin, S. A., & Leonard, N. E. (2011). Uninformed individuals promote democratic consensus in animal groups. Science, 334(6062), 1578–1580.CrossRefPubMedGoogle Scholar
  16. Dean, J. S., Gumerman, G. J., Epstein, J. M., Axtell, R. L., Swedlund, A. C., Parker, M. T., & McCarroll, S. (2000). Understanding Anasazi culture change through agent-based modeling. In Dynamics in human and primate societies: Agent-based modeling of social and spatial processes (pp. 179–205). New York: Oxford University Press.Google Scholar
  17. Edmonds, B., & Bryson, J. J. (2004). The insufficiency of formal design methods – The necessity of an experimental approach for the understanding and control of complex mas. In N. R. Jenning, C. Sierra, L. Sonenberg, & M. Tambe (Eds.), The 3rd international joint conference on autonomous agents and multi agent systems (AAMAS 2004) (pp. 936–943). ACM Press, Columbia University, New York City.Google Scholar
  18. Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Washington, DC: Brookings Institution Press.Google Scholar
  19. Folcik, V. A., An, G. C., & Orosz, C. G. (2007). The basic immune simulator: An agent-based model to study the interactions between innate and adaptive immunity. Theoretical Biology and Medical Modelling, 4(1), 1.CrossRefGoogle Scholar
  20. Gallagher, E. M. (2017). Evolutionary models for the origins of agriculture. Unpublished Doctoral thesis, University College London.Google Scholar
  21. Gallagher, E. M., Shennan, S. J., & Thomas, M. G. (2015). Transition to farming more likely for small, conservative groups with property rights, but increased productivity is not essential. Proceedings of the National Academy of Sciences, 112(46), 14218–14223.CrossRefGoogle Scholar
  22. Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223(4), 120–123.CrossRefGoogle Scholar
  23. Hamilton, W. D. (1971). Geometry for the selfish herd. Journal of Theoretical Biology, 31, 295–311.CrossRefPubMedGoogle Scholar
  24. Hemelrijk, C. K. (2000). Towards the integration of social dominance and spatial structure. Animal Behaviour, 59(5), 1035–1048.CrossRefPubMedGoogle Scholar
  25. Hogeweg, P., & Hesper, B. (1979). Heterarchical selfstructuring simulation systems: Concepts and applications in biology. In B. P. Zeigler, M. S. Ezas, G. J. Klir, & T. I. Ören (Eds.), Methodologies in systems modelling and simulation (pp. 221–231). North-Holland Publishing Co, North-Holland, Amsterdam.Google Scholar
  26. Hogeweg, P., & Hesper, B. (1983). The ontogeny of the interaction structure in bumble bee colonies: A MIRROR model. Behavioral Ecology and Sociobiology, 12(4), 271–283.CrossRefGoogle Scholar
  27. Kennedy, R., Xiang, X., Madey, G., & Cosimano, T. (2005). Verification and validation of scientific and economic models. In M. North, D. Sallach, & C. Macal (Eds.), Proceedings of the Agent 2005: Generative Social Processes, Models, and Mechanisms (pp. 177–192). Chicago: Argonne National Laboratory.Google Scholar
  28. King, G. (1995). Replication, replication. With comments from nineteen authors and a response, ‘A revised proposal, proposal. Political Science & Politics, 28(3), 444–452.CrossRefGoogle Scholar
  29. Laver, M. J. (2005). Policy and the dynamics of political competition. American Political Science Review, 99(2), 263–281.CrossRefGoogle Scholar
  30. Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.CrossRefGoogle Scholar
  31. Mock, K., & Testa, J. (2007). An agent-based model of predator-prey relationships between transient killer whales and other marine mammals. Anchorage: University of Alaska Anchorage. Tech. Rep.Google Scholar
  32. Myung, J., Forster, M. R., & Browne, M. W. (2000). Special issue on model selection. Journal of Mathematical Psychology, 44(1), 1–2. http://www.sciencedirect.com/science/article/pii/S0022249699912737?via%3Dihub
  33. North, M. J., Collier, N. T., Ozik, J., Tatara, E. R., Macal, C. M., Bragen, M., & Sydelko, P. (2013). Complex adaptive systems modeling with repast simphony. Complex Adaptive Systems Modeling, 1(1), 3.CrossRefGoogle Scholar
  34. Pan, X., Han, C. S., Dauber, K., & Law, K. H. (2007). A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. Ai & Society, 22(2), 113–132.CrossRefGoogle Scholar
  35. Powell, A., Shennan, S., & Thomas, M. G. (2009). Late Pleistocene demography and the appearance of modern human behavior. Science, 324(5932), 1298–1301.CrossRefPubMedGoogle Scholar
  36. Preziosi, L. (2003). Cancer modelling and simulation. Boca Raton: CRC Press.CrossRefGoogle Scholar
  37. Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006). Agent-based simulation platforms: Review and development recommendations. Simulation, 82(9), 609–623.CrossRefGoogle Scholar
  38. Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Computer Graphics, 21(4), 25–34.CrossRefGoogle Scholar
  39. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.CrossRefGoogle Scholar
  40. Whitehouse, H., Kahn, K., Hochberg, M. E., & Bryson, J. J. (2012). The role for simulations in theory construction for the social sciences: Case studies concerning divergent modes of religiosity. Religion, Brain & Behavior, 2(3), 182–224.CrossRefGoogle Scholar
  41. Wilensky, U. (1999). Netlogo. http://ccl.northwestern.edu/netlogo/. Evanston: Center for Connected Learning and Computer-Based Modeling, Northwestern University.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University College LondonLondonUK
  2. 2.University of BathBathUK
  3. 3.Center for Information Technology PolicyPrinceton UniversityPrincetonUSA

Section editors and affiliations

  • Emily Patterson-Kane
    • 1
  1. 1.Animal Welfare DivisionAmerican Veterinary Medical Association (AVMA)SchaumburgUSA