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Agent‐Based Modelling

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Encyclopedia of Animal Cognition and Behavior

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...

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References

  • 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 

  • Axelrod, R. (1984). The evolution of cooperation. New York: Basic Books.

    Google Scholar 

  • Axelrod, R. (1997a). The dissemination of culture a model with local convergence and global polarization. Journal of Conflict Resolution, 41(2), 203–226.

    Article  Google Scholar 

  • Axelrod, R. M. (1997b). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton: Princeton University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Balci, O. (1998). Verification, validation, and testing (Vol. 10, pp. 335–393). New York: Wiley.

    Google Scholar 

  • Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379–406.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Box, G. E. (1979). Robustness in the strategy of scientific model building. Robustness in Statistics, 1, 201–236.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bundy, A., Jamnik, M., & Fugard, A. (2005). What is a proof? Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 363(1835), 2377–2391.

    Article  Google Scholar 

  • 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 

  • Choi, J.-K., & Bowles, S. (2007). The coevolution of parochial altruism and war. Science, 318(5850), 636–640.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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 

  • 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 

  • Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Washington, DC: Brookings Institution Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gallagher, E. M. (2017). Evolutionary models for the origins of agriculture. Unpublished Doctoral thesis, University College London.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223(4), 120–123.

    Article  Google Scholar 

  • Hamilton, W. D. (1971). Geometry for the selfish herd. Journal of Theoretical Biology, 31, 295–311.

    Article  PubMed  Google Scholar 

  • Hemelrijk, C. K. (2000). Towards the integration of social dominance and spatial structure. Animal Behaviour, 59(5), 1035–1048.

    Article  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • King, G. (1995). Replication, replication. With comments from nineteen authors and a response, ‘A revised proposal, proposal. Political Science & Politics, 28(3), 444–452.

    Article  Google Scholar 

  • Laver, M. J. (2005). Policy and the dynamics of political competition. American Political Science Review, 99(2), 263–281.

    Article  Google Scholar 

  • Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.

    Article  Google Scholar 

  • 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 

  • 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

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Powell, A., Shennan, S., & Thomas, M. G. (2009). Late Pleistocene demography and the appearance of modern human behavior. Science, 324(5932), 1298–1301.

    Article  PubMed  Google Scholar 

  • Preziosi, L. (2003). Cancer modelling and simulation. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006). Agent-based simulation platforms: Review and development recommendations. Simulation, 82(9), 609–623.

    Article  Google Scholar 

  • Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Computer Graphics, 21(4), 25–34.

    Article  Google Scholar 

  • Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wilensky, U. (1999). Netlogo. http://ccl.northwestern.edu/netlogo/. Evanston: Center for Connected Learning and Computer-Based Modeling, Northwestern University.

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Author information

Authors and Affiliations

  1. University College London, London, UK

    Elizabeth M. Gallagher

  2. University of Bath, Bath, UK

    Joanna J. Bryson

  3. Center for Information Technology Policy, Princeton University, Princeton, NJ, USA

    Joanna J. Bryson

Authors
  1. Elizabeth M. Gallagher
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  2. Joanna J. Bryson
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Corresponding author

Correspondence to Elizabeth M. Gallagher .

Editor information

Editors and Affiliations

  1. Oakland University , Rochester, Michigan, USA

    Jennifer Vonk

  2. Department of Psychology, Oakland University Department of Psychology, Rochester, Michigan, USA

    Todd Shackelford

Section Editor information

  1. Animal Welfare Division, American Veterinary Medical Association (AVMA), Schaumburg, IL, USA

    Emily Patterson-Kane

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Gallagher, E.M., Bryson, J.J. (2018). Agent‐Based Modelling. In: Vonk, J., Shackelford, T. (eds) Encyclopedia of Animal Cognition and Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-47829-6_224-1

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  • DOI: https://doi.org/10.1007/978-3-319-47829-6_224-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47829-6

  • Online ISBN: 978-3-319-47829-6

  • eBook Packages: Springer Reference Behavioral Science and PsychologyReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

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