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Evolutionary computation and agent-based modeling: biologically-inspired approaches for understanding complex social systems

Author

Listed:
  • Claudio Cioffi-Revilla

    (Krasnow Institute for Advanced Study
    George Mason University)

  • Kenneth Jong

    (Krasnow Institute for Advanced Study
    George Mason University)

  • Jeffrey K. Bassett

    (Krasnow Institute for Advanced Study
    George Mason University)

Abstract

Computational social science in general, and social agent-based modeling (ABM) simulation in particular, are challenged by modeling and analyzing complex adaptive social systems with emergent properties that are hard to understand in terms of components, even when the organization of component agents is know. Evolutionary computation (EC) is a mature field that provides a bio-inspired approach and a suite of techniques that are applicable to and provide new insights on complex adaptive social systems. This paper demonstrates a combined EC-ABM approach illustrated through the RebeLand model of a simple but complete polity system. Results highlight tax rates and frequency of public issue that stress society as significant features in phase transitions between stable and unstable governance regimes. These initial results suggest further applications of EC to ABM in terms of multi-population models with heterogeneous agents, multi-objective optimization, dynamic environments, and evolving executable objects for modeling social change.

Suggested Citation

  • Claudio Cioffi-Revilla & Kenneth Jong & Jeffrey K. Bassett, 2012. "Evolutionary computation and agent-based modeling: biologically-inspired approaches for understanding complex social systems," Computational and Mathematical Organization Theory, Springer, vol. 18(3), pages 356-373, September.
    • Handle: RePEc:spr:comaot:v:18:y:2012:i:3:d:10.1007_s10588-012-9129-7
    DOI: 10.1007/s10588-012-9129-7
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    References listed on IDEAS

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    1. Edmund Chattoe-Brown, 1998. "Just How (Un)realistic Are Evolutionary Algorithms As Representations of Social Processes?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(3), pages 1-2.
    2. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, December.
    3. Robert Axelrod, 1997. "Advancing the Art of Simulation in the Social Sciences," Working Papers 97-05-048, Santa Fe Institute.
    4. Claudio Cioffi-Revilla, 2010. "A Methodology for Complex Social Simulations," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-7.
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    Cited by:

    1. Davide Secchi & Raffaello Seri, 2017. "Controlling for false negatives in agent-based models: a review of power analysis in organizational research," Computational and Mathematical Organization Theory, Springer, vol. 23(1), pages 94-121, March.

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