Computational and Mathematical Organization Theory

, Volume 18, Issue 3, pp 356–373 | Cite as

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

  • Claudio Cioffi-RevillaEmail author
  • Kenneth De Jong
  • Jeffrey K. Bassett
SI: Data to Model


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.


Agent-based modeling Social simulation Evolutionary computation MASON RebeLand model 


  1. Almond GA, Powell GB Jr, Dalton RJ, Strom K (2006) Comparative politics today: a world view. Longman, New York Google Scholar
  2. Axelrod R (1997) Advancing the art of simulation in the social sciences. Complexity 3(2):193–199 CrossRefGoogle Scholar
  3. Cantù-Paz E (2001) Migration policies, selection pressure and parallel evolutionary algorithms. J Heuristics 7(4):311–334 CrossRefGoogle Scholar
  4. Chattoe E (1998) Just how (un)realistic are evolutionary algorithms as representations of social processes? J Artif Soc Soc Simul 1(3). Available online Google Scholar
  5. Chattoe E, Gilbert N (1997) A simulation of adaptation mechanisms in budgetary decision making. In: Conte R, Hegelsmann R, Terna P (eds) Simulating social phenomena. Springer, Berlin Google Scholar
  6. Cioffi-Revilla C (2005) A canonical theory of origins and development of social complexity. J Math Soc 29(April–June) Google Scholar
  7. Cioffi-Revilla C (2010) On the methodology of complex social simulations. J Artif Soc Soc Simul 13(1):7. Available online Google Scholar
  8. Cioffi-Revilla C, Rouleau M (2010) MASON RebeLand: an agent-based model of politics, environment, and insurgency. Int Stud Rev 12(1):31–46 CrossRefGoogle Scholar
  9. De Jong K (2006) Evolutionary computation. MIT Press, Cambridge Google Scholar
  10. De Jong K (2009) Evolutionary computation. Wiley Interdiscip Rev (WIREs) Comput Stat 1(1):52–56 CrossRefGoogle Scholar
  11. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York Google Scholar
  12. Epstein J (2006) Generative social science: studies in agent-based computational modeling. Princeton University Press, Princeton Google Scholar
  13. Gilbert N (2008) Agent-based models. Sage Publishers, Thousand Oaks Google Scholar
  14. Gilbert N, Troitzsch K (2005) Simulation for the social scientist, 2nd edn. Open University Press, Buckingham and Philadelphia Google Scholar
  15. Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18 CrossRefGoogle Scholar
  16. Holland J (1986) Escaping brittleness: the possibilities of general purpose learning algorithms applied to parallel rule-based systems. In: Michalski R, Carbonell J, Mitchell T (eds) Machine learning II. Morgan Kaufmann, San Mateo, pp 593–623 Google Scholar
  17. Kluger J (2008) Simplexity: why simple things become complex (and how complex things can be made simple). Hyperion, New York Google Scholar
  18. Liu J, Dietz T, Carpenter SR, Alberti M, Folke C, Moran E, Pell AN, Deadman P, Kratz T, Lubchenco J, Ostrom E, Ouyang Z, Provencher W, Redman CL, Schneider SH, Taylor WWA (2007) Complexity of coupled human and natural systems. Science 317(5844):1513–1516 CrossRefGoogle Scholar
  19. Lomborg B (1996) Nucleus and shield: the evolution of social structure in the iterated prisoner’s dilemma. Am Sociol Rev 61(2):278–307 CrossRefGoogle Scholar
  20. Nolfi S, Floreano D (2000) Evolutionary robotics: the biology, intelligence, and technology. MIT Press, Cambridge Google Scholar
  21. Ostrom E (2009) A general framework for analyzing sustainability of socio-ecological systems. Science 325:419–422 CrossRefGoogle Scholar
  22. Parisi D, Cecconi F, Cerini A (1995) Kin-directed altruism and attachment behavior in an evolving population of neural networks. In: Gilbert N, Conte R (eds) Artificial societies: the computer simulation of social life. UCL Press, London, pp 238–251 Google Scholar
  23. Rapoport A (1970) N-person game theory: concepts and applications. University of Michigan Press, Ann Arbor Google Scholar
  24. Rennard J-P (ed) (2006) Handbook of research on nature inspired computing for economics and management. Idea Group Inc, Hershey Google Scholar
  25. Reynolds RG (2008) Computing with the social fabric: the evolution of social intelligence within a cultural framework. IEEE Comput Intell 3(1):18–30 CrossRefGoogle Scholar
  26. Reynolds RG, Lazar A, Kim S (2002) The agent-based simulation of the evolution of archaic states. In: Macal C, Sallach D (eds) Proceedings of the agent 2002 conference on social agents: ecology, exchange, and evolution, University of Chicago and Argonne National Laboratory, Chicago Google Scholar
  27. Reynolds RG, Ali M, Jayyoussi T (2008) Mining the social fabric of archaic urban centers with cultural algorithms. Computer 41(1):64–72 CrossRefGoogle Scholar
  28. Ritter HWJ, Webber MM (1973) Dilemmas in a general theory of planning. Policy Sci 4:161–167 Google Scholar
  29. Sarma J (1998) An analysis of decentralized and spatially distributed genetic algorithms. Ph.D. Thesis, George Mason University Google Scholar
  30. Schultz AC, Grefenstette JJ, De Jong KA (1993) Test and evaluation by genetic algorithms. Intell Syst 8(4):9–14 Google Scholar
  31. Simon H (1996) The sciences of the artificial. MIT Press, Cambridge Google Scholar
  32. Takadama K, Cioffi-Revilla C, Deffaunt G (eds) (2010) Simulating interacting agents and social phenomena: the second world congress in social simulation. Springer, Tokyo Google Scholar
  33. Terano T, Sallach D (eds) (2007) Advancing social simulation: the first world congress in social simulation. Springer, Tokyo Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Claudio Cioffi-Revilla
    • 1
    • 2
    Email author
  • Kenneth De Jong
    • 1
    • 2
  • Jeffrey K. Bassett
    • 1
    • 2
  1. 1.Center for Social ComplexityKrasnow Institute for Advanced StudyFairfaxUSA
  2. 2.George Mason UniversityFairfaxUSA

Personalised recommendations