Survey of evolutionary computation methods in social agent-based modeling studies

Abstract

Agent-based modeling is a well-established discipline today with a rich and vibrant research community. The field of evolutionary computation (EC) is also well recognized within the larger family of computational sciences. In the past decades many agent-based modeling studies of social systems have used EC methods to tackle various research questions. Despite the relative frequency of such efforts, no systematic review of the use of evolutionary computation in agent-based modeling has been put forth. Here, we review a number of prominent agent-based models of social systems that employ evolutionary algorithms as a method. We comment on some theoretical considerations, the state of current practice, and suggest some best practices for future work.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Angeline, P. J. (2000). Parse trees. In T. Bäck, D. Fogel, & T. Michalewicz (Eds.), Evolutionary computation 1: basic algorithms and operators. Bristol: Institute of Physics Publishing.

    Google Scholar 

  2. 2.

    Arthur, B. W. (1994). Inductive reasoning and bounded rationality. American Economic Review, 84, 406–411.

    Google Scholar 

  3. 3.

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

    Google Scholar 

  4. 4.

    Axelrod, R. (1986). An evolutionary approach to norms. The American Political Science Review, 80(4), 1095–1111.

    Article  Google Scholar 

  5. 5.

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

    Article  Google Scholar 

  6. 6.

    Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science, 211, 1390–96.

    Article  Google Scholar 

  7. 7.

    Bäck, T. (1996). Evolutionary algorithms in theory and practice. New York, New York: Oxford University Press.

    Google Scholar 

  8. 8.

    Bäck, T., Fogel, D., & Michalewicz, T. (2000). Evolutionary computation 1: basic algorithms and operators. Bristol: Institute of Physics Publishing.

    Google Scholar 

  9. 9.

    Bausch, W. (2015). The geography of ethnocentrism. Journal of Conflict Resolution, 59(3), 510–527.

    Article  Google Scholar 

  10. 10.

    Bianchi, F., & Squazzoni, F. (2015). Agent-based models in sociology. Wiley Interdisciplinary Reviews: Computational Statistics, 7, 284306.

    Article  Google Scholar 

  11. 11.

    Bowles, S., & Gintis, H. (2004). The evolution of strong reciprocity: cooperation in heterogeneous populations. Theoretical Population Biology, 65, 17–28.

    Article  Google Scholar 

  12. 12.

    Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionary process. Chicago: University of Chicago Press.

    Google Scholar 

  13. 13.

    Castelfranchi, C., Conte, R., & Paolucci, M. (1998). Normative reputation and the costs of compliance. Journal of Artificial Societies and Social Simulation, 1(3), 3.

    Google Scholar 

  14. 14.

    Cegielski, W. H., & Rogers, J. D. (2016). Rethinking the role of agent-based modeling in archaeology. Journal of Anthropological Archaeology, 41, 283–298.

    Article  Google Scholar 

  15. 15.

    Chattoe, E. (1998). Just how (un)realistic are evolutionary algorithms as representations of social processes? Journal of Artificial Societies and Social Simulation, 1(3), 2.

    Google Scholar 

  16. 16.

    Chiang, Y. S. (2013). Cooperation could evolve in complex networks when activated conditionally on network characteristics. Journal of Artificial Societies and Social Simulation, 16(2), 6.

    Article  Google Scholar 

  17. 17.

    Cioffi-Revilla, C. (2017). Introduction to computational social science. New York: Springer.

    Google Scholar 

  18. 18.

    Cioffi-Revilla, C., Luke, S., Parker, D., Rogers, J. D., Fitzhugh, W. W., Honeychurch, W., et al. (2006). Agent-based modeling simulation of social adaptation and long-term change in inner Asia. In S. Takahashi, D. Sallach, & J. Rouchier (Eds.), Advancing social simulation: the first world congress (pp. 189–200). New York: Springer.

    Google Scholar 

  19. 19.

    Dawkins, R. (1976). The selfish gene. Oxford: Oxford University Press.

    Google Scholar 

  20. 20.

    De Jong, K. A. (2005). Evolutionary computation: a unified approach. Cambridge: MIT Press.

    Google Scholar 

  21. 21.

    Deb, K. (2000). Introduction to selection. In T. Bäck, D. Fogel, & T. Michalewicz (Eds.), Evolutionary computation 1: basic algorithms and operators. Bristol: Institute of Physics Publishing.

    Google Scholar 

  22. 22.

    Deffuant, G., & Huet, S. F. A. (2005). An individual-based model of innovation diffusion mixing social value and individual benefit. American Journal of Sociology, 110, 10411069.

    Article  Google Scholar 

  23. 23.

    Dosi, G., Marengo, L., Bassanini, A., & Valente, M. (1999). Norms as emergent properties of adaptive learning: the case of economic routines. Journal of Evolutionary Economics, 9, 5–26.

    Article  Google Scholar 

  24. 24.

    Edmonds, B.: Modelling bounded rationality using evolutionary techniques. Discussion paper, Centre for Policy Modelling, Metropolitan University of Manchester. http://www.cfpm.org/papers/mbruet.pdf.

  25. 25.

    Eiben, A. E., & Smith, J. E. (2007). Introduction to evolutionary computing. New York: Springer.

    Google Scholar 

  26. 26.

    Epstein, J., & Axtell, R. (1996). Growing artificial societies. Cambridge: MIT Press.

    Google Scholar 

  27. 27.

    Fogel, D. (1995). Evolutionary computation: toward a new philosophy of machine intelligence. Piscataway: IEEE Press.

    Google Scholar 

  28. 28.

    Fogel, D. (2000). Real-valued vectors. In T. Bäck, D. Fogel, & T. Michalewicz (Eds.), Evolutionary computation 1: basic algorithms and operators. Bristol: Institute of Physics Publishing.

    Google Scholar 

  29. 29.

    Fogel, L., Owens, A., & Walsh, M. (1966). Artificial intelligence through simulated evolution. New York: Wiley.

    Google Scholar 

  30. 30.

    Galan, J. M., & Izquierdo, L. R. (2005). Appearances can be deceiving: lessons learned re-implementing Axelrod’s ‘Evolutionary Approach to Norms’. Journal of Artificial Societies and Social Simulation, 8(3), 2.

    Google Scholar 

  31. 31.

    Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Maidenhead: Open University Press.

    Google Scholar 

  32. 32.

    Goldberg, D. (1989). Genetic algorithms in search, optimization, and machine learning. New York: Addison-Wesley.

    Google Scholar 

  33. 33.

    Gould, S. J., & Lewontin, C. R. (1971). The spandrels of San Marco and the Panglossian pradigm: a critique of the adaptationist programme. Proceedings of the Royal Society of London. Series B, Biological Sciences, 205(1161), 581–598.

    Article  Google Scholar 

  34. 34.

    Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., et al. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modeling, 198(1), 115–126.

    Article  Google Scholar 

  35. 35.

    Hales, D. (2000). Cooperation without space or memory: tags, groups and the prisoner’s dilemma. In S. Moss & P. Davidsson (Eds.), Multi-agent-based simulation, lecture notes in artificial intelligence. Berlin: Springer.

    Google Scholar 

  36. 36.

    Hall, B. K., & Hallgrimsson, B. (2008). Strickberger’s evolution. New York: Jones and Bartlett Publishers.

    Google Scholar 

  37. 37.

    Hammond, R. A., & Axelrod, R. (2006). The evolution of ethnocentrism. Journal of Conflict Resolution, 50, 926–936.

    Article  Google Scholar 

  38. 38.

    Hancock, P. J. B. (2000). A comparison of selection mechanisms. In T. Bäck, D. Fogel, & T. Michalewicz (Eds.), Evolutionary computation 1: basic algorithms and operators. Bristol: Institute of Physics Publishing.

    Google Scholar 

  39. 39.

    Henrich, J. (2004). Demography and cultural evolution: how adaptive cultural processes can produce maladaptive lossesthe Tasmanian case. American Antiquity, 69(2), 197–214.

    Article  Google Scholar 

  40. 40.

    Hobbes, T. (1651). Leviathan. Folkestone: Renaissance Books.

    Google Scholar 

  41. 41.

    Holland, J. (1962). Outline for a logical theory of adaptive systems. JACM, 9, 297–314.

  42. 42.

    Holland, J. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.

    Google Scholar 

  43. 43.

    Holland, J. (1992). Adaptation in natural and artificial systems (2nd ed.). Cambridge: MIT Press.

    Google Scholar 

  44. 44.

    Janssen, M. (2005). Evolution of cooperation when feedback to reputation scores is voluntary. Journal of Artificial Societies and Social Simulation, 9(1), 17.

    Google Scholar 

  45. 45.

    Kachel, A. F., Premo, L. S., & Hublin, J. J. (2010). Grandmothering and natural selection. Proceedings of the Royal Society B Biological Sciences, vol. 278 (pp. 384–391).

  46. 46.

    Kertzer, D. I. (1989). Ritual, politics, and power. New Haven: Yale University Press.

    Google Scholar 

  47. 47.

    Klos, T. B. (1999). Decentralized interaction and co-adaptation in the repeated prisoners dilemma. Computational and Mathematical Organization Theory, 5(2), 147165.

    Article  Google Scholar 

  48. 48.

    Kohler, T. A., Cockburn, D., Hooper, P. L., Bocinsky, R. K., & Kobti, Z. (2012). The coevolution of group size and leadership: an agent-based public goods model for prehispanic Pueblo societies. Advances in Complex Systems, 15(1–2), 1–29.

    Google Scholar 

  49. 49.

    Koza, J. (1992). Genetic programming. Cambridge: MIT Press.

    Google Scholar 

  50. 50.

    Lake, M. W. (2001). The use of pedestrian modelling in archaeology, with an example from the study of cultural learning. Environment and Planning B: Planning and Design, 28, 385–403.

    Article  Google Scholar 

  51. 51.

    Lake, M. W. (2014). Trends in archaeological simulation. Journal of Archaeological Method and Theory, 21(2), 2258287.

    Article  Google Scholar 

  52. 52.

    Lake, M. W., & Crema, E. R. (2012). The cultural evolution of adaptive-trait diversity when resources are uncertain and finite. Advances in Complex Systems, 15(1–2), 1–19.

    Google Scholar 

  53. 53.

    Macy, M., & Skvoretz, J. (1998). The evolution of trust and cooperation between strangers: a computational model. American Sociological Review, 63, 638–660.

    Article  Google Scholar 

  54. 54.

    Mattias, K.E., Whitley, L.D.: Transforming the search space with gray coding. In Proceedings of the first IEEE conference on evolutionary computation. IEEE world congress on computational intelligence, vol. 1, pp. 513–518 (1994)

  55. 55.

    Miller, J. H. (1996). The co-evolution of automata in the repeated prisoner’s dilemma. Journal of Economic Behavior and Organization, 29(1), 87–112.

    Article  Google Scholar 

  56. 56.

    Mitchell, M. (1998). An introduction to genetic algorithms. Cambridge: MIT Press.

    Google Scholar 

  57. 57.

    Moravec, H. (1989). Human culture: a genetic takeover underway. In: C. G. Langton (Ed.), Artificial Life: The Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, Los Alamos, New Mexico, September 1987. SFI Studies in the Sciences of Complexity, vol. VI (pp. 167–199). Redwood City, CA: Addison-Wesley.

  58. 58.

    Pepper, J.W., Smuts, B.: The Evolution of Cooperation in an Ecological Context: An Agent-based Model. In: G.G.J. Kohler T. A. (ed.) Dynamics in Human and Primate Societies: Agent-Based Modelling of Social and Spatial Processes, pp. 45–76. Oxford University Press, New York, New York (2000)

  59. 59.

    Perez-Losada, J., & Fort, J. (2011). Spatial dimensions increase the effect of cultural drift. Journal of Archaeological Science, 38(6), 1294–1299.

    Article  Google Scholar 

  60. 60.

    Poundstone, W. (1992). Prisoner’s dilemma: John von Neumann, game theory, and the puzzle of the bomb. Oxford, United Kingdom: Oxford University Press.

    Google Scholar 

  61. 61.

    Powell, A., Shennan, S. J., & Thomas, M. G. (2009). Late pleistocene demography and the appearance of modern human behavior. Science, 324, 12981301.

    Article  Google Scholar 

  62. 62.

    Premo, L. S., & Hublin, J. J. (2009). Culture, population structure, and low genetic diversity in pleistocene hominins. Proceedings of the National Academy of Science, 106(1), 33–37.

    Article  Google Scholar 

  63. 63.

    Rappaport, R. A. (1967). Ritual regulation of environmental relations among a New Guinea people. Ethnology, 6(1), 17–30.

    Article  Google Scholar 

  64. 64.

    Rechenberg, I. (1965). Cybernetic solution path of an experimental problem. Royal Aircraft Establishment, Library Translation 1122, Farnborough.

  65. 65.

    Riolo, R.: The Effects of Tag-Mediated Selection of Partners in Evolving Populations playing the iterated prisoner’s dilemma. Working Paper 97-02-016, Santa Fe Institute, Santa Fe, NM (1997). http://www.cfpm.org/papers/mbruet.pdf

  66. 66.

    Rousseau, J. J. (1762). The Social Contract. Penguin Classics. New York: Penguin Classics.

    Google Scholar 

  67. 67.

    Saam, N. J., & Harrer, A. (1999). Simulating norms, social inequality, and functional change in artificial societies. Journal of Artificial Societies and Social Simulation, 2(1), 2.

    Google Scholar 

  68. 68.

    Santos, F. C., Pacheco, J., & Lenaerts, T. (2006). Cooperation prevails when individuals adjust their social ties. PLoS Computational Biology, 2, e140.

    Article  Google Scholar 

  69. 69.

    Sarma, J., & De Jong, K. (2000). Generation gap methods. In T. Bäck, D. Fogel, & T. Michalewicz (Eds.), Evolutionary computation 1: basic algorithms and operators. Bristol: Institute of Physics Publishing.

    Google Scholar 

  70. 70.

    Saussure, F. (1986). Course in general linguistics. Chicago: Open Court.

    Google Scholar 

  71. 71.

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

    Article  Google Scholar 

  72. 72.

    Simon, H. A. (1996). Sciences of the artificial (3rd ed.). Cambridge: MIT Press.

    Google Scholar 

  73. 73.

    Takahashi, N. (2000). The emergence of generalized exchange. The American Journal of Sociology, 105(4), 1105–1134.

    Article  Google Scholar 

  74. 74.

    Vaesen, K. (2012). Cumulative cultural evolution and demography. PLoS ONE, 7(7), e40989.

    Article  Google Scholar 

  75. 75.

    de Vos, H., Smaniotto, R., & Elsas, D. A. (2001). Reciprocal altruism under conditions of partner selection. Rationality and Society, 13(2), 139–183.

    Article  Google Scholar 

  76. 76.

    Xue, J. Z., Costopoulos, A., & Guichard, F. (2011). Choosing fitness-enhancing innovations can be detrimental under fluctuating environments. PLoS ONE, 6(11), e26770.

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded in part by the Center for Social Complexity at GMU, by the US National Science Foundation, CDI Program, Grant no. IIS-1125171, and by ONR-Minerva Grant no. N00014130054.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Peter Revay.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Revay, P., Cioffi-Revilla, C. Survey of evolutionary computation methods in social agent-based modeling studies. J Comput Soc Sc 1, 115–146 (2018). https://doi.org/10.1007/s42001-017-0003-8

Download citation

Keywords

  • Agent-based modeling
  • Evolutionary computation
  • Evolutionary algorithms
  • Survey