Evolutionary Models for Agent-Based Complex Behavior Modeling

  • Zengchang Qin
  • Yingsai Dong
  • Tao Wan
Part of the Studies in Computational Intelligence book series (SCI, volume 427)


In this chapter, the essentials of genetic algorithm (GA) following the footsteps of Turing are introduced. We introduce the connection between Turing’s early ideas of organized machines and modern evolutionary computation. We mainly discuss the GA applications to adaptive complex system modeling. We study the agent-based market where collective behaviors are regarded as aggregations of individual behaviors. A complex collective behavior can be decomposed into aggregations of several groups agents following different game theoretic strategies. Complexity emerges from the collaboration and competition of these agents. The parameters governing agent behaviors can be optimized by GA to predict future collective behaviors based on history data. GA can also be used in designing market mechanisms by optimizing agent behavior parameters to obtain the most efficient market. Experimental results show the effectiveness of both models. Using evolutionary models may help us to gain some more insights in understanding the complex adaptive systems.


Genetic Algorithm Turing Machine Collective Behavior Complex Adaptive System Transaction Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arthur, W.: Bounded rationality and inductive behavior (the El Farol problem). American Economic Review 84, 406 (1994)Google Scholar
  2. 2.
    Bagley, J.D.: The behavior of adaptive systems which employ genetic and correlation algorithms. Ph.D. dissertation. University of Michigan, Ann Arbor (1967)Google Scholar
  3. 3.
    Challet, D., Zhang, Y.: Emergence of cooperation in an evolutionary game. Physica A 246, 407 (1997)CrossRefGoogle Scholar
  4. 4.
    Cliff, D.: Minimal-intelligence agents for bargaining behaviors in market-based environments. Technical Report HPL-97-91, Hewlett-Packard Laboratories (1997)Google Scholar
  5. 5.
    Cliff, D.: Explorations in evolutionary design of online auction market mechanism. Electronic Commerce Research and Applications 2, 162–175 (2003)CrossRefGoogle Scholar
  6. 6.
    Copeland, B.J. (ed.): The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus the Secrets of Enigma. Oxford Press (2004)Google Scholar
  7. 7.
    Copeland, B.J., Proudfoot, D.: Alan Turings forgotten ideas in computer science. Scientific American, 99-103 (April 1999)Google Scholar
  8. 8.
    Copeland, B.J., Proudfoot, D.: On Alan Turing’s anticipation of connectionism. Synthese 108, 361–377 (1996)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Eberbach, E., Goldin, D., Wegner, P.: Turings ideas and models of computation. In: Teuscher, C. (ed.) Alan Turing: Life and Legacy of a Great Thinker, pp. 159–194. Springer (2004)Google Scholar
  10. 10.
    De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation. University of Michigan, Ann Arbor (1975)Google Scholar
  11. 11.
    Du, Y., Dong, Y., Qin, Z., Wan, T.: Exploring Market Behaviors with Evolutionary Mixed-Games Learning Model. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 244–253. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Gode, D., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy 101(1), 119–137 (1993)CrossRefGoogle Scholar
  13. 13.
    Farmer, J.D., Foley, D.: The economy needs agent-based modelling. Nature 460, 685–686 (2009)CrossRefGoogle Scholar
  14. 14.
    Fisher, R.A.: On the Genetical Theory of Natural Selection. Clarendon Press, Oxford (1930)Google Scholar
  15. 15.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  16. 16.
    Gou, C.: Agents play mix-game. In: Econophysics of Stock and Other Markets, Part II. LNCS, pp. 123–132 (2006)Google Scholar
  17. 17.
    Holland, J.H.: Outline for a logical theory of adaptive systems. Journal of the Association for Computing Machinery 3, 297–314 (1962)CrossRefGoogle Scholar
  18. 18.
    Holland, J.H.: Adaptation in Natural and Artificial Systems, 2nd edn. The MIT Press (1992)Google Scholar
  19. 19.
    Husbands, P., Holland, O., Wheeler, M. (eds.): The Mechanical Mind in History. MIT Press (2008)Google Scholar
  20. 20.
    Johnson, N., Jefferies, P., Hui, P.: Financial Market Complexity. Oxford University Press, Oxford (2003)CrossRefGoogle Scholar
  21. 21.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers (2001)Google Scholar
  22. 22.
    LeBaron, B.: Agent-based computational finance: Suggested readings and early research. Journal of Economic Dynamics and Control 24, 679–702 (2000)zbMATHCrossRefGoogle Scholar
  23. 23.
    Li, G., Ma, Y., Dong, Y., Qin, Z.: Behavior Learning in Minority Games. In: Guttmann, C., Dignum, F., Georgeff, M. (eds.) CARE 2009 / 2010. LNCS, vol. 6066, pp. 125–136. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  24. 24.
    Luke, S.: Essentials of Metaheuristics (2009),
  25. 25.
    Ma, Y., Li, G., Dong, Y., Qin, Z.: Minority Game Data Mining for Stock Market Predictions. In: Cao, L., Bazzan, A.L.C., Gorodetsky, V., Mitkas, P.A., Weiss, G., Yu, P.S. (eds.) ADMI 2010. LNCS, vol. 5980, pp. 178–189. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  26. 26.
    Mantegna, R., Stanley, H.: An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press (1999)Google Scholar
  27. 27.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press (1996)Google Scholar
  28. 28.
    Moro, E.: The Minority Game: an introductory guide. In: Cuerno, K. (ed.) Advances in Condensed Matter and Statistical Physics, Nova Science Publisher, Inc. (2004)Google Scholar
  29. 29.
    Stiglitz, J.E., Driffill, J.: Economics. W. W. Norton & Company, Inc. (2000)Google Scholar
  30. 30.
    Sysi-Aho, M., Chakraborti, A., Kaski, K.: Searching for good strategies in adapative minority games. Physical Review (2004), doi:10.1103/PhysRevE.69.036125Google Scholar
  31. 31.
    Qin, Z.: Evolving Marketplace Designs by Artificial Agents. MSc Dissertation, Computer Science, University of Bristol (2002)Google Scholar
  32. 32.
    Qin, Z.: Market mechanism designs with heterogeneous trading agents. In: Proceedings of Fifth International Conference on Machine Learning and Applications (ICMLA), pp. 69–74 (2006)Google Scholar
  33. 33.
    Qin, Z.: Nave Bayes classification given probability estimation trees. In: The Proceedings of Fifth International Conference on Machine Learning and Applications (ICMLA), pp. 34–39 (2006)Google Scholar
  34. 34.
    Qin, Z., Kovacs, T.: Evolution of realistic auctions. In: Withall, M., Hinde, C. (eds.) Proceedings of the 2004 UK Workshop on Computational Intelligence, Loughborough, UK, pp. 43–50 (2004)Google Scholar
  35. 35.
    Rapoport, A., Chammah, A., Orwant, C.: Prsoner’s Dilemma: A Study in Conflict and Cooperation. University of Michigan Press, Ann Arbor (1965)Google Scholar
  36. 36.
    Smith, V.: An experimental study of competitive market behavior. Journal of Political Economy 70, 111–137 (1962)CrossRefGoogle Scholar
  37. 37.
    Teuscher, C., Sanchez, E.: A revival of Turings forgotten connectionist ideas: exploring unorganized machines. In: Connectionist Models of Learning, Development and Evolution, Perspectives in Neural Computing, pp. 153–162 (2001)Google Scholar
  38. 38.
    Turing, A.: Computing machinery and intelligence. Mind LIX (236), 433–460, doi:doi:10.1093/mind/LIX.236.433Google Scholar
  39. 39.
    Turing, A.: Intelligent machinery. In: Ince, D.C. (ed.) Collected Words of A. M. Turing: Mechanical Intelligence. Elsevier Science (1992)Google Scholar
  40. 40.
    Webster, C., Fleming, W.: Evolved Turing neural networks,
  41. 41.
    Wiesinger, J., Sornette, D., Satinover, J.: Reverse engineering financial markets with majority and minority game using genetic algorithms. Swiss Finance Institute Research Paper No. 10-08 (2010)Google Scholar
  42. 42.
  43. 43.
  44. 44.
  45. 45.
  46. 46.

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical EngineeringBeihang University (BUAA)BeijingChina
  2. 2.School of MedicineBoston UniversityBostonUSA

Personalised recommendations