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Learning and behavioral stability An economic interpretation of genetic algorithms

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Author Info
Thomas Riechmann () (Universit, t Hannover, FB Wirtschaftswissenschaften, K, nigsworther Platz 1, D-30167 Hannover, Germany)

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Abstract

This article tries to connect two separate strands of literature concerning genetic algorithms. On the one hand, extensive research took place in mathematics and closely related sciences in order to find out more about the properties of genetic algorithms as stochastic processes. On the other hand, recent economic literature uses genetic algorithms as a metaphor for social learning. This paper will face the question of what an economist can learn from the mathematical branch of research, especially concerning the convergence and stability properties of the genetic algorithm.

It is shown that genetic algorithm learning is a compound of three different learning schemes. First, each particular scheme is analyzed. Then it is shown that it is the combination of the three schemes that gives genetic algorithm learning its special flair: A kind of stability somewhere in between asymptotic convergence and explosion.

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Publisher Info
Article provided by Springer in its journal Journal of Evolutionary Economics.

Volume (Year): 9 (1999)
Issue (Month): 2 ()
Pages: 225-242
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Handle: RePEc:spr:joevec:v:9:y:1999:i:2:p:225-242

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Keywords: Learning ; Computational economics ; Genetic algorithms ; Markov process ; Evolutionary dynamics;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Bullard, James & Duffy, John, 1998. "A model of learning and emulation with artificial adaptive agents," Journal of Economic Dynamics and Control, Elsevier, vol. 22(2), pages 179-207, February. [Downloadable!] (restricted)
    Other versions:
  2. Andreoni James & Miller John H., 1995. "Auctions with Artificial Adaptive Agents," Games and Economic Behavior, Elsevier, vol. 10(1), pages 39-64, July. [Downloadable!] (restricted)
  3. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-41, June. [Downloadable!] (restricted)
  4. Blume, Lawrence E. & Easley, David, 1993. "Economic natural selection," Economics Letters, Elsevier, vol. 42(2-3), pages 281-289. [Downloadable!] (restricted)
  5. Clemens, Christiane & Riechmann, Thomas, 1996. "Evolutionäre Optimierungsverfahren und ihr Einsatz in der ökonomischen Forschung," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Universität Hannover dp-195, Universität Hannover, Wirtschaftswissenschaftliche Fakultät. [Downloadable!]
  6. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January. [Downloadable!] (restricted)
  7. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June. [Downloadable!] (restricted)
  8. Chris Birchenhall & Nikos Kastrinos & Stan Metcalfe, 1997. "Genetic algorithms in evolutionary modelling," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 375-393. [Downloadable!] (restricted)
  9. Birchenhall, Chris, 1995. "Modular Technical Change and Genetic Algorithms," Computational Economics, Springer, vol. 8(3), pages 233-53, August.
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Paolo Pin, 2006. "Selection matters," Working Papers 138, Department of Applied Mathematics, University of Venice. [Downloadable!]
  2. David van Bragt & Cees van Kemenade & Han La Poutre, 1999. "The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma," Computing in Economics and Finance 1999 344, Society for Computational Economics.
  3. Christiane Clemens & Thomas Riechmann, 2006. "Evolutionary Dynamics in Public Good Games," Computational Economics, Springer, vol. 28(4), pages 399-420, November. [Downloadable!] (restricted)
  4. Juan Montoro-Pons & Francisco Garcia-Sobrecases, 2003. "A Computational Approach to the Collective Action Problem: Assessment of Alternative Learning Rules," Computational Economics, Springer, vol. 21(1), pages 137-151, February. [Downloadable!] (restricted)
  5. Soman, Sethuram & Misgna, Girmay & Kraft, Steven & Lant, Chris & Beaulieu, Jeff, 2008. "An Agent-Based Model of Multifunctional Agricultural Landscape Using Genetic Algorithms," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6142, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association). [Downloadable!]
  6. David van Bragt & Han La Poutré, 2001. "Evolving Automata Play the Alternating-Offers Game," CeNDEF Workshop Papers, January 2001 2B.3, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  7. Juan D. Montoro-Pons, 2000. "Collective Action, Free Riding And Evolution," Computing in Economics and Finance 2000 279, Society for Computational Economics. [Downloadable!]
  8. Riechmann, Thomas, 2001. "Two Notes on Replication in Evolutionary Modelling," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Universität Hannover dp-239, Universität Hannover, Wirtschaftswissenschaftliche Fakultät. [Downloadable!]
  9. John Duffy, 2004. "Agent-Based Models and Human Subject Experiments," Computational Economics 0412001, EconWPA. [Downloadable!]
    Other versions:
  10. Theo S Eicher & Klaas vant Veld, 2000. "Search in Research: An Evolutionary Approach to Technical Change and Growth"," Discussion Papers in Economics at the University of Washington 0005, Department of Economics at the University of Washington. [Downloadable!]
    Other versions:
  11. Riechmann, Thomas, 2000. "A Model of Boundedly Rational Consumer Choice - An Agent Based Appraoch," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Universität Hannover dp-232, Universität Hannover, Wirtschaftswissenschaftliche Fakultät. [Downloadable!]
  12. Tomas Klos, 1999. "Governance and Matching," Computing in Economics and Finance 1999 341, Society for Computational Economics. [Downloadable!]
    Other versions:
  13. Riechmann, Thomas, 2002. "Cournot or Walras? Agent Based Learning, Rationality, and Long Run Results in Oligopoly Games," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Universität Hannover dp-261, Universität Hannover, Wirtschaftswissenschaftliche Fakultät. [Downloadable!]
  14. Marco Casari, 2002. "Can genetic algorithms explain experimental anomalies? An application to common property resources," UFAE and IAE Working Papers 542.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC). [Downloadable!]
  15. Enrico Gerding & David van Bragt & Han La Poutré, 2003. "Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions," Computational Economics, Springer, vol. 22(1), pages 39-63, August. [Downloadable!] (restricted)
  16. Thomas Riechmann, 2000. "A Model of Boundedly Rational Consumer Choice," Econometric Society World Congress 2000 Contributed Papers 0715, Econometric Society. [Downloadable!]
    Other versions:
  17. Marco Casari, 2004. "Can Genetic Algorithms Explain Experimental Anomalies?," Computational Economics, Springer, vol. 24(3), pages 257-275, March. [Downloadable!] (restricted)
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