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Learning and behavioral stability An economic interpretation of genetic algorithms

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  • Thomas Riechmann

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

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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Bibliographic 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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Related research

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

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  1. Lucas, Robert E, Jr, 1986. "Adaptive Behavior and Economic Theory," The Journal of Business, University of Chicago Press, vol. 59(4), pages S401-26, October.
  2. Chris Birchenhall & Nikos Kastrinos & Stan Metcalfe, 1997. "Genetic algorithms in evolutionary modelling," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 375-393.
  3. Birchenhall, Chris, 1995. "Modular Technical Change and Genetic Algorithms," Computational Economics, Society for Computational Economics, vol. 8(3), pages 233-53, August.
  4. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  5. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
  6. Blume, Lawrence E. & Easley, David, 1993. "Economic natural selection," Economics Letters, Elsevier, vol. 42(2-3), pages 281-289.
  7. 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.
  8. 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.
  9. Clemens, Christiane & Riechmann, Thomas, 1996. "Evolutionäre Optimierungsverfahren und ihr Einsatz in der ökonomischen Forschung," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover dp-195, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  10. Andreoni James & Miller John H., 1995. "Auctions with Artificial Adaptive Agents," Games and Economic Behavior, Elsevier, vol. 10(1), pages 39-64, July.
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Citations

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Cited by:
  1. Thomas Riechmann, 2000. "A Model of Boundedly Rational Consumer Choice," Econometric Society World Congress 2000 Contributed Papers 0715, Econometric Society.
  2. Marco Casari, 2004. "Can Genetic Algorithms Explain Experimental Anomalies?," Computational Economics, Society for Computational Economics, vol. 24(3), pages 257-275, March.
  3. Paolo Pin, 2006. "Selection matters," Working Papers 138, Department of Applied Mathematics, Università Ca' Foscari Venezia.
  4. Enrico Gerding & David van Bragt & Han La Poutré, 2003. "Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions," Computational Economics, Society for Computational Economics, vol. 22(1), pages 39-63, August.
  5. John Duffy, 2004. "Agent-Based Models and Human Subject Experiments," Computational Economics 0412001, EconWPA.
  6. 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.
  7. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
  8. 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.
  9. Christiane Clemens & Thomas Riechmann, 2006. "Evolutionary Dynamics in Public Good Games," Computational Economics, Society for Computational Economics, vol. 28(4), pages 399-420, November.
  10. Juan Montoro-Pons & Francisco Garcia-Sobrecases, 2003. "A Computational Approach to the Collective Action Problem: Assessment of Alternative Learning Rules," Computational Economics, Society for Computational Economics, vol. 21(1), pages 137-151, February.
  11. 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).
  12. Theo Eicher, 2002. "Search in Research: An Evolutionary Approach to Technical Change and Growth," Computing in Economics and Finance 2002 197, Society for Computational Economics.
  13. Tomas Klos, 1999. "Governance and Matching," Computing in Economics and Finance 1999 341, Society for Computational Economics.
  14. Sándor Karajz, 2007. "Genetic Algorithms as Optimalisation Procedures," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 4(01), pages 37-41.
  15. Riechmann, Thomas, 2001. "Two Notes on Replication in Evolutionary Modelling," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover dp-239, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  16. Juan D. Montoro-Pons, 2000. "Collective Action, Free Riding And Evolution," Computing in Economics and Finance 2000 279, Society for Computational Economics.
  17. Riechmann, Thomas, 2000. "A Model of Boundedly Rational Consumer Choice - An Agent Based Appraoch," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover dp-232, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  18. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO).
  19. Soman, Sethuram & Misgna, Girmay & Kraft, Steven E. & Lant, Chris & Beaulieu, Jeffrey R., 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).

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