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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.

Suggested Citation

  • Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    • Handle: RePEc:spr:joevec:v:9:y:1999:i:2:p:225-242
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    Cited by:

    1. Sándor Karajz, 2007. "Genetic Algorithms as Optimalisation Procedures," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 4(01), pages 37-41.
    2. Maria Minniti & William Bygrave, 2001. "A Dynamic Model of Entrepreneurial Learning," Entrepreneurship Theory and Practice, , vol. 25(3), pages 5-16, April.
    3. Juan Montoro-Pons & Francisco Garcia-Sobrecases, 2003. "A Computational Approach to the Collective Action Problem: Assessment of Alternative Learning Rules," Computational Economics, Springer;Society for Computational Economics, vol. 21(1), pages 137-151, February.
    4. 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.
    5. Kluger, Brian D. & McBride, Mark E., 2011. "Intraday trading patterns in an intelligent autonomous agent-based stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 79(3), pages 226-245, August.
    6. van Bragt, David & van Kemenade, Cees & la Poutre, Han, 2001. "The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma," Computational Economics, Springer;Society for Computational Economics, vol. 17(2-3), pages 253-263, June.
    7. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM) [Das räumliche agenten-basierte Wettbewerbsmodell SpAbCoM]," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Transition Economies (IAMO).
    8. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    9. 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).
    10. 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).
    11. Theo S Eicher & Klaas vant Veld, 2000. "Search in Research: An Evolutionary Approach to Technical Change and Growth"," Working Papers 0005, University of Washington, Department of Economics.
    12. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
    13. Thomas Riechman, 2000. "A Model Of Boundedly Rational Consumer Choice," Computing in Economics and Finance 2000 321, Society for Computational Economics.
    14. Thomas Riechmann, 2006. "Cournot or Walras? Long-Run Results in Oligopoly Games," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 162(4), pages 702-720, December.
    15. Marco Casari, 2004. "Can Genetic Algorithms Explain Experimental Anomalies?," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 257-275, March.
    16. Paolo Pin, 2006. "Selection matters," Working Papers 138, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    17. Christiane Clemens & Thomas Riechmann, 2006. "Evolutionary Dynamics in Public Good Games," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 399-420, November.
    18. Tomas Klos, 1999. "Governance and Matching," Computing in Economics and Finance 1999 341, Society for Computational Economics.
    19. Riechmann, Thomas, 2001. "Two Notes on Replication in Evolutionary Modelling," Hannover Economic Papers (HEP) dp-239, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    20. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
    21. Juan D. Montoro-Pons, 2000. "Collective Action, Free Riding And Evolution," Computing in Economics and Finance 2000 279, Society for Computational Economics.
    22. Graubner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 109915, Institute of Agricultural Development in Transition Economies (IAMO).
    23. Enrico Gerding & David van Bragt & Han La Poutré, 2003. "Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions," Computational Economics, Springer;Society for Computational Economics, vol. 22(1), pages 39-63, August.
    24. Riechmann, Thomas, 2000. "A Model of Boundedly Rational Consumer Choice - An Agent Based Appraoch," Hannover Economic Papers (HEP) dp-232, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    25. repec:dgr:rugsom:99b41 is not listed on IDEAS

    More about this item

    Keywords

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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