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Can evolutionary algorithms describe learning processes?

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  • Thomas Brenner

    () (Max-Planck-Institute for Research into Economic Systems, Evolutionary Economics Unit, Kahlaische Strasse 10, D-07745 Jena, Germany)

Abstract

Evolutionary algorithms have attracted more and more the attention of economists in recent years. Repeatedly it is claimed that they are an adequate tool to describe learning processes within a population of individuals. The present paper examines this claim. To this end, a learning model is set up that contains the three elements of variation, elimination, and imitation that are claimed to correspond with the processes of mutation, selection, and replication of biological evolution. Subsequently, this model is compared with a formulation of evolutionary algorithms. The comparison reveals that although both processes have a similar structure there are crucial differences between the two dynamics.

Suggested Citation

  • Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
  • Handle: RePEc:spr:joevec:v:8:y:1998:i:3:p:271-283
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    Citations

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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. Sieg, Gernot, 2001. "A political business cycle with boundedly rational agents," European Journal of Political Economy, Elsevier, vol. 17(1), pages 39-52, March.
    3. Ulrich Witt, 2013. "The Future of Evolutionary Economics: Why Modalities Matter," Papers on Economics and Evolution 2013-09, Philipps University Marburg, Department of Geography.
    4. Klos, Tomas B., 1999. "Governance and matching," Research Report 99B41, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    5. Yildizoglu, Murat, 2002. "Competing R&D Strategies in an Evolutionary Industry Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 51-65, February.
    6. Guido Buenstorf, 2012. "Introduction," Chapters,in: Evolution, Organization and Economic Behavior, chapter 1 Edward Elgar Publishing.
    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. Jérome VICENTE (GRES-LEREPS), 2003. "From interaction economics to economic geography : theories and evidences (In French)," Cahiers du GRES (2002-2009) 2003-02, Groupement de Recherches Economiques et Sociales.
    9. repec:dgr:rugsom:99b41 is not listed on IDEAS
    10. Dahl, F.A., 2005. "The lagging anchor model for game learning--a solution to the Crawford puzzle," Journal of Economic Behavior & Organization, Elsevier, vol. 57(3), pages 287-303, July.
    11. Fent, Thomas, 1999. "Using Genetics Based Machine Learning to find Strategies for Product Placement in a dynamic Market," MPRA Paper 2837, University Library of Munich, Germany.
    12. Scholl, Armin & Becker, Christian, 2006. "State-of-the-art exact and heuristic solution procedures for simple assembly line balancing," European Journal of Operational Research, Elsevier, pages 666-693.
    13. Geoffrey Hodgson & Kainan Huang, 2012. "Evolutionary game theory and evolutionary economics: are they different species?," Journal of Evolutionary Economics, Springer, vol. 22(2), pages 345-366, April.

    More about this item

    Keywords

    Social evolution ; Evolutionary algorithms ; Learning;

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations

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