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Learning and Behavoiral Stability - An Economic Interpretation of Genetic Algorithms

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  • Riechmann, Thomas

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 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, every particular scheme is analyzed. Then it will be pointed out 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

  • Riechmann, Thomas, 1997. "Learning and Behavoiral Stability - An Economic Interpretation of Genetic Algorithms," Hannover Economic Papers (HEP) dp-209, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-209
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    References listed on IDEAS

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    1. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    2. Clemens, Christiane & Riechmann, Thomas, 1996. "Evolutionäre Optimierungsverfahren und ihr Einsatz in der ökonomischen Forschung," Hannover Economic Papers (HEP) dp-195, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
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    More about this item

    Keywords

    Learning; Computational economics; Genetic algorithms; Markov process; Evolutionary dynamics;
    All these keywords.

    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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