Genetic algorithm learning and the cobweb model

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Abstract

This paper presents the cobweb model in which competitive firms, in a market for a single good, use a genetic algorithm to update their decision rules about next-period production and sales. The results of simulations show that the genetic algorithm converges to the rational expectations equilibrium for a wider range of parameter values than other algorithms frequently studied within the context of the cobweb model. Price and quantity patterns generated by the genetic algorithm are also compared to the data of experimental cobweb economies. It is shown that the algorithm can capture several features of the experimental behavior of human subjects better than three other learning algorithms that are considered.

References (35)

  • L.M. Blume et al.

    Introduction to the stability of rational expectations equilibrium

    Journal of Economic Theory

    (1982)
  • J. Arifovic

    Learning by genetic algorithms in economic environments

  • J. Arifovic

    Adaptation of genetic algorithm in the environments with changing parameters

    (1992)
  • B. Arthur

    Designing economic agents that act like human agents: A behavioral approach to bounded rationality

  • K. Binmore et al.

    Evolutionary stability in repeated games played by finite automata

  • A. Brandenburger

    Information and learning in market games

    (1984)
  • M.M. Bray et al.

    Rational expectations equilibria, learning, and model specification

    Econometrica

    (1986)
  • J. Carlson

    An invariably stable cobweb model

    Review of Economic Studies

    (1969)
  • V.P. Crawford

    An ‘evolutionary’ explanation of Van Huyck, Battalio, and Beil's experimental results on coordination

  • S.J. DeCanio

    Rational expectations and learning from experience

    Quarterly Journal of Economics

    (1979)
  • G.M. Edelman

    Neural Darwinism

    (1987)
  • M. Ezekiel

    The cobweb theorem

    Quarterly Journal of Economics

    (1938)
  • R. Frydman

    Towards an understanding of market processes

    American Economic Review

    (1982)
  • D.E. Goldberg

    Genetic algorithms in search, optimization and machine learning

    (1989)
  • J.H. Holland

    Robust algorithms for adaptation set in a general formal framework

  • J.H. Holland

    Processing and processors for schemata

  • J.H. Holland

    Adaptation in natural and artificial systems

    (1975)
  • Cited by (0)

    This paper derives from my doctoral dissertation. My special thanks go to my advisors Robert Lucas, Thomas Sargent, and Michael Woodford for their valuable help and ideas. Helpful suggestions were also received on an earlier draft from three anonymous referees.

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