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A Model Of Boundedly Rational Consumer Choice


  • Thomas Riechman

    (University of Hannover)


The paper presents an extended version of the standard textbook problem of consumer choice. As usual, agents have to decide about their desired quatities of various consumption goods, at the same time taking into account their limited budget. Prices for the goods are not fixed but arise from a Walrasian interaction of total demand and a stilized supply function for each of the goods. After showing that this type of model cannot be solved analytically, three different types of evolutionary algorithms are set up to answer the question whether agents behaving according to the rules of these algorithms can solve the problem of extended consumer choice. There are two important answers to this question: a) The quality of the learned results crucially depends on the elasticity of supply, which in turn can be shown to be a measure of the degree of state dependency of the economic problem. b) Statistical tests suggest that for the agents in the model it is relatively easy to adhere to the budget constraint, but that it is relatively difficult to reach an optimum with marginal utility per Dollar being equal for each good.

Suggested Citation

  • Thomas Riechman, 2000. "A Model Of Boundedly Rational Consumer Choice," Computing in Economics and Finance 2000 321, Society for Computational Economics.
  • Handle: RePEc:sce:scecf0:321

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    References listed on IDEAS

    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    3. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July.
    4. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    5. Chris Birchenhall & Nikos Kastrinos & Stan Metcalfe, 1997. "Genetic algorithms in evolutionary modelling," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 375-393.
    6. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
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    Cited by:

    1. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.

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