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Genetic Algorithms in Resource Economic Models

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  • Sylvie Geisendorf

Abstract

The paper describes the application of Genetic Algorithms to a Resource Economics problem; the decision about the intensity of exploitation of a renewable resource. Genetic Algorithms, developed by HOLLAND (1975), are a model of biological evolution, that captures some important features of evolution in general: selection, recombination and arbitrary mistakes. They have therefore also been used already to model economic evolution and learning processes. The model will be based on two main assumptions. First, the agents using the resource are not informed about its reproduction dynamics. And second, although profits are their only concern, they are not able to calculate the optimal extraction rate that would maximize present value of all present and future benefits, like in neoclassical Resource Economics. This is caused by restrictions on the informational as well as the "intellectual" level, all referred to as "bounded rationality". The paper explains the model and its results. It then discusses problems of the application of a biologically motivated prodecure to social evolution and makes some suggestions for a better adaptation of Genetic Algorithms for the purpose of economic modeling.

Suggested Citation

  • Sylvie Geisendorf, 1999. "Genetic Algorithms in Resource Economic Models," Working Papers 99-08-058, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:99-08-058
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    References listed on IDEAS

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    1. Selten, Reinhard, 1991. "Evolution, learning, and economic behavior," Games and Economic Behavior, Elsevier, vol. 3(1), pages 3-24, February.
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    Cited by:

    1. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.

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    More about this item

    Keywords

    Genetic algorithms; bounded rationality; resource economics;
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