Genetic Algorithms in Resource Economic Models
AbstractThe 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.
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Bibliographic InfoPaper provided by Santa Fe Institute in its series Working Papers with number 99-08-058.
Date of creation: Aug 1999
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Genetic algorithms; bounded rationality; resource economics;
This paper has been announced in the following NEP Reports:
- NEP-ALL-1999-09-21 (All new papers)
- NEP-CMP-1999-10-04 (Computational Economics)
- NEP-ENE-1999-09-21 (Energy Economics)
- NEP-EVO-1999-09-21 (Evolutionary Economics)
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- 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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