Sylvie Geisendorf () (Department of Economics, University of Kassel)
Abstract
Genetic Algorithms (GA) have been used for some years now to depict learning in economic models. Some authors criticize their use on the basis that they are a biologically motivated procedure having nothing to do with human learning. One argument of this paper is that the criticism of GA is focused at the wrong point – and was probably incurred by the much too simple applications we have seen so far. It is not primarily the origin of a model we should be concerned of, but its general characteristics and the specification in its current use. After a brief introduction into the procedure the paper tries to show why GA offer some important features for the modelling of bounded rationality. Learning models based on them are among the few that create novelty and describe the mechanisms of selection, recombination and variation by which novelty is generated. The paper discusses the criticism of GA and argues that the biological origin of the model should not be a substantial problem. The biological features of the model are only a shell in which the general mechanisms of evolutionary processes are imbedded. An up to now underestimated problem however lies in the adequate specification of the fitness function of GA. Proponents as well as critics of GA seemed to have overlooked the necessary distinction between internal fitness criteria of the agents and external criteria of the economy. Both are relevant for economic selection processes and have their proper place in the model. If this is respected GA based learning models can be a useful tool to investigate economic evolution.
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