| Abstract: | The ‘as if ’ view of economic rationality defends the profit maximization hypothesis by pointing out that only those firms who act as if they maximize profits can survive in the long run. Recently, the problem of arriving at a logically consistent definition of rational behavior in games has shown that one must sometimes study explicitly the evolutionary processes that form the basis of this view. The purpose of this paper is to investigate the usefulness of genetic programming as a tool for generating hypotheses about rational behavior in situations where explicit maximization is not well defined. We use an investment decision problem with Knightian uncertainty as a borderline test case, and show that when the artificial agents receive the same information about the unknown probability distributions, they develop behavior rules as if they were expected utility maximizers with Bayesian learning rules. |
