We model entrepreneurial learning as a calibrated algorithm of an iterated choice problem in which entrepreneurs learn by updating a subjective stock of knowledge accumulated on the basis of past experiences. Specifically, we argue that entrepreneurs repeat only those choices that appear most promising and discard the ones that resulted in failure. The contribution of the paper is twofold. First, we provide a structural model of entrepreneurial learning in which failure is as informative—though clearly not as desirable—as success. Second, to complement standard economic models in which agents are rational, we allow our entrepreneurs to have myopic foresight. Our entrepreneurs process information, make mistakes, update their decisional algorithms and, possibly, through this struggle, improve their performance.
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