Empirical evidence suggests that entrepreneurs make mistakes: too many enter markets and, once there, persist too long. While scholars have largely settled on behavioral bias as the cause, we suggest that this consensus is premature. These mistakes may also arise from a process in which entrepreneurs continually learn about their prospects, and make entry and exit decisions from what they have learned. We develop a computational model of this process that connects pre- and post-entry learning and can be directed to analyze Bayesian rational or biased entrepreneurs. The model suggests that, to outside observers, rational entrepreneurs may appear overconfident, seem to take too long to exit, and exhibit a positive correlation between entry cost and persistence in the market. When examining confidence biases, the model suggests that entrepreneurs whose biases cause them to perform the worst post-entry will be most likely to enter, that pre-entry learning induces a positive correlation between distinct confidence biases among entrants, and that exit changes the prevalence of certain biases in the surviving population of entrants over time. Our study also speaks to recent work on pre-entry experience that documents the transfer of knowledge from parent to progeny firms, suggesting that, in addition to inheritance, differential performance may also be the result of heterogeneity in the length and quality of pre-entry learning during which an opportunity is assessed.
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