Abstract
Decision problems in environments with iid shocks are typically modeled as involving payoffs that follow a martingale process. The paper formalizes entrepreneurial skills as the ability to design optimal investment strategies for problems, in which payoffs do not follow a martingale. It develops a new model of states of nature, such that Gaussian states (finite variances and finite expectations) are as likely as non-Gaussian states (infinite variances and infinite expectations). To determine a true state, an entrepreneur performs a nonstandard Bayesian inference with a Markov Chain Monte Carlo algorithm. Her competitor uses a standard Bayesian inference and 96% of the time concludes that a true state is Gaussian even when, in reality, it is non-Gaussian. By properly applying strategies optimal for non-Gaussian states, and strategies optimal for Gaussian states, the entrepreneur finds and benefits from profit opportunities that are missed by her competitor.
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