We study the manner in which learning shapes behavior towards risk when individuals are not assumed to know, or to have beliefs about, probability distributions. In any period, the behavior change induced by learning is assumed to depend on the action chosen and the payoff obtained. We characterize learning processes that, in expected value, increase the probability of choosing the safest (or riskiest) actions and provide sufficient conditions for them to converge, in the long run, to the choices of risk averse (or risk seeking) expected utility maximizers. We provide a learning theoretic motivation for long run risk choices, such as those in expected utility theory with known payoff distributions. * We thank two anonymous referees and an associate editor for instructive comments. We also thank
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