We show that the probability that Bayesian posteriors assign to the outcome distributions that do not ``best fit'' the empirical distribution in terms of Kullback-Leibler divergence converges to zero at a uniform and exponential rate, even when the prior does not have full support. This extends...

We use an evolutionary model to determine what sorts of misperceptions are likely to persist. In our model, every period a new generation of agents uses their subjective models and the outcome distribution induced by the previous generation to update their beliefs about some underlying...

We use an evolutionary model to determine which misperceptions can persist. Every period, a new generation of agents use their subjective models and the data generated by the previous generation to update their beliefs, and models that induce better actions become more prevalent. An equilibrium...

We show that Bayesian posteriors concentrate on the outcome distributions that approximately minimize the Kullback–Leibler divergence from the empirical distribution, uniformly over sample paths, even when the prior does not have full support. This generalizes Diaconis and Freedman's (1990)...

We show that Bayesian posteriors concentrate on the outcome distributions that approximately minimize the Kullback-Leibler divergence from the empirical distribution, uniformly over sample paths, even when the prior does not have full support. This generalizes Diaconis and Freedman (1990)'s...

We use an evolutionary model to determine which misperceptions can persist. Every period, a new generation of agents use their subjective models and the data generated by the previous generation to update their beliefs, and models that induce better actions become more prevalent. An equilibrium...