We consider learning in games that are misspecified in that players are unable to learn the true probability distribution over outcomes. Under misspecification, Bayes rule might not converge to the model that leads to actions with the highest objective payoff among the models subjectively...

We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one prior distribution over a set of parameters and provide necessary and sufficient condition for ambiguity to fade away because of learning. Our condition applies to most learning environments: iid...

Under-reaction is a robust response to model misspecification rewarded by financial markets, rather than an “irrational” attitude that leads to extinction. I show that under-reacting rules guarantee predictions as accurate as Bayes' in well-specified learning problems and beat Bayes' in many...