2010, Bayesian learning in unstable settings: Experimental evidence based on the bandit problem, Working paper

We study learning in a bandit task in which the outcome probabilities of six arms switch (“jump”) over time. In the experiment, optimal Bayesian learning tracks the jumps by discovering the probability of a jump or direct jump detection. Although more complex than the natural alternative of learning, through adaptive expectations, when combined with a partially myopic decision rule, Bayesian learning better matches the behavior observed in the lab. This result suggests that agents might be less limited in their computational capacities than previously thought, and that complexity does not always hamper fully rational learning