Presented by: 
Andy McLennan
Tue 29 May, 3:00 pm - 4:00 pm
Room 442, Building 67

A Bayesian decision maker does not know which of several parameters is true. In each period she chooses an action a from an open subset of Rn, observes an outcome, and updates her beliefs. There is an action a* that is uninformative in the sense that when it is chosen all parameters give the same distribution over outcomes, and consequently beliefs do not change. We give conditions under which a policy specifying an action as a function of the current belief can result in a positive probability that the sequence of beliefs converge to a belief at which a* is chosen, so that learning is asymptotically incomplete. Such a policy can be optimal even when the decision maker is not myopic and values experimentation.