This talk has two parts. The first part is Bayesian and describes structural results for partially observed Markov decision processes in multi-agent systems when individual agents perform social learning. Two specific examples are considered. The first example deals with the so called constrained optimal social learning problem where the onset of herding is delayed by agents sharing full information. The second example deals with change detection when individual agents perform social learning.

The second part of the talk discusses regret based stochastic approximation algorithms for learning correlated equilibria in repeated games with Markov switched parameters.