DYNAMIC PROGRAMMING AND SOCIAL LEARNING VIA REPLICATOR DYNAMICS

This paper introduces a social learning algorithm for recursive decision problems faced by players in large anonymous games. The algorithm keeps track of only the distributions of agents over possible state-action pairs. State update, value update and behavior update constitute the three stages of the algorithm. The stability of the algorithm is studied. Numerical applications to consumption problems with and without cash-in-advance constraints are considered.