Social learning with case-based decisions

Abstract

In decision problems with unknown payoffs, individuals use their own past experience and the experiences of others. In this article I develop a theory that links in a natural way aggregated decision processes to the underlying communication structure. In particular, I consider a binary decision problem. Since there is noise in the payoff structure agents have to learn which decision has the better performance. I analyze the long-run convergence behaviour of the social learning path for four basic communication structures. Specifically, it is shown that for complete information there is a tendency to conformity if the aspiration level of the population is low and a tendency for non-convergence if the aspiration level is high. Star communication structures, in contrast, are characterized by diversity in the long run, whereas in Δ- neighbourhood communication structures the better decision diffuses with a constant pace. The relative performances of subpopulations or cliques are shown to depend crucially on the communication intensity and relative size of the subpopulations.