Proceedings of the Annual Meeting of the Cognitive Science Society

We present a model of social learning over networks were individuals with insufficient and heterogeneous sources ofinformation aggregate their private observations with samples from belief distributions of their neighbors in order to learnan underlying state of the world. We presume two behavioral assumptions. The first assumes communication constraintsin that agents can only share, in each round, a single sample from their belief on the true state with their neighbors. This isin contrast with standard models of sharing the full belief, i.e. the entire probability distribution over the set of parameters.The second behavioral assumption points to an updating scheme according to which agents use simple linear rules toaggregate their neighbors’ actions with their private Bayesian posterior. We rigorously analyze the asymptotic behaviorof such an update and show that so long as all the individuals trust their neighbors more than their private informationsources, they do not learn the true parameter with positive probability. Social learning can occur, however, if the societycontains confident individuals that are experts in distinguishing different alternatives from truth, even though no singleindividuals may be able to distinguish the truth on her own. Our results indicate that social learning is possible even whenagents only share a single sample from their belief distribution.