We study a sequential model of Bayesian social learning in networks in which agents have heterogeneous preferences, and neighbors tend to have similar preferences — a phenomenon known as homophily. We find that the density of network connections determines the impact of preference diversity...

We study the perfect Bayesian equilibria of a sequential model of social learning in networks where agents learn about a state of the world by observing the actions of their neighbors. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We...

This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. We begin with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is...

We study the (perfect Bayesian) equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible...