We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new...

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 how to optimally attract new customers using a referral program. Whenever a consumer makes a purchase, the firm gives her a link to share with friends, and every purchase coming through that link generates a referral payment. The firm chooses the referral payment function and consumers...

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...

We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new...

We study a model of social learning in networks where agents have heterogeneous preferences, and neighbors tend to have similar preferences---a phenomenon known as homophily. Using this model, we resolve a puzzle in the literature: theoretical models predict that preference diversity helps...