Social Networks

This paper examines a game-theoretic model of competition between firms which can target their marketing budgets to individuals embedded in a social network. We provide a sharp characterization of the optimal targeted advertising strategies and highlight their dependence on the underlying social network structure. Furthermore, we provide conditions under which it is optimal for the firms to asymmetrically target a subset of the individuals and establish a lower bound on the ratio of their payoffs in these asymmetric equilibria. Finally, we find that at equilibrium firms invest inefficiently high in targeted advertising and the extent of the inefficiency is increasing in the centralities of the agents they target.

We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. We define asymptotic learning as the fraction of agents taking the correct action converging to one as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the induced communication network most agents are a short distance away from “information hubs,” which receive and distribute a large amount of information.

We study the optimal pricing strategies of a monopolist selling a divisible good (service) to consumers who are embedded in a social network. A key feature of our model is that consumers experience a (positive) local network effect. We characterize the monopolist’s optimal personalized pricing policy and show that it is directly linked to an individual’s Bonacich centrality.