We study a model of online news dissemination on a Twitter-like social network. Given a news item and its credibility, agents with heterogeneous priors strategically decide whether to share the news with their followers. An agent shares the news, if the news can persuade her followers to take an...

We study rebate mechanisms according to which a monopolist selling a product introduces rebates as function of the volume of buyers. This enables the firm to induce payoff externalities that ordinarily do not exist. The monopoly firm sells an indivisible good to a mass of consumers with...

This paper studies the behavioral foundations of non-Bayesian models of learning over social networks and develops a taxonomy of conditions for information aggregation in a general framework. As our main behavioral assumption, we postulate that agents follow social learning rules that satisfy...

We study a dynamic game in which short-run players repeatedly play a symmetric, strictly supermodular game whose payoff depends on a fixed unknown state of nature. Each short-run player inherits the beliefs of his immediate predecessor in addition to observing the actions of the players in his...

This paper examines how the structure of a social network and the quality of information available to different agents determine the speed of social learning. To this end, we study a variant of the seminal model of DeGroot (1974), according to which agents linearly combine their personal...

We develop a dynamic model of opinion formation in social networks when the information required for learning a payoff-relevant parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences. However,...

We develop a dynamic model of opinion formation in social networks. Relevant information is spread throughout the network in such a way that no agent has enough data to learn a payoff-relevant parameter. Individuals engage in communication with their neighbors in order to learn from their...