This paper provides a formal characterization of the process of rational learning in social networks. Agents receive initial private information and select an action out of a choice set under uncertainty in each of infinitely many periods, observing the history of choices of their neighbors....

We consider a strategic online social network that controls information flows between agents in a social learning setting. Agents on the network select among products of competing firms of unknown quality. The network sells advertising to firms. We consider display advertising, which is standard...

This paper presents a non-Bayesian model of social learning in networks in an environment with a finite set of actions. We conduct a laboratory experiment in which participants play an urn-guessing game over several decision rounds while observing the previous choices of the network members to...

In online social networks, social information, i.e., content generated and shared by users (e.g., past purchases), can be a substitute for sponsored advertising, which constitutes the network's main source of revenue. When will social information be let to spread freely over the platform? We...

We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we...

We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we...

We consider a general model of boundedly rational opinion formation in social networks. We show that long run opinions are extremely vulnerable to unilateral subtle manipulation. For a given updating system, any agent can drive the long run opinions of all agents to an arbitrary desired opinion,...