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,...

We consider a dynamic market with two firms that sell competing common-value products. The firms offer both products to an infinite set of rational consumers. Each consumer observes a conditionally independent and identically distributed private signal about the product qualities. Consumers...

We consider a general model of boundedly rational opinion formation in social networks. Some agents are initially exposed to misinformation resulting in misspecified prior beliefs. All agents receive a normal, unbiased signal of the true state and subsequently interact repeatedly by announcing...

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...

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...

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....

This paper investigates the robustness of three of the core insights of the DeGroot model of boundedly rational updating in social networks. Two updating systems are ε-close if their supremum norm distance is equal to ε. An ε-perturbation of a DeGroot (weighted average) updating system is any...

This paper develops a model of repeated interaction in social networks among agents with differing degrees of sophistication. The focus of the model is observational learning; that is, each agent receives initial private information and makes inferences regarding the private information of...

We study a sequential social learning model where agents privately acquire information by costly search. Search costs of agents are private to them, and are independently and identically distributed. We show that asymptotic learning occurs if and only if search costs are not bounded away from...