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. Choices are made based on a common behavioral rule. Conditions under which rational learning leads to global consensus, local indifference, and local disagreement are characterized. In the general setting considered, rational learning can lead to pairs of neighbors selecting different actions once learning ends while not being indifferent among their choices. The effect of the network structure on the degree of information aggregation and speed of convergence is also considered, and an answer to the question of optimal information aggregation in networks is provided. The results highlight distinguishing features between properties of Bayesian and non‐Bayesian learning in social networks.

No abstract

No abstract

We study a sequential social learning model where agents privately acquire information by costly search. Search costs of agents are private, and are independently and identically distributed. We show that asymptotic learning occurs if and only if search costs are not bounded away from zero. We explicitly characterize equilibria for the case of two actions, and show that the probability of late moving agents taking the suboptimal action vanishes at a linear rate. Social welfare converges to the social optimum as the discount rate converges to one if and only if search costs are not bounded away from zero. (JEL D81, D83)

This paper introduces a new general model of boundedly rational observational learning: Quasi-Bayesian updating. The approach is applicable to any environment of observational learning and is rationally founded. We conduct a laboratory experiment and find strong supportive evidence for Quasi-Bayesian updating. We analyze the theoretical long run implications of Quasi-Bayesian updating in a model of repeated interaction in social networks with binary actions. We provide a characterization of the environment in which consensus and information aggregation is achieved. The experimental evidence is in line with our theoretical predictions. Finally, we establish that for any environment information aggregation fails in large networks.