Title: The Social Learning Barrier

Abstract: We consider long-lived agents who interact repeatedly in a social network. In each period, each agent learns about an unknown state by observing a private signal and her neighbors’ actions in the previous period before taking an action herself. Our main result shows that the learning rate of the slowest learning agent is bounded independently of the network size and structure and the agents’ strategies. This extends recent findings on equilibrium learning by demonstrating that the limitation stems from an inherent tradeoff between optimal action choices and information revelation, rather than strategic considerations. We complement this result by showing that a social planner can design strategies for which each agent learns faster than an isolated individual, provided the network is sufficiently large and strongly connected.

Link: http://brandlf.com/docs/network-learning.pdf