Naïve Learning in Social Networks and the Wisdom of Crowds

Social networks are primary conduits of information, opinions, and behaviors. They carry news about products, jobs, and various social programs; influence decisions to become educated, to smoke, and to commit crimes; and drive political opinions and attitudes toward other groups. In view of this, it is important to understand how beliefs and behaviors evolve over time, how this depends on the network structure, and whether or not the resulting outcomes are efficient. In this paper, we examine one aspect of this broad theme: for which social network structures will a society of agents who communicate and update naïvely come to aggregate decentralized information completely and correctly? Given the complex forms that social networks often take, it can be difficult for the agents involved (or even for a modeler with full knowledge of the network) to update beliefs properly. For example, Syngjoo Choi, Douglas Gale, and Shachar Kariv (2005, 2008) find that although subjects in simple three-person networks update fairly well in some circumstances, they do not do so well in evaluating repeated observations and judging indirect information for which the origin is uncertain. Given that social communication often involves repeated transfers of information among large numbers of individuals in complex networks, fully rational learning becomes infeasible. Nonetheless, it is possible that agents using fairly simple updating rules will arrive at outcomes like those achieved through fully rational learning. We identify social networks for which naïve individuals converge to fully rational