Non-Bayesian Social Learning
University of Pennsylvania - Department of Electrical and Systems Engineering
University of Pennsylvania - School of Engineering & Applied Science
University of Pennsylvania - Department of Economics; Northwestern University - Kellogg School of Management
Columbia Business School - Decision Risk and Operations
August 5, 2011
PIER Working Paper No. 11-025
Columbia Business School Research Paper No. 13-5
We develop a dynamic model of opinion formation in social networks when the information required for learning a payoff-relevant parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner, agents use a simple updating rule which linearly combines their personal experience and the views of their neighbors (even though the neighbors’ views may be quite inaccurate). This non-Bayesian learning rule is motivated by the formidable complexity required to fully implement Bayesian updating in networks. We show that, as long as individuals take their personal signals into account in a Bayesian way, repeated interactions lead them to successfully aggregate information and learn the true underlying state of the world. This result holds in spite of the apparent naiveté of agents’ updating rule, the agents’ need for information from sources the existence of which they may not be aware of, the possibility that the most persuasive agents in the network are precisely those least informed and with worst prior views, and the assumption that no agent can tell whether her own views or those of her neighbors are more accurate.
Number of Pages in PDF File: 25
Keywords: Social networks, learning, information aggregation
JEL Classification: D83, L14working papers series
Date posted: August 24, 2011 ; Last revised: January 30, 2013
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