University of Pennsylvania
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 naivete 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. Furthermore, we show that when learning occurs, the rate is exponentially fast, and we provide upper and lower bounds on the rate of learning, as a functionof informativeness of the private signals and "Centrality of agents" in the network.
Joint work with Alireza Tahbaz-Salehi (Columbia GSB), Pooya Molavi (Penn), and Alvaro Sandroni (Kellogg School, Northwestern Unievrsity)
Ali Jadbabaie received his BS degree (with high honors) in Electrical Engineering from Sharif University of Technology in 1995. After a brief period of working as a control engineer, he came to the US and got his Masters degree in Electrical and Computer Engineering from the University of New Mexico, Albuquerque in 1997and a Ph.D. degree in Control and Dynamical Systems from California Institute of Technology in 2001.
From July 2001-July 2002 he was a postdoctoral associate at the department of Electrical Engineering at Yale University. Since July 2002 he has been at the University of Pennsylvania, Philadelphia, PA, where he is currently a Professor of Electrical and Systems Engineering with a secondary appointment in Computer & Information Science.
He is the founding co-director of the Singh Program in Market and Social Systems Engineering, an elite new undergraduate degree program at Penn that blends operations research, economics, and computer science with systems theory. He is a recipient of an NSF Career award, an ONR Young Investigator award, the O. Hugo Schuck Best Paper award of the American Automatic Control Council, and the George S. Axelby Outstanding Paper Award of the IEEE Control Systems Society.
His students have been recipients and finalists of best student paper awards in the American Control Conference and IEEE Conference on Decision and Control. His research is broadly in the interface of control theory and network science, specifically, analysis, design and optimization of networked dynamical systems with applications to sensor networks, multi-robot formation control, opinion aggregation, social learning and other collective phenomena.