Observational learning in an uncertain world
Author(s)Acemoglu, Daron; Dahleh, Munther A.; Ozdaglar, Asuman E.; Tahbaz Salehi, Alireza
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We study a model of observational learning in social networks in the presence of uncertainty about agents' type distributions. Each individual receives a private noisy signal about a payoff-relevant state of the world, and can observe the actions of other agents who have made a decision before her. We assume that agents do not observe the signals and types of others in the society, and are also uncertain about the type distributions. We show that information is correctly aggregated when preferences of different types are closely aligned. On the other hand, if there is sufficient heterogeneity in preferences, uncertainty about type distributions leads to potential identification problems, preventing asymptotic learning. We also show that even though learning is guaranteed to be incomplete ex ante, there are sample paths over which agents become certain about the underlying state of the world.
DepartmentMassachusetts Institute of Technology. Department of Economics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Proceedings of the 49th IEEE Conference on Decision and Control (CDC), 2010
Institute of Electrical and Electronics Engineers
Acemoglu, D. et al. “Observational Learning in an Uncertain World.” Decision and Control (CDC), 2010 49th IEEE Conference On. 2010. 6645-6650. © 2011 IEEE.
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