Observational learning in an uncertain world
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Ozdaglar_Observational learning.pdf
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Author(s)
Acemoglu, Daron
Dahleh, Munther A.
Ozdaglar, Asuman E.
Tahbaz Salehi, Alireza
Date Issued
February 2011
Journal
Proceedings of the 49th IEEE Conference on Decision and Control (CDC), 2010
Publisher
Institute of Electrical and Electronics Engineers
Citation
Acemoglu, D. et al. “Observational Learning in an Uncertain World.” Decision and Control (CDC), 2010 49th IEEE Conference On. 2010. 6645-6650. © 2011 IEEE.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts 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
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/CDC.2010.5717483
