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Naïve Learning in Social Networks and the Wisdom of Crowds

Naïve Learning in Social Networks and the Wisdom of Crowds Abstract We study learning in a setting where agents receive independent noisy signals about the true value of a variable and then communicate in a network. They naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. We show that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows. We also identify obstructions to this, including prominent groups, and provide structural conditions on the network ensuring efficient learning. Whether agents converge to the truth is unrelated to how quickly consensus is approached. (JEL D83 , D85 , Z13 ) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Economic Journal: Microeconomics American Economic Association

Naïve Learning in Social Networks and the Wisdom of Crowds

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Publisher
American Economic Association
Copyright
Copyright © 2010 by the American Economic Association
Subject
Articles
ISSN
1945-7685
eISSN
1945-7685
DOI
10.1257/mic.2.1.112
Publisher site
See Article on Publisher Site

Abstract

Abstract We study learning in a setting where agents receive independent noisy signals about the true value of a variable and then communicate in a network. They naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. We show that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows. We also identify obstructions to this, including prominent groups, and provide structural conditions on the network ensuring efficient learning. Whether agents converge to the truth is unrelated to how quickly consensus is approached. (JEL D83 , D85 , Z13 )

Journal

American Economic Journal: MicroeconomicsAmerican Economic Association

Published: Feb 1, 2010

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