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Na�ve Learning in Social Networks and the Wisdom of Crowds

  • Benjamin Golub
  • Matthew O. Jackson

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 na�vely 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)

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File URL: http://www.aeaweb.org/articles.php?doi=10.1257/mic.2.1.112
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Article provided by American Economic Association in its journal American Economic Journal: Microeconomics.

Volume (Year): 2 (2010)
Issue (Month): 1 (February)
Pages: 112-49

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Handle: RePEc:aea:aejmic:v:2:y:2010:i:1:p:112-49
Note: DOI: 10.1257/mic.2.1.112
Contact details of provider: Web page: https://www.aeaweb.org/aej-microEmail:


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  1. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer, vol. 18(1), pages 39-43, March.
  2. Choi, Syngjoo & Gale, Douglas & Kariv, Shachar, 2008. "Sequential equilibrium in monotone games: A theory-based analysis of experimental data," Journal of Economic Theory, Elsevier, vol. 143(1), pages 302-330, November.
  3. Rosenberg, Dinah & Solan, Eilon & Vieille, Nicolas, 2009. "Informational externalities and emergence of consensus," Games and Economic Behavior, Elsevier, vol. 66(2), pages 979-994, July.
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