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Naïve 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 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)

Suggested Citation

  • Benjamin Golub & Matthew O. Jackson, 2010. "Naïve Learning in Social Networks and the Wisdom of Crowds," American Economic Journal: Microeconomics, American Economic Association, vol. 2(1), pages 112-149, February.
  • Handle: RePEc:aea:aejmic:v:2:y:2010:i:1:p:112-49 Note: DOI: 10.1257/mic.2.1.112

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    References listed on IDEAS

    1. 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.
    2. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    3. 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.
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    Cited by:

    1. Eger, Steffen, 2016. "Opinion dynamics and wisdom under out-group discrimination," Mathematical Social Sciences, Elsevier, vol. 80(C), pages 97-107.
    2. Michel Grabisch & Agnieszka Rusinowska, 2015. "Lattices in Social Networks with Influence," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-18.
    3. Antony Millner & Hélène Ollivier, 2016. "Beliefs, Politics, and Environmental Policy," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 10(2), pages 226-244.
    4. repec:wsi:acsxxx:v:20:y:2017:i:06n07:n:s0219525917500151 is not listed on IDEAS
    5. Marco Angrisani & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2017. "Information redundancy neglect versus overconfidence: a social learning experiment," CeMMAP working papers CWP32/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. repec:hal:journl:hal-00633859 is not listed on IDEAS
    7. Azomahou, T. & Opolot, D., 2014. "Beliefs dynamics in communication networks," MERIT Working Papers 034, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    8. Jackson, Matthew O. & Rogers, Brian & Zenou, Yves, 2016. "Networks: An economic perspective," CEPR Discussion Papers 11452, C.E.P.R. Discussion Papers.
    9. Sun, Lan, 2016. "Hypothesis testing equilibrium in signaling games," Center for Mathematical Economics Working Papers 557, Center for Mathematical Economics, Bielefeld University.
    10. repec:eee:mateco:v:72:y:2017:i:c:p:51-69 is not listed on IDEAS
    11. Michel Grabisch & Agnieszka Rusinowska, 2016. "Determining models of influence," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 2, pages 69-85.
    12. Pivato, Marcus, 2017. "Epistemic democracy with correlated voters," Journal of Mathematical Economics, Elsevier, vol. 72(C), pages 51-69.
    13. Fang, Aili & Wang, Lin & Zhao, Jiuhua & Wang, Xiaofan, 2013. "Chaos in social learning with multiple true states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5786-5792.
    14. Michel Grabisch & Agnieszka Rusinowska, 2016. "Determining models of influence," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 2, pages 69-85.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification


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