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Treasure Hunt: Social Learning in the Field

Author

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  • Markus Mobius
  • Tuan Phan
  • Adam Szeidl

Abstract

We seed noisy information to members of a real-world social network to study how information diffusion and information aggregation jointly shape social learning. Our environment features substantial social learning. We show that learning occurs via diffusion which is highly imperfect: signals travel only up to two steps in the conversation network and indirect signals are transmitted noisily. We then compare two theories of information aggregation: a naive model in which people double-count signals that reach them through multiple paths, and a sophisticated model in which people avoid double-counting by tagging the source of information. We show that to distinguish between these models of aggregation, it is critical to explicitly account for imperfect diffusion. When we do so, we find that our data are most consistent with the sophisticated tagged model.

Suggested Citation

  • Markus Mobius & Tuan Phan & Adam Szeidl, 2015. "Treasure Hunt: Social Learning in the Field," NBER Working Papers 21014, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:21014
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    References listed on IDEAS

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    1. Brandts, Jordi & Giritligil, Ayça Ebru & Weber, Roberto A., 2015. "An experimental study of persuasion bias and social influence in networks," European Economic Review, Elsevier, vol. 80(C), pages 214-229.
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    5. Esther Duflo & Emmanuel Saez, 2003. "The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment," The Quarterly Journal of Economics, Oxford University Press, vol. 118(3), pages 815-842.
    6. Michael Kremer & Edward Miguel, 2007. "The Illusion of Sustainability," The Quarterly Journal of Economics, Oxford University Press, vol. 122(3), pages 1007-1065.
    7. Syngjoo Choi & Douglas Gale & Shachar Kariv, 2012. "Social learning in networks: a Quantal Response Equilibrium analysis of experimental data," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 135-157, September.
    8. Cameron,A. Colin & Trivedi,Pravin K., 2008. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9787111235767.
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    1. repec:wsi:acsxxx:v:20:y:2017:i:06n07:n:s0219525917500151 is not listed on IDEAS
    2. Brandts, Jordi & Giritligil, Ayça Ebru & Weber, Roberto A., 2015. "An experimental study of persuasion bias and social influence in networks," European Economic Review, Elsevier, vol. 80(C), pages 214-229.
    3. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2015. "Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field," NBER Working Papers 21468, National Bureau of Economic Research, Inc.
    4. Drago, Francesco & Mengel, Friederike & Traxler, Christian, 2015. "Compliance Behavior in Networks: Evidence from a Field Experiment," IZA Discussion Papers 9443, Institute for the Study of Labor (IZA).
    5. Dey, Oindrila & Das, Abhishek & Gupta, Gautam & Banerjee, Swapnendu, 2017. "Favouritism Or Fairness?: A Framed Laboratory Experiment," MPRA Paper 80214, University Library of Munich, Germany.
    6. Yann Algan & Quoc-Anh Do & Nicolò Dalvit & Alexis Le Chapelain & Yves Zenou, 2015. "How Social Networks Shape Our Beliefs: A Natural Experiment among Future French Politicians," Sciences Po publications info:hdl:2441/78vacv4udu9, Sciences Po.

    More about this item

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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