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Overabundant Information and Learning Traps

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  • Annie Liang

    (Department of Economics, University of Pennsylvania)

  • Xiaosheng Mu

    (Department of Economics, Harvard University)

Abstract

We develop a model of learning from overabundant information: Agents have access to many sources of information, where observation of all sources is not necessary in order to learn the payoff-relevant unknown. Short-lived agents sequentially choose to acquire a signal realization from the best source for them. All signal realizations are public. Our main results characterize two starkly different possible long-run outcomes, and the conditions under which each obtains: (1) efficient information aggregation, where signal acquisitions eventually achieve the highest possible speed of learning; (2) “learning traps,†where the community gets stuck using an suboptimal set of sources and learns inefficiently slowly. A simple property of the correlation structure separates these two possibilities. In both regimes, we characterize which sources are observed in the long run and how often.

Suggested Citation

  • Annie Liang & Xiaosheng Mu, 2018. "Overabundant Information and Learning Traps," PIER Working Paper Archive 18-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 27 Mar 2018.
    • Handle: RePEc:pen:papers:18-008
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    References listed on IDEAS

    as
    1. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2017. "Dynamic Information Acquisition from Multiple Sources," PIER Working Paper Archive 17-023, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 17 Aug 2017.
    2. Benjamin Golub & Matthew O. Jackson, 2012. "How Homophily Affects the Speed of Learning and Best-Response Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1287-1338.
    3. Hann-Caruthers, Wade & Martynov, Vadim V. & Tamuz, Omer, 2018. "The speed of sequential asymptotic learning," Journal of Economic Theory, Elsevier, vol. 173(C), pages 383-409.
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    5. Rajiv Sethi & Muhamet Yildiz, 2016. "Communication With Unknown Perspectives," Econometrica, Econometric Society, vol. 84, pages 2029-2069, November.
    6. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    7. Manuel Mueller-Frank & Mallesh M. Pai, 2016. "Social Learning with Costly Search," American Economic Journal: Microeconomics, American Economic Association, vol. 8(1), pages 83-109, February.
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