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Overabundant Information and Learning Traps

Author

Listed:
  • Annie Liang

    (Department of Economics, University of Pennsylvania)

  • Xiaosheng Mu

    (Department of Economics, Harvard University)

Abstract

We study a model of sequential learning, where agents choose what kind of information to acquire from a large, fixed set of Gaussian signals with arbitrary correlation. In each period, a short-lived agent acquires a signal from this set of sources to maximize an individual objective. All signal realizations are public. We study the community's asymptotic speed of learning, and characterize the set of sources observed in the long run. A simple property of the correlation structure guarantees that the community learns as fast as possible, and moreover that a \best" set of sources is eventually observed. When the property fails, the community may get stuck in an inefficient set of sources and learn (arbitrarily) slowly. There is a specific, diverse set of possible final outcomes, which we characterize.

Suggested Citation

  • Annie Liang & Xiaosheng Mu, 2017. "Overabundant Information and Learning Traps," PIER Working Paper Archive 17-024, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 24 Oct 2017.
    • Handle: RePEc:pen:papers:17-024
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    References listed on IDEAS

    as
    1. 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.
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