Overabundant Information and Learning Traps

Annie Liang () and Xiaosheng Mu ()
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Annie Liang: Department of Economics, University of Pennsylvania
Xiaosheng Mu: Department of Economics, Harvard University

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

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.

Pages: 49 pages
Date: 2017-10-24, Revised 2017-10-24
New Economics Papers: this item is included in nep-mic
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