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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 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.

New Economics Papers: this item is included in nep-mic
Date: 2018-03-27, Revised 2018-03-27
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