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
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 payoï¬€-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 diï¬€erent possible long-run outcomes, and the conditions under which each obtains: (1) eï¬ƒcient information aggregation, where signal acquisitions eventually achieve the highest possible speed of learning; (2) â€œlearning traps,â€ where the community gets stuck using an suboptimal set of sources and learns ineï¬ƒciently 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.
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Date: 2018-03-27, Revised 2018-03-27
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