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Dynamic Information Acquisition from Multiple Sources

Annie Liang (), Xiaosheng Mu () and Vasilis Syrgkanis ()
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Annie Liang: Department of Economics, University of Pennsylvania
Xiaosheng Mu: Department of Economics, Harvard University
Vasilis Syrgkanis: Microsoft Research, New England

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

Abstract: Consider a decision-maker who dynamically acquires Gaussian signals that are related by a completely flexible correlation structure. Such a setting describes information acquisition from news sources with correlated biases, as well as aggregation of complementary information from specialized sources. We study the optimal sequence of information acquisitions. Generically, myopic signal acquisitions turn out to be optimal at sufficiently late periods, and in classes of informational environments that we describe, they are optimal from period 1. These results hold independently of the decision problem and its (endogenous or exogenous) timing. We apply these results to characterize dynamic information acquisition in games.

New Economics Papers: this item is included in nep-gth and nep-mic
Date: 2017-08-17, Revised 2017-08-17
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