Optimal Myopic Information Acquisition

55 Pages Posted: 13 Mar 2017  

Annie Liang

University of Pennsylvania

Xiaosheng Mu

Harvard University - Department of Economics

Vasilis Syrgkanis

Microsoft Corporation - Microsoft Research New England

Date Written: August 14, 2017

Abstract

We consider the problem of optimal information acquisition from many correlated information sources. Each period, the DM jointly takes an action and allocates a fixed number of observations across the available sources. His payoff depends on the actions taken and on an unknown state. In a canonical setting--jointly normal information sources--we show that the optimal dynamic information acquisition rule proceeds myopically after finitely many periods. If signals are acquired in large blocks each period, then the optimal rule turns out to be myopic from period 1. These results demonstrate the possibility of robust and "simple" optimal information acquisition, and simplify the analysis of dynamic information acquisition in a widely used informational environment.

Keywords: learning, information acquisition, dynamics

JEL Classification: D81, D83, C44

Suggested Citation

Liang, Annie and Mu, Xiaosheng and Syrgkanis, Vasilis, Optimal Myopic Information Acquisition (August 14, 2017). Available at SSRN: https://ssrn.com/abstract=2931845 or http://dx.doi.org/10.2139/ssrn.2931845

Annie Liang (Contact Author)

University of Pennsylvania ( email )

Philadelphia, PA 19104
United States

Xiaosheng Mu

Harvard University - Department of Economics ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Vasilis Syrgkanis

Microsoft Corporation - Microsoft Research New England ( email )

One Memorial Drive, 14th Floor
Cambridge, MA 02142
United States

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