Information Costs and Sequential Information Sampling

57 Pages Posted: 3 Dec 2018

See all articles by Benjamin Hebert

Benjamin Hebert

Stanford University

Michael Woodford

Columbia University, Graduate School of Arts and Sciences, Department of Economics; National Bureau of Economic Research (NBER)

Date Written: November 2018

Abstract

We propose a new approach to modeling the cost of information structures in rational inattention problems, the "neighborhood-based" cost functions. These cost functions have two properties that we view as desirable: they summarize the results of a sequential evidence accumulation problem, and they capture notions of "perceptual distance." The first of these properties is connected to an extensive literature in psychology and neuroscience, and the second ensures that neighborhood-based cost functions, unlike mutual information, make accurate predictions about behavior in perceptual experiments. We compare the implications of our neighborhood-based cost functions with those of a mutual-information cost function in a series of applications: security design, global games, modeling perceptual judgments, and a linear-quadratic-Gaussian tracking problem.

Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Suggested Citation

Hebert, Benjamin M. and Woodford, Michael, Information Costs and Sequential Information Sampling (November 2018). NBER Working Paper No. w25316. Available at SSRN: https://ssrn.com/abstract=3294919

Benjamin M. Hebert (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

Michael Woodford

Columbia University, Graduate School of Arts and Sciences, Department of Economics ( email )

420 W. 118th Street
New York, NY 10027
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
6
Abstract Views
117
PlumX Metrics