Paying (for) Attention: The Impact of Information Processing Costs on Bayesian Inference

25 Pages Posted: 24 Oct 2016 Last revised: 4 Dec 2016

Scott Duke Kominers

Harvard University

Xiaosheng Mu

Harvard University - Department of Economics

Alexander Peysakhovich

Yale University - Human Cooperation Lab

Date Written: December 3, 2016

Abstract

Human information processing is often modeled as costless Bayesian inference. However, research in psychology shows that attention is a computationally costly and potentially limited resource. We study a Bayesian individual for whom computing posterior beliefs is costly. Such an agent faces a tradeoff between economizing on attention costs and having more accurate beliefs. We show that even small processing costs can lead to significant departures from the standard costless processing model. There exist situations where beliefs can cycle persistently and never converge. In addition, when updating is costly, agents are more sensitive to signals about rare events than to signals about common events. Thus, these individuals can permanently overestimate the likelihood of rare events (e.g., the probability of a plane crash). There is a commonly held assumption in economics that individuals will converge to correct beliefs/optimal behavior given sufficient experience. Our results contribute to a growing literature in psychology, neuroscience, and behavioral economics suggesting that this assumption is both theoretically and empirically fragile.

Keywords: behavioral economics, learning, bayesian updating

Suggested Citation

Kominers, Scott Duke and Mu, Xiaosheng and Peysakhovich, Alexander, Paying (for) Attention: The Impact of Information Processing Costs on Bayesian Inference (December 3, 2016). Available at SSRN: https://ssrn.com/abstract=2857978

Scott Duke Kominers

Harvard University ( email )

Rock Center, Harvard Business School
Soldiers Field
Boston, MA 02163
United States

HOME PAGE: http://www.scottkom.com/

Xiaosheng Mu

Harvard University - Department of Economics ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Alexander Peysakhovich (Contact Author)

Yale University - Human Cooperation Lab ( email )

New Haven, CT
United States

Paper statistics

Downloads
110
Rank
204,371
Abstract Views
495