American Economic Review
ISSN 0002-8282 (Print) | ISSN 1944-7981 (Online)
Welfare Comparisons for Biased Learning
American Economic Review
vol. 114,
no. 6, June 2024
(pp. 1612–49)
Abstract
We study robust welfare comparisons of learning biases (misspecified Bayesian and some forms of non-Bayesian updating). Given a true signal distribution, we deem one bias more harmful than another if it yields lower objective expected payoffs in all decision problems. We characterize this ranking in static and dynamic settings. While the static characterization compares posteriors signal by signal, the dynamic characterization employs an "efficiency index" measuring how fast beliefs converge. We quantify and compare the severity of several well-documented biases. We also highlight disagreements between the static and dynamic rankings, and that some "large" biases dynamically outperform other "vanishingly small" biases.Citation
Frick, Mira, Ryota Iijima, and Yuhta Ishii. 2024. "Welfare Comparisons for Biased Learning." American Economic Review, 114 (6): 1612–49. DOI: 10.1257/aer.20210410Additional Materials
JEL Classification
- D60 Welfare Economics: General
- D82 Asymmetric and Private Information; Mechanism Design
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making