Document Type
Discussion Paper
Publication Date
2-15-2021
CFDP Number
2274R
CFDP Revision Date
3-11-2021
CFDP Pages
52
Journal of Economic Literature (JEL) Code(s)
D80,D90
Abstract
We study robust welfare comparisons of learning biases, i.e., deviations from correct 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 (one signal) and dynamic (many signals) settings. While the static characterization compares posteriors signal-by-signal, the dynamic characterization employs an “efficiency index” quantifying the speed of belief convergence. Our results yield welfare-founded quantifications of the severity of well-documented biases. Moreover, the static and dynamic rankings can disagree, and “smaller” biases can be worse in dynamic settings.
Recommended Citation
Frick, Mira; Iijima, Ryota; and Ishii, Yuhta, "Welfare Comparisons for Biased Learning" (2021). Cowles Foundation Discussion Papers. 2605.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/2605