Welfare Comparisons for Biased Learning

Mira Frick, , and Yuhta Ishii ()

No 16833, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Date: 2021-12
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://cepr.org/publications/DP16833 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

Related works:
Working Paper: Welfare Comparisons for Biased Learning (2021)
Working Paper: Welfare Comparisons for Biased Learning (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:16833

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP16833

Access Statistics for this paper

More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().