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Approximate Expected Utility Rationalization

Echenique, Federico and Imai, Taisuke and Saito, Kota (2021) Approximate Expected Utility Rationalization. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210303-132639472

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Abstract

We propose a new measure of deviations from expected utility theory. For any positive number e, we give a characterization of the datasets with a rationalization that is within e (in beliefs, utility, or perceived prices) of expected utility theory. The number e can then be used as a measure of how far the data is to expected utility theory. We apply our methodology to data from three large-scale experiments. Many subjects in those experiments are consistent with utility maximization, but not with expected utility maximization. Our measure of distance to expected utility is correlated with subjects' demographic characteristics.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2102.06331arXivDiscussion Paper
https://resolver.caltech.edu/CaltechAUTHORS:20191018-101812371Related ItemWorking Paper
ORCID:
AuthorORCID
Echenique, Federico0000-0002-1567-6770
Imai, Taisuke0000-0002-0610-8093
Saito, Kota0000-0003-1189-8912
Additional Information:We are very grateful to Nicola Persico, who posed questions to us that led to some of the results in this paper, and to Jose Apesteguía, Miguel Ballester, Geoffroy de Clippel, Dan Friedman, Yves Le Yaouanq, Pietro Ortoleva, Matthew Polisson, John Quah and Kareen Rozen for helpful comments. We are also grateful for the feedback provided by numerous audience at FUR 2018, ESA World Meetings 2018, 4th Hitotsubashi Summer Institute: Microeconomic Theory, 2018 European Summer Meeting of the Econometric Society, CESifo Area Conference on Behavioural Economics 2018, Measuring Individual Well-Being Workshop, and 2019 European Summer Symposium in Economic Theory. This research is supported by Grant SES-1558757 from the National Science Foundation. The authors also acknowledge financial support by the NSF through the grants CNS-1518941 (Echenique) and SES-1919263 (Saito), and the Deutsche Forschungsgemeinschaft through CRC TRR 190 (Imai).
Funders:
Funding AgencyGrant Number
NSFSES-1558757
NSFCNS-1518941
NSFSES-1919263
Deutsche Forschungsgemeinschaft (DFG)CRC TRR 190
Record Number:CaltechAUTHORS:20210303-132639472
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210303-132639472
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108292
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 Mar 2021 21:46
Last Modified:03 Mar 2021 21:46

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