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Preference Identification

Chambers, Christopher P. and Echenique, Federico and Lambert, Nicolas S. (2017) Preference Identification. Social Science Working Paper, 1428. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20170707-095244159

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Abstract

An experimenter seeks to learn a subject's preference relation. The experimenter produces pairs of alternatives. For each pair, the subject is asked to choose. We argue that, in general, large but finite data do not give close approximations of the subject's preference, even when countably infinite many data points are enough to infer the preference perfectly. We then provide sufficient conditions on the set of alternatives, preferences, and sequences of pairs so that the observation of finitely many choices allows the experimenter to learn the subject's preference with arbitrary precision. The sufficient conditions are strong, but encompass many situations of interest. And while preferences are approximated, we show that it is harder to identify utility functions. We illustrate our results with several examples, including expected utility, and preferences in the Anscombe-Aumann model.


Item Type:Report or Paper (Working Paper)
Related URLs:
URLURL TypeDescription
https://arxiv.org/abs/1807.11585arXivDiscussion Paper
ORCID:
AuthorORCID
Echenique, Federico0000-0002-1567-6770
Additional Information:Echenique thanks the National Science Foundation for its support through the grants SES 1558757 and CNS 1518941. Lambert gratefully acknowledges the financial support and hospitality of Microsoft Research New York and the Cowles Foundation at Yale University.
Group:Social Science Working Papers
Funders:
Funding AgencyGrant Number
NSFSES 1558757
NSFCNS 1518941
Microsoft Research New YorkUNSPECIFIED
Cowles FoundationUNSPECIFIED
Subject Keywords:revealed preferences, experimental economics, decision theory, identification
Classification Code:JEL classification: D90,D70
Record Number:CaltechAUTHORS:20170707-095244159
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170707-095244159
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:78839
Collection:CaltechAUTHORS
Deposited By: Hanna Storlie
Deposited On:13 Jul 2017 17:11
Last Modified:28 Jun 2019 17:49

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