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Characterizing behavioral decisions with choice data

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Dalton, Patricio S. (Patricio Santiago) and Ghosal, Sayantan (2013) Characterizing behavioral decisions with choice data. Working Paper. Coventry, UK: Department of Economics, University of Warwick. CAGE Online Working Paper Series, Volume 2013 (Number 107).

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Abstract

This paper provides an axiomatic characterization of choices in a setting where a
decision-maker may not fully internalize all the consequences of her choices on herself.
Such a departure from rationality, it turns out, is common across a variety of positive
behavioral models and admits the standard rational choice model as a special case. We
show that choice data satisfying (a) Senís axioms if and
fully characterize behavioral
decisions, and (b) Senís axiom if and if fully characterize standard decision-making. In
addition, we show that (a) it is possible to identify a minimal and a maximal set of
psychological states using choice data alone, and (b) under specific choice scenarios,
"revealed mistakes" can be inferred directly from choice data.

Item Type: Working or Discussion Paper (Working Paper)
Alternative Title: Characterizing behavioural decisions with choice data
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Economics
Library of Congress Subject Headings (LCSH): Human behavior models, Economics -- Mathematical models
Series Name: CAGE Online Working Paper Series
Publisher: Department of Economics, University of Warwick
Place of Publication: Coventry, UK
Official Date: January 2013
Dates:
DateEvent
January 2013Published
Volume: Volume 2013
Number: Number 107
Number of Pages: 30
Status: Not Peer Reviewed
Publication Status: Published
Date of first compliant deposit: 1 August 2016
Date of first compliant Open Access: 1 August 2016
Funder: Marie Curie Fellowship Association (MCFA), Royal Economic Society (Great Britain) (RES), University of Warwick, Economic and Social Research Council (Great Britain) (ESRC), Great Britain. Department for International Development
Grant number: RES-167-25-0364 (ESRC-DFID)

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