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- Article] Using logarithmic derivative functions for assessing the risky weighting function for binary gambles
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DocNo of ILP: 830
Doc. Type: Article
Title: Using logarithmic derivative functions for assessing the risky weighting function for binary gambles
Authors: Chechile, RA; Barch, DH
Full Name of Authors: Chechile, Richard A.; Barch, Daniel H.
Keywords by Author: Utility theory; Risky weighting functions; Models for the probability weighting function; Model comparison
Keywords Plus: PARAMETER-FREE ELICITATION; GENERIC UTILITY-THEORY; SUBJECTIVE-PROBABILITY; EXPECTED UTILITY; HAZARD FUNCTIONS; PROSPECT-THEORY; MODELS; CHOICE; ADDITIVITY; BEHAVIOR
Abstract: A logarithmic derivative (LD) of a continuous function g (x) is itself a function in the form of g'(x)/g(x). Hazard and reverse hazard are examples of ID functions that have proven to be useful for discriminating among similar functions for stochastic systems, and the essential idea of ID functions can be used more generally. In this research, an analysis of the logarithmic derivative was employed to evaluate the various proposals for the risky weighting function omega(p) that have been advanced in the psychological and economic literature. Risky weighting functions are the weighting coefficients of the outcome utility values, i.e., if an outcome has an associated probability p, then g'(x)/g(x)(p) is the transform of p that weights the utility of the outcome. An experiment was done to obtain empirical estimates of the logarithmic derivative of the risky weighting function for individuals by utilizing a novel gamble-matching paradigm with binary gambles. Five models from the research literature did not predict the observed shape for the LD function. Four additional models for the risky weighting function could predict the general profile of the LD function but nonetheless resulted in a nonrandom, systematic pattern for the corresponding model fit residuals. The nonrandom pattern of the fit residuals is taken as evidence against the models. Consequently nine models had problems in accounting for the empirical LD function. However, two risky weighting functions provided an accurate description of the empirical LD function. These risky weighting functions are the Prelec function omega(p) = e(-s(-Inp)a), with a and s as fitting parameters, and a novel model, the Exponential Odds function omega(p) = e(-s(1-)b/pa) with a, b and s as fitting parameters. (C) 2013 Elsevier Inc. All rights reserved.
Cate of OECD: Mathematics
Year of Publication: 2013
Business Area: gamble
Detail Business: gamble
Country: USA
Study Area:
Name of Journal: JOURNAL OF MATHEMATICAL PSYCHOLOGY
Language: English
Country of Authors: [Chechile, Richard A.; Barch, Daniel H.] Tufts Univ, Medford, MA 02155 USA
Press Adress: Chechile, RA (reprint author), Tufts Univ, Dept Psychol, Medford, MA 02155 USA.
Email Address: Richard.Chechile@tufts.edu
Citaion:
Funding:
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Number of Citaion: 54
Publication: ACADEMIC PRESS INC ELSEVIER SCIENCE
City of Publication: SAN DIEGO
Address of Publication: 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
ISSN: 0022-2496
29-Character Source Abbreviation: J MATH PSYCHOL
ISO Source Abbreviation: J. Math. Psychol.
Volume: 57
Version: 41641
Start of File: 15
End of File: 28
DOI: 10.1016/j.jmp.2013.03.001
Number of Pages: 14
Web of Science Category: Mathematics, Interdisciplinary Applications; Social Sciences, Mathematical Methods; Psychology, Mathematical
Subject Category: Mathematics; Mathematical Methods In Social Sciences; Psychology
Document Delivery Number: 158GS
Unique Article Identifier: WOS:000319958100002
[이 게시물은 HyeJung Mo…님에 의해 2015-05-20 17:09:59 GAMBLING에서 이동 됨]
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