An Empirical Test of Gain-Loss Separability in Prospect Theory

We investigate a basic premise of prospet theory: that the evaluation of gains and losses is separable. In prospect theory, gain-loss separability implies that a mixed gamble is valued by summing the valuations of the gain and loss portions of the gamble. Two experimental studies demonstrate a syste...

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Bibliographische Detailangaben
Veröffentlicht in:	Management Science. - Institute for Operations Research and the Management Sciences, 1954. - 54(2008), 7, Seite 1322-1335
1. Verfasser:	Wu, George (VerfasserIn)
Weitere Verfasser:	Markle, Alex B.
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2008
Zugriff auf das übergeordnete Werk:	Management Science
Schlagworte:	risky choice prospect theory mixed gambles double matching probability weighting function Behavioral sciences Mathematics Applied sciences


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520			\|a We investigate a basic premise of prospet theory: that the evaluation of gains and losses is separable. In prospect theory, gain-loss separability implies that a mixed gamble is valued by summing the valuations of the gain and loss portions of the gamble. Two experimental studies demonstrate a systematic violation of the double-matching axiom, an axiom that is necessary for gain-loss separability. We document a reversal between preferenes for mixed gambles and the associated gain and loss gambles-mixed gamble A is preferred to mixed gamble B, but the gain and loss portions of B are preferred to the gain and loss portions of A. The observed choice patterns are consistent with a process in which individuals are less sensitive to probabilityy differences when choosing among mixed gambles than when choosing among either gain or loss gambles.
540			\|a Copyright 2008 INFORMS
650		4	\|a risky choice
650		4	\|a prospect theory
650		4	\|a mixed gambles
650		4	\|a double matching
650		4	\|a probability weighting function
650		4	\|a Behavioral sciences \|x Behavioral economics \|x Prospect theory
650		4	\|a Mathematics \|x Mathematical expressions \|x Mathematical functions \|x Weighting functions
650		4	\|a Behavioral sciences \|x Psychology \|x Cognitive psychology \|x Cognitive processes \|x Problem solving \|x Heuristics
650		4	\|a Mathematics \|x Applied mathematics \|x Management science
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Error rates
650		4	\|a Applied sciences \|x Research methods \|x Modeling
650		4	\|a Mathematics \|x Applied mathematics \|x Analytics \|x Error analysis \|x Random errors
650		4	\|a Behavioral sciences \|x Behavioral economics \|x Prospect theory \|x Loss aversion
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Applied statistics \|x Statistical models \|x Parametric models
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Applied statistics \|x Statistical analysis \|x Statistical tests
650		4	\|a Behavioral sciences \|x Behavioral economics \|x Prospect theory
650		4	\|a Mathematics \|x Mathematical expressions \|x Mathematical functions \|x Weighting functions
650		4	\|a Behavioral sciences \|x Psychology \|x Cognitive psychology \|x Cognitive processes \|x Problem solving \|x Heuristics
650		4	\|a Mathematics \|x Applied mathematics \|x Management science
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Error rates
650		4	\|a Applied sciences \|x Research methods \|x Modeling
650		4	\|a Mathematics \|x Applied mathematics \|x Analytics \|x Error analysis \|x Random errors
650		4	\|a Behavioral sciences \|x Behavioral economics \|x Prospect theory \|x Loss aversion
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Applied statistics \|x Statistical models \|x Parametric models
650		4	\|a Mathematics \|x Applied mathematics \|x Statistics \|x Applied statistics \|x Statistical analysis \|x Statistical tests
655		4	\|a research-article
700	1		\|a Markle, Alex B. \|e verfasserin \|4 aut
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773	1	8	\|g volume:54 \|g year:2008 \|g number:7 \|g pages:1322-1335
856	4	0	\|u https://www.jstor.org/stable/20122478 \|3 Volltext
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