An Empirical Test of Gain-Loss Separability in Prospect Theory

Abstract

We investigate a basic premise of prospect theory, that the valuation 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 that 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 preferences 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 probability differences when choosing among mixed gambles than when choosing among either gain or loss gambles.

Descriptive Models Of Decision Making

“…For multiple-outcome lotteries that resolve now, cumulative prospect theory is the prevailing extension [25]. However, empirical evidence is mixed [62,63] and replacements such as the TAX model proposed by Birnbaum [64] may outperform cumulative prospect theory.…”

Time and outcome framing in intertemporal tradeoffs.

“…8 For the tradeoff models, in which RHS and LHS represent pairwise advantages, the above specification is equivalent to Restle's (1961) choice model. We adopt what is known in the literature on risky choice as the representative agent approach (Wu & Markle, 2008) or singleagent stochastic choice method (Camerer & Ho, 1994;Wu & Gonzalez, 1996), which assumes that all participants have the same underlying preferences, but that their choices are stochastically determined by the model equation, that is, the ratio between RHS and LHS. There are two justifications for this approach (see Camerer & Ho, 1994, for more).…”

A prospect theory-based interval dynamic reference point method for emergency decision making

“…This is because PT models two major deviations from expected utility: nonlinear decision weighting and loss aversion, that is, the tendency that people treat outcomes as deviations from a reference point and are more sensitive to losses than gains of the same magnitude. Both nonlinear decision weighting and loss aversion are documented widely in the empirical literature (Abdellaoui, Bleichrodt and Paraschiv, 2007;Bleichrodt, Schmidt and Zank, 2009;Booij and Kuilen, 2009;Wu and Markle, 2008). PT describes the DM's behavioral characteristics well and provides the calculation formulas for gains, losses, and values.…”