In their pioneering works on prospect theory Kahneman and Tversky (1979, 1992) propose the ground-breaking idea that in making decisions under risk individuals evaluate asymmetrically losses and gains against to a personal reference point. According to the Kahneman and Tversky (1979) statement “losses loom larger than gains”, individuals display loss aversion. However, Sacchi and Stanca (2014) argue that people may exhibit gain appetite that states that “gains loom larger than losses”. Although the prospect theory can be traced back of more than thirty years, how to formalize asymmetrical preferences to a reference point is still an open issue (see Abdellaoui et al., 2007; and Ghossoub, 2012). In this short note we set a preference- based definition for loss aversion, gain appetite and equally weighted preferences “in the small”, i.e. for outcomes around a given reference point; and “in the large”, i.e. for any outcome of the domain. The classical Kahneman and Tversky (1979, page 279) loss aversion definition follows as a special case.
Loss aversion and gain appetite in the small and in the large
TIBILETTI, Luisa;UBERTI, Mariacristina
2016-01-01
Abstract
In their pioneering works on prospect theory Kahneman and Tversky (1979, 1992) propose the ground-breaking idea that in making decisions under risk individuals evaluate asymmetrically losses and gains against to a personal reference point. According to the Kahneman and Tversky (1979) statement “losses loom larger than gains”, individuals display loss aversion. However, Sacchi and Stanca (2014) argue that people may exhibit gain appetite that states that “gains loom larger than losses”. Although the prospect theory can be traced back of more than thirty years, how to formalize asymmetrical preferences to a reference point is still an open issue (see Abdellaoui et al., 2007; and Ghossoub, 2012). In this short note we set a preference- based definition for loss aversion, gain appetite and equally weighted preferences “in the small”, i.e. for outcomes around a given reference point; and “in the large”, i.e. for any outcome of the domain. The classical Kahneman and Tversky (1979, page 279) loss aversion definition follows as a special case.File | Dimensione | Formato | |
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