抄録
The probability weighting function is a quantitative model that captures people's responses to risk as a non-linear bias to probability. Various fields such as psychology, economics, and management science apply this function. However, the one-parameter functional form in Tversky and Kahneman’s cumulative prospect theory (CPT) is not capable of predicting the common consequence elimination case originally presented by Allais. Additionally, it cannot model the reference dependency in this problem. Therefore, we proposed a series of computational experiments of a modified version of CPT with a beta distribution; that is, an incomplete beta ratio and other functional forms, including generalized hyperbolic discounting, instead of the original probability weighting function used in CPT. The results reveal that the modified CPT with a beta distribution can reproduce a typical choice pattern in the Allais paradox and reference dependency under more natural inverse S-shaped curvature.
