Measuring Probabilistic Risk Attitudes
提出了衡量概率风险态度中吸引力（动机因素）和可辨别性（认知因素）的正式度量方法，这些方法数学上易处理、对不连续性稳健，并适用于任何权重函数及大小概率。
We introduce formal measures for two psychological factors of probabilistic risk attitudes: attractiveness (motivational factor) and discriminability (cognitive factor). Unlike previous approaches that relied on heuristic proxies, our measures precisely capture these two fundamental factors. Our measures are mathematically tractable, robust to discontinuities, such as in the NEO-additive case, and flexible to be applied to any weighting function, as well as to both small and large probabilities. Additionally, through detailed numerical analysis, we examine to what extent existing weighting function parameters capture the two factors: attractiveness and discriminability. Finally, using these new measures, we provide a formal understanding of the independence between motivational and cognitive factors. This paper was accepted by Jack Soll, behavioral economics and decision analysis. Funding: The research was made possible by the financial support from Brazilian Agency of Research and Innovation (Finep), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) [Grants PROJ–CAPES PRINT 1033427P] and PUC-Rio. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.04870 .