Discrete Arrow–Pratt indexes for risk and uncertainty
提出一种可直接从选择数据中获得的离散Arrow–Pratt指数及其相对版本，该方法非参数、适用于风险与不确定性，且对概率变换等稳健，可用于刻画多种决策模型。
Abstract The Arrow–Pratt index, a gold standard in studies of risk attitudes, is not directly observable from choice data. Existing methods to measure it rely on parametric assumptions. We introduce a discrete Arrow–Pratt index, and its relative counterpart, that can be directly obtained from choices. Our approach is general: it is (i) non-parametric, (ii) applicable to both risk and uncertainty, (iii) and robust to probability transformation, non-additive beliefs and multiple priors. Our index can also be used to characterize various decision models through various simple consistency requirements. We analyze its properties and demonstrate how it can be measured.