Predicting insurance demand from risk attitudes
通过实验测量1730人的风险态度参数，并用17种结构模型预测保险选择，发现所有模型预测效果差于随机预测，原因是模型对价格敏感度过高、对概率变化反应方向错误。
Abstract Can measured risk attitudes and associated structural models predict insurance demand? In an experiment ( n = 1730), we elicit measures of utility curvature, probability weighting, loss aversion, and preference for certainty and use them to parameterize seventeen common structural models (e.g., expected utility, cumulative prospect theory). Subjects also make 12 insurance choices over different loss probabilities and prices. The insurance choices show coherence and some correlation with various risk‐attitude measures. Yet all the structural models predict insurance poorly, often less accurately than random predictions. This is because established structural models predict opposite reactions to probability changes and more sensitivity to prices than people display. Approaches that temper the price responsiveness of structural models show more promise for predicting insurance choices across different conditions.