Bayesian Identification: A Theory for State-Dependent Utilities
提出一种揭示偏好方法，用于识别随状态变化的信念和效用，并引入比标准Blackwell排序更弱的比较信息性概念，通过随机选择实现识别，适用于招聘、贷款和医疗建议中的偏差分析。
We provide a revealed preference methodology for identifying beliefs and utilities that can vary across states. A notion of comparative informativeness is introduced that is weaker than the standard Blackwell ranking. We show that beliefs and state-dependent utilities can be identified using stochastic choice from two informational treatments, where one is strictly more informative than another. Moreover, if the signal structure is known, then stochastic choice from a single treatment is enough for identification. These results illustrate novel identification methodologies unique to stochastic choice. Applications include identifying biases in job hiring, loan approvals, and medical advice.