Random Choice and Learning
提出贝叶斯概率单位模型，将情境依赖选择解释为信息不完美代理人对选项比较难度的最优反应，能识别稳定偏好并解释吸引效应和折中效应，在青蛙交配数据中拟合和预测优于随机效用模型。
Context-dependent individual choice challenges the principle of utility maximization. I explain context dependence as the optimal response of an imperfectly informed agent to the ease of comparison of the options. I introduce a discrete choice model, the Bayesian probit, which allows the analyst to identify stable preferences from context-dependent choice data. My model accommodates observed behavioral phenomena--including the attraction and compromise effects--that lie beyond the scope of any random utility model. I use data from frog mating choices to illustrate how the model can outperform the random utility framework in goodness of fit and out-of-sample prediction.