Mixture Choice Data: Revealing Preferences and Cognition
研究了混合选择数据（包含群体选择联合分布）如何同时识别异质性偏好与认知因素，并扩展到随机满意阈值和量化费希纳选择模型。
Mixture choice data consist of the joint distribution of choices of a group of agents from a collection of menus, comprising the implied stochastic choice function plus any cross-menu correlations. When agents are heterogeneous with respect to both preferences and other aspects of cognition, we show that these two determinants of behavior are identified simultaneously by suitable mixture choice data. We also demonstrate how this finding can be extended to allow for specialized assumptions about cognition, focusing on models of random satisficing thresholds and “quantal Fechnerian” choice.