Mixture Choice Data: Revealing Preferences and Cognition
Abstract: 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 cognition, we show that these two components of behavior can be revealed simultaneously by appropriate mixture choice data. We then extend this finding to stochastic consideration sets, random satisficing thresholds, multinomial logit, and Fechnerian models of cognition. Finally, we demonstrate how the mixture choice framework can be used by applying it to a preexisting experimental dataset.