Random Choice and Differentiation
Coauthor: Junnan He
Abstract:
Differentiation determines the comparability of different options and can be crucial to predict how choice architecture elicits behavioral responses. To facilitate the measurement of differentiation, we develop a flexible yet tractable model of random choice in a multi-attribute setting. We show the analyst can separately identify vertical and horizontal differentiation from binary comparison data alone. We characterize the binary choice rules that arise from our model using four easily understood postulates. In multinomial choice, we show that the intersection of our model with the classic random utility framework yields random coefficients with an elliptical distribution. We provide applications to consumer demand with differentiated products and to measuring the complexity faced by an agent in individual decision-making problems.
Organized by: Li Hao
