Dynamic discrete choice model and its estimation algorithm with dynamic inconsistent expected utility in an uncertain situation

Abstract

Decision making in terms of travel behavior is always dynamic; however, the future behavior of an individual is often inconsistent with their present plan. Therefore, this study aims to incorporate dynamic inconsistency into the expected utility of dynamic sequential choices with a novel structural estimation approach. The incorporation of dynamic heterogeneity into expected utility enables the illustrative modeling of more types of decision making because decision making often involves dynamic inconsistent expected utilities. However, the existing approaches for dynamic discrete choice suppose an equilibrium situation and cannot evaluate these dynamic inconsistent situations. In this study, we propose a novel dynamic discrete choice model with dynamic inconsistency in its expected utility and an empirical algorithm for parameter estimation. We formulate a heterogeneous expected utility as a range constraint near the dynamic consistent expected utility for model identification. Our algorithm, which applies a mathematical program to inequality constraints, enables parameter estimation with dynamic heterogeneity. The proposed model and algorithm were applied to the problem of evacuation departure choice in the city of Rikuzentakata during the Great East Japan Earthquake and Tsunami of 2011. The case study demonstrated the validity of introducing dynamic heterogeneity via likelihood comparisons; it was also found that dynamic heterogeneity is distributed around the dynamic consistent expected utility.