Bounded Rationality Choice Model Incorporating Attribute Threshold, Mental Effort, and Risk Attitude: Illustration to Pedestrian Walking Direction Choice Decision in Shopping Streets

Abstract

Discrete choice models have been widely used in modeling individual choice behavior. By assuming that people are rational and utility-maximizers, these models are effective in capturing underlying preferences and choice outcornes. However, these models are not explicitly developed to represent decision processes. On the other hand, models of bounded rationality, for the majority based on decision heuristics, are meant to be more realistic representations of decision processes based on the assumption that people have limited mental capacity for making a decision. People may apply different heuristics for a decision. To reflect this idea, we first introduce a conceptual framework which articulates (i) that individuals use attribute thresholds to map external continuous factors into discrete internal representations, (ii) information search costs effort which is considered important for individual selecting heuristics, and (iii) individual's risk attitude towards heuristics, reflecting expected outcome diversity implied in a heuristic decision. The framework is general enough to identify several typical heuristics, including conjunctive, disjunctive and lexicographic rules. A decision can be modeled as a two-stage process, first choosing a heuristic, and then applied it to reach a decision. To illustrate this approach, it is applied to a dataset about pedestrians choosing walking directions in a shopping street in Shanghai, China. Results about parameter estimates and estimated probabilities of applying different heuristics are presented. Implications of the model results are discussed.