A discrete choice modeling framework of heterogenous decision rules accounting for non-trading behavior

We present a discrete choice modeling framework with heterogeneous decision rules accounting for non-trading behavior. The proposed approach builds upon the state-of-the-art probabilistic finite mixture models and tackles non-trading behavior while accounting for inertia effects and serial correlation in the SP data, and contextual effects on the probability of an individual employing a specific decision rule. The framework involves three subpopulations of decision-makers, referred to respectively as pure utility-maximizers, utility-maximizers with strong preference for one alternative, and non-traders non-utility-maximizers employing a non-trading heuristic. The second subpopulation is expected to exhibit non-trading behavior, despite making trade-offs consistent with utility maximization. Our goal is to disentangle the two types of manifested non-trading behavior. We assume that the manifestation of non-trading behavior – by otherwise utility-maximizing individuals – may be driven by important context variables. In order to accommodate this assumption in the modeling framework, we define and add a relative advantage (RA) component in the class-membership model. Finally, we apply the framework to a Swiss stated preferences (SP) mode choice case study, and demonstrate the impact of accounting for non-trading behavior on the value of time estimates.