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

Evanthia Kazagli and Matthieu de Lapparent

Journal of choice modelling, 2023, vol. 48, issue C

Abstract: 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.

Keywords: Contextual effects; Discrete choice modeling; Non-trading behavior; Probabilistic decision process models; Relative advantage; SP data (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:48:y:2023:i:c:s1755534523000143

DOI: 10.1016/j.jocm.2023.100413

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