Date of Award

Spring 5-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Mathematics & Statistics

Program/Concentration

Computational and Applied Mathematics

Committee Director

Norou Diawara

Committee Member

Sandipan Dutta

Committee Member

Kayoung Park

Committee Member

Ross Gore

Abstract

Models for discrete choice experiments (DCE) are frequently used to analyze consumer choices about products and services. A family of DCE, best-worst scaling experiments, offers more in-depth insights into consumer preferences by eliciting a best and worst choice from a set of options, rather than just a single preference. Traditional approaches often assume that choices are mutually exclusive over time, which may not always be the case. This dissertation proposes a novel model for DCE that takes into account the changing nature of consumer choices over time and the priority constraint of transition probabilities. The model introduces a copula combination of uniform and shifted binomial distribution (CO-CUB) to model the dependence structure in best-worst choice pairs. CO-CUB also allows for the modeling of latent factors such as uncertainty and consumer feelings in the decision-making process. The proposed priority constraint considers that human choice behaviors generally favor options closer to the ordinal structure of choices. The impact of choice pairs over consecutive time periods is characterized using a dynamic Markov decision process. The utility function of a choice pair over consecutive time periods is described, along with the best-worst choice pairs in a network. Compared to classical K-means classification, the proposed approach demonstrates its effectiveness using simulated data and a transportation industry example, providing a more comprehensive understanding of consumer preferences and decision-making behaviors while accounting for priority constraints of transition probabilities.

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DOI

10.25777/mtem-6473

ISBN

9798379734442

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