Document Type

Article

Publication Date

2024

DOI

10.3390/stats7010012

Publication Title

Stats

Volume

7

Issue

1

Pages

185-202

Abstract

Discrete choice models (DCMs) are applied in many fields and in the statistical modelling of consumer behavior. This paper focuses on a form of choice experiment, best-worst scaling in discrete choice experiments (DCEs), and the transition probability of a choice of a consumer over time. The analysis was conducted by using simulated data (choice pairs) based on data from Flynn's (2007) 'Quality of Life Experiment'. Most of the traditional approaches assume the choice alternatives are mutually exclusive over time, which is a questionable assumption. We introduced a new copula-based model (CO-CUB) for the transition probability, which can handle the dependent structure of best-worst choices while applying a very practical constraint. We used a conditional logit model to calculate the utility at consecutive time points and spread it to future time points under dynamic programming. We suggest that the CO-CUB transition probability algorithm is a novel way to analyze and predict choices in future time points by expressing human choice behavior. The numerical results inform decision making, help formulate strategy and learning algorithms under dynamic utility in time for best-worst DCEs.

Rights

© 2024 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "Data was simulated."

Original Publication Citation

Adikari, S., & Diawara, N. (2024). Utility in time description in priority best-worst discrete choice models: An empirical evaluation using Flynn's data. Stats, 7(1), 185-202. https://doi.org/10.3390/stats7010012

ORCID

0000-0001-6006-1591 (Adikari), 0000-0002-8403-6793 (Diawara)

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