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Evolutionary Preference/Utility Functions: A Dynamic Perspective

Published online by Cambridge University Press:  01 January 2025

Wayne S. DeSarbo ,
Duncan K. H. Fong ,
John Liechty  and
Jennifer Chang Coupland
Wayne S. DeSarbo*
Affiliation:
Pennsylvania State University
Duncan K. H. Fong
Affiliation:
Pennsylvania State University
John Liechty
Affiliation:
Pennsylvania State University
Jennifer Chang Coupland
Affiliation:
Pennsylvania State University
*
Requests for information or reprints can be sent to: Wayne S. DeSarbo, Marketing Department, Smeal College of Business, Pennsylvania State University, University Park, PA. 16802, USA, E-Mail: desarbows@aol.com
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Abstract

The collection of repeated measures in psychological research is one of the most common data collection formats employed in survey and experimental research. The behavioral decision theory literature documents the existence of the dynamic evolution of preferences that occur over time and experience due to learning, exposure to additional information, fatigue, cognitive storage limitations, etc. We introduce a Bayesian dynamic linear methodology employing an empirical Bayes estimation framework that permits the detection and modeling of such potential changes to the underlying preference utility structure of the respondent. An illustration of revealed stated preference analysis (i.e., conjoint analysis) is given involving students’ preferences for apartments and their underlying attributes and features. We also present the results of several simulations demonstrating the ability of the proposed procedure to recover a variety of different sources of dynamics that may surface with preference elicitation over repeated sequential measurement. Finally, directions for future research are discussed.

Keywords

preference analysisdynamic modelsbayesian analysissequential measurement

Information

Type
Original Paper
Information
Psychometrika , Volume 70 , Issue 1 , March 2005 , pp. 179 - 202
Copyright
Copyright © 2005 The Psychometric Society

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Footnotes

The authors wish to acknowledge and thank the Editor, the Associate Editor, and two anonymous reviewers for their constructive and insightful comments. Duncan K.H. Fong’s work was sponsored in part by a research grant from the Smeal College.

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