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Evolutionary preference/utility functions: A dynamic perspective

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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.

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Author information

Correspondence to Wayne S. DeSarbo.

Additional information

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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DeSarbo, W.S., Fong, D.K.H., Liechty, J. et al. Evolutionary preference/utility functions: A dynamic perspective. Psychometrika 70, 179–202 (2005). https://doi.org/10.1007/s11336-002-0976-x

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Keywords

  • preference analysis
  • dynamic models
  • bayesian analysis
  • sequential measurement