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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.

References

Addelman, S. (1962). Orthogonal main-effect plans for asymmetrical factorial experiments. Technometrics, 4, 21–46.CrossRefGoogle Scholar
Aguilar, O., & West, M. (2000). Bayesian dynamic factor models and portfolio allocation. Journal of Business and Economic Statistics, 18, 338–357.CrossRefGoogle Scholar
Anderson, N.H. (1971). Integration theory and attitude change. Psychological Review, 78, 171–206.CrossRefGoogle Scholar
Anderson, N.H. (1981). Foundations of information integration theory. San Diego, CA: Academic Press.Google Scholar
Bechtel, G.G. (1976). Multidimensional preference scaling. The Hague, Netherlands: Mouton.CrossRefGoogle Scholar
Berlyne, D.E. (1960). Conflict, Arousal and Curiosity. New York: Mc-Graw Hill.CrossRefGoogle Scholar
Berlyne, D.E. (1974). Studies in the new experimental aesthetics. Washington, D.C.: Hemisphere.Google Scholar
Bernardo, J.M. (1979). Reference posterior distributions for Bayesian inference (with discussion) Journal of the Royal Statistical Society Ser. Journal of the Royal Statistical Society Ser. B, 41, 113–148.CrossRefGoogle Scholar
Bhattacharjee, B., Sinha, K.C., & Krogmeier, J.V. (2001). Modeling the effects of traveler information on freeway origin-destination demand prediction. Transportation Research Part C-Emerging Technologies, 9, 381–398.CrossRefGoogle Scholar
Brickman, P., & D’Amato, B. (1975). Exposure effects in a free-choice situation. Journal of Personality and Social Psychology, 32 Sept415–420.CrossRefGoogle Scholar
Cacioppo, J.T., & Petty, R.E. (1979). Effects of message repetition and position of cognition response, recall, and persuasion. Journal of Personality and Social Psychology, 37 January97–109.CrossRefGoogle Scholar
Cargnoni, C., Muller, P., & West, M. (1997). Bayesian forecasting of multinomial time series through conditionally Gaussian dynamic models. Journal of the American Statistical Association, 92, 640–647.Google Scholar
Carroll, J.D. (1969). Categorical conjoint measurement Meeting of Mathematical Psychology. MI: Ann Arbor.Google Scholar
Cook, D.T., & Campbell, D.T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Boston: Houghton-Mifflin.Google Scholar
Fischhoff, B., Slovic, P., & Lichtenstein, S. (1980). Knowing what you want: Measuring labile values. In Wallstein, T. (Eds.), Cognitive processes in choice and decision behavior (pp. 117–141). Hillsdale, NJ: Erlbaum.Google Scholar
Fruhwirth-Schnatter, S. (2001). Markov chain monte-carlo estimation of classical and dynamic switching and mixture models. Journal of the American Statistical Association, 96, 194–209.CrossRefGoogle Scholar
Gelman, A., Carlin, J.B., Stern, H.S., & Rubin, D.B. (1997). Bayesian data analysis. London: Chapman and Hall.Google Scholar
Gilbert, D.T. (1989). Thinking lightly about others: Automatic components of the social inference process. In Uleman, J.S., & Bargh, J.A. (Eds.), Unintended Thought (pp. 189–211). New York: Guilford.Google Scholar
Gilbert, D.T., Krull, D.S., & Pelham, B.W. (1988). Of thoughts unspoken: Social inference and the self-regulation of behavior. Journal of Personality and Social Psychology, 55, 685–694.CrossRefGoogle Scholar
Green, P.E. (1974). On the design of choice experiments involving multifactor alternatives. Journal of Consumer Research, 1, 61–68.CrossRefGoogle Scholar
Green, P.E., Helsen, K., & Shandler, B. (1988). Conjoint internal validity under alternative profile presentations. Journal of Consumer Research, 15, 392–397.CrossRefGoogle Scholar
Green, P.E., & Rao, V.R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355–363.Google Scholar
Hair, J.E., Anderson, R.E., Tatham, R.L., & Black, W.C. (1995). Multivariate data analysis (4th ed.). Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Herr, P.M. (1989). Priming price: Prior knowledge and context effects. Journal of Consumer Research, 16, 67–75.CrossRefGoogle Scholar
Huber, J., Wittink, D.R., Johnson, R. M., & Miller, R. (1992) Learning effects in preference tasks: Choice-based versus standard conjoint. In Sawtooth Software Conference Proceedings, Sequim, WA: Sawtooth Software, Inc.Google Scholar
Huber, J., Hansen, D. (1986). Testing the impact of dimensional complexity and affective differences of paired concepts in adaptive conjoint analysis. In Wallendorf, M., Anderson, P. (Eds.), Advances in Consumer Research (Vol. 14) (pp. 159–163). Provo, UT: Association for Consumer Research.Google Scholar
Huynh, H. (1980). A nonrandomized Minimax solution for passing scores in the binomial error model. Psychometrika, 45, 167–182.CrossRefGoogle Scholar
Jeffreys, H. (1961). Theory of Probability (3rd ed.). London: Oxford University Press.Google Scholar
Johar, G.V., Jedidi, K., & Jacoby, J. (1997). A varying-parameter averaging model of on-line brand evaluations. Journal of Consumer Research, 24, 232–246.CrossRefGoogle Scholar
Johnson, E.J., & Meyer, R.J. (1984). Compensatory choice models of noncompensatory processes: The effect of varying context. Journal of Consumer Research, 11, 528–541.CrossRefGoogle Scholar
Johnson, R.M., & Orme, B.K. (1996). How many questions should you ask in choice-based conjoint studies? Technical Report Sequim. Sequim, WA: Sawtooth Software, Inc.Google Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision making under risk. Econometrica, 47, 263–291.CrossRefGoogle Scholar
Kardes, F.R., & Kalyanaram, G. (1992). Order-of-entry effects on consumer memory and judgment: An information integration perspective. Journal of Marketing Research, 29, 343–357.CrossRefGoogle Scholar
Kass, R., & Raftery, A.E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 40–60.CrossRefGoogle Scholar
Kruskal, J.B. (1965). Analysis of factorial experiments by estimating monotone transformations of the data. Journal of the Royal Statistical Society, Series B27, 251–263.CrossRefGoogle Scholar
Lopes, L. L. (1982) Toward a procedural theory of judgment. Unpublished doctoral dissertation, University of WisconsinGoogle Scholar
Louviere, J.J., Hensher, D.A., & Swait, J.D. (2000). Stated choice models: Analysis and application. Cambridge, England: Cambridge University Press.CrossRefGoogle Scholar
Luce, R.D., & Tukey, J.W. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1, 1–27.CrossRefGoogle Scholar
March, J.G. (1978). Bounded rationality, ambiguity, and the engineering of choice. Bell Journal of Economics, 9, 587–608.CrossRefGoogle Scholar
Meyer, R.J. (1987). The learning of multiattribute judgment policies. Journal of Consumer Research, 14, 155–173.CrossRefGoogle Scholar
Moore, D.A. (1999). Order effects in preference judgments: evidence for context dependence in the generation of preferences. Organizational Behavior and Human Decision Processes, 78, 146–165.CrossRefGoogle ScholarPubMed
Pole, A., West, M., & Harrison, J. (1994). Applied bayesian forecasting and time series analysis. New York: Chapman and Hall.CrossRefGoogle Scholar
Prado, R., West, M., & Krystal, A.D. (1999). Multi-channel EEG analyses via dynamic regression models with time-varying lag lead structure. Applied Statistics, 50, 95–110.Google Scholar
Simonson, I., & Tversky, A. (1992). Choice in context: tradeoff contrast and extremeness aversion. Journal of Marketing Research, 29, 281–295.CrossRefGoogle Scholar
Slovic, P., Griffin, D., & Tversky, A. (1990). Compatibility affects in judgment and choice. In Hogarth, R.M. (Eds.), Insights in decision making: A tribute to Hillel J. Einhorn. Chicago: University of Chicago Press.Google Scholar
Tonellato, S.F. (2001). A multivariate time series model for the analysis and prediction of carbon monoxide atmospheric concentrations. Journal of the Royal Statistical Society Series C-Applied Statistics, 50, 187–200.CrossRefGoogle Scholar
Torgerson, W.S. (1958). Theory and methods of scaling. New York: Wiley.Google Scholar
West, M., & Harrison, J. (1997). Bayesian forecasting and dynamic models. New York: Springer.Google Scholar
Young, F.W. (1969). Polynomial conjoint analysis of similarities: Definitions for a special algorithm. Research Paper No. 76. University of North Carolina: Psychometric Laboratory.Google Scholar
Zajonc, R.B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology Monograph Supplement, 9 (Part 21–28.CrossRefGoogle Scholar
Zajonc, R.B., Markus, H., & Wilson, W.R. (1974). Exposure affects and associative learning. Journal of Experimental Psychology, 10, 248–263.Google Scholar