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Estimating Utility Functions in the Presence of Response Error

Author

Listed:
  • Kathryn Blackmond Laskey

    (Decision Science Consortium, Inc., Falls Church, Virginia 22043)

  • Gregory W. Fischer

    (Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

This paper explores the nature and extent of response error when direct multiattribute utility assessment procedures are used as a basis for modeling preferences for risky multiattribute alternatives. The analysis is based on an experimental study of preferences for alternative air pollution control policies whose consequences were characterized by three value attributes: cost to consumers, level of pollution related illness, and level of pollution related mortality. The study generated the following findings: (i) direct assessments of preferences for outcomes were quite reliable and stable over a two-week time period; (ii) parameter estimates for additive utility functions fitted to direct utility assessments were both precise and stable over a two-week time period; (iii) statistically fitted additive utility models provided very accurate predictions of directly assessed preferences two weeks later (or earlier); (iv) ranking outcomes before assigning utilities to them resulted in high levels of serial correlation of errors in direct assessments; and (v) using a parameter estimation procedure that adjusted for serial correlation of errors had little effect on the accuracy of the model's predictions of preferences in a different time period.

Suggested Citation

  • Kathryn Blackmond Laskey & Gregory W. Fischer, 1987. "Estimating Utility Functions in the Presence of Response Error," Management Science, INFORMS, vol. 33(8), pages 965-980, August.
    • Handle: RePEc:inm:ormnsc:v:33:y:1987:i:8:p:965-980
    DOI: 10.1287/mnsc.33.8.965
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    Citations

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    Cited by:

    1. John C. Butler & James S. Dyer & Jianmin Jia, 2006. "Using Attributes to Predict Objectives in Preference Models," Decision Analysis, INFORMS, vol. 3(2), pages 100-116, June.
    2. Gregory W. Fischer & Jianmin Jia & Mary Frances Luce, 2000. "Attribute Conflict and Preference Uncertainty: The RandMAU Model," Management Science, INFORMS, vol. 46(5), pages 669-684, May.
    3. Zanakis, Stelios H. & Mandakovic, Tomislav & Gupta, Sushil K. & Sahay, Sundeep & Hong, Sungwan, 1995. "A review of program evaluation and fund allocation methods within the service and government sectors," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 59-79, March.
    4. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    5. Fry, Phillip C. & Rinks, Dan B. & Ringuest, Jeffrey L., 1996. "Comparing the predictive validity of alternatively assessed multi-attribute preference models when relevant decision attributes are missing," European Journal of Operational Research, Elsevier, vol. 94(3), pages 599-609, November.
    6. Gregory W. Fischer & Mary Frances Luce & Jianmin Jia, 2000. "Attribute Conflict and Preference Uncertainty: Effects on Judgment Time and Error," Management Science, INFORMS, vol. 46(1), pages 88-103, January.
    7. Lahtinen, Tuomas J. & Hämäläinen, Raimo P., 2016. "Path dependence and biases in the even swaps decision analysis method," European Journal of Operational Research, Elsevier, vol. 249(3), pages 890-898.
    8. Philippe Delquié, 2003. "Optimal Conflict in Preference Assessment," Management Science, INFORMS, vol. 49(1), pages 102-115, January.
    9. Olivier Toubia & Eric Johnson & Theodoros Evgeniou & Philippe Delquié, 2013. "Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters," Management Science, INFORMS, vol. 59(3), pages 613-640, June.
    10. Zafar Hakim & Dev S. Pathak, 1999. "Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling," Health Economics, John Wiley & Sons, Ltd., vol. 8(2), pages 103-116, March.

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