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Preferences for Proxy Attributes

Author

Listed:
  • Gregory W. Fischer

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

  • Nirmala Damodaran

    (School of Urban and Public Affairs, Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213)

  • Kathryn B. Laskey

    (Decision Science Consortium, Falls Church, Virginia)

  • David Lincoln

    (CH2M Hill, Bellevue, Washington)

Abstract

A proxy attribute is an indirect measure of an ultimate decision objective. Keeney and Raiffa (Keeney, R. L., H. Raiffa. 1976. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. John Wiley, New York.) argued that assessing utility functions over proxy attributes requires complex inferences that may exceed the human capacity for consistent judgment, thus biasing utility assessments. This hypothesis was tested in an experimental study of preferences for pollution control alternatives. Each decision maker made two sets of utility assessments: the first regarding outcomes described by the fundamental attributes "pollution control cost" and "pollution related illness"; the second regarding outcomes described by the fundamental attribute "pollution control cost" and the proxy attribute "pollution emissions level" (which served as an indirect measure of illness). The subjects displayed a near universal bias to overweight the proxy attribute relative to the prescriptions of expected utility theory. The bias was large, resulting in a substantial loss of expected utility in a simulated policy decision making scenario. To account for this bias, we developed three heuristic models of preferences for proxy attributes: the best guess, worst case, and relative importance models. The results strongly favored the relative importance model, according to which decision makers assess scaling constants by relying on general attitudes regarding the relative importance of different decision objectives rather than on well-articulated preferences for rates of substitution between pairs of attributes.

Suggested Citation

  • Gregory W. Fischer & Nirmala Damodaran & Kathryn B. Laskey & David Lincoln, 1987. "Preferences for Proxy Attributes," Management Science, INFORMS, vol. 33(2), pages 198-214, February.
    • Handle: RePEc:inm:ormnsc:v:33:y:1987:i:2:p:198-214
    DOI: 10.1287/mnsc.33.2.198
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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. Marit Kragt & Jeffrey Bennett, 2012. "Attribute Framing in Choice Experiments: How Do Attribute Level Descriptions Affect Value Estimates?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 51(1), pages 43-59, January.
    3. Ralph L. Keeney, 2002. "Common Mistakes in Making Value Trade-Offs," Operations Research, INFORMS, vol. 50(6), pages 935-945, December.
    4. Suk, Kwanho & Yoon, Song-Oh, 2012. "The moderating role of decision task goals in attribute weight convergence," Organizational Behavior and Human Decision Processes, Elsevier, vol. 118(1), pages 37-45.
    5. Manel Baucells & Rakesh K. Sarin, 2003. "Group Decisions with Multiple Criteria," Management Science, INFORMS, vol. 49(8), pages 1105-1118, August.
    6. Marttunen, Mika & Belton, Valerie & Lienert, Judit, 2018. "Are objectives hierarchy related biases observed in practice? A meta-analysis of environmental and energy applications of Multi-Criteria Decision Analysis," European Journal of Operational Research, Elsevier, vol. 265(1), pages 178-194.
    7. James S. Dyer & James E. Smith, 2021. "Innovations in the Science and Practice of Decision Analysis: The Role of Management Science," Management Science, INFORMS, vol. 67(9), pages 5364-5378, September.
    8. 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.
    9. Gilberto Montibeller & Detlof von Winterfeldt, 2015. "Cognitive and Motivational Biases in Decision and Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1230-1251, July.
    10. Robin L. Dillon & Vicki M. Bier & Richard Sheffield John & Abdullah Althenayyan, 2023. "Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic," Decision Analysis, INFORMS, vol. 20(2), pages 109-132, June.
    11. Tobias Stangl & Ulrich W. Thonemann, 2017. "Equivalent Inventory Metrics: A Behavioral Perspective," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 472-488, July.
    12. James E. Smith & Detlof von Winterfeldt, 2004. "Anniversary Article: Decision Analysis in Management Science," Management Science, INFORMS, vol. 50(5), pages 561-574, May.
    13. L. Robin Keller & Craig W. Kirkwood & Nancy S. Jones, 2010. "Assessing stakeholder evaluation concerns: An application to the Central Arizona water resources system," Systems Engineering, John Wiley & Sons, vol. 13(1), pages 58-71, March.
    14. Chinander, Karen R. & Schweitzer, Maurice E., 2003. "The input bias: The misuse of input information in judgments of outcomes," Organizational Behavior and Human Decision Processes, Elsevier, vol. 91(2), pages 243-253, July.

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