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Utility Functions for Multiattributed Consequences

Author

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  • Ralph L. Keeney

    (Massachusetts Institute of Technology)

Abstract

One of the important methodological problems concerning the application of decision analysis to complex problems is the restrictiveness of existing techniques for systematically assessing multiattributed utility functions valid for decision making under uncertainty. This is the problem addressed in this paper. Operational assumptions are postulated about the decision maker's preferences for multiattributed consequences, and functional forms of utility functions satisfying these assumptions are derived. The results are a number of representation theorems which simplify the assessment of the utility function provided the requisite assumptions hold. A special case of these results is the well-known additive utility function. A procedure to verify the appropriateness of the necessary assumptions is included.

Suggested Citation

  • Ralph L. Keeney, 1972. "Utility Functions for Multiattributed Consequences," Management Science, INFORMS, vol. 18(5-Part-1), pages 276-287, January.
  • Handle: RePEc:inm:ormnsc:v:18:y:1972:i:5-part-1:p:276-287
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    File URL: http://dx.doi.org/10.1287/mnsc.18.5.276
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    Cited by:

    1. de Almeida, Jonatas Araujo & Costa, Ana Paula Cabral Seixas & de Almeida-Filho, Adiel Teixeira, 2016. "A new method for elicitation of criteria weights in additive models: Flexible and interactive tradeoffAuthor-Name: de Almeida, Adiel Teixeira," European Journal of Operational Research, Elsevier, vol. 250(1), pages 179-191.
    2. John W. Boudreau, 2004. "50th Anniversary Article: Organizational Behavior, Strategy, Performance, and Design in Management Science," Management Science, INFORMS, vol. 50(11), pages 1463-1476, November.
    3. Pierre Picard, 2016. "A Note on Health Insurance under Ex Post Moral Hazard," Risks, MDPI, Open Access Journal, vol. 4(4), pages 1-9, October.
    4. 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.
    5. Voola, Persis & A., Vinaya Babu, 2017. "Study of aggregation algorithms for aggregating imprecise software requirements’ priorities," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1191-1199.
    6. John R. Hauser, 1977. "Consumer Preference Axioms: Behavioral Postulates for Describing and Predicting Stochastic Choice," Discussion Papers 287, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    7. Zhang, Junyi & Kuwano, Masashi & Lee, Backjin & Fujiwara, Akimasa, 2009. "Modeling household discrete choice behavior incorporating heterogeneous group decision-making mechanisms," Transportation Research Part B: Methodological, Elsevier, vol. 43(2), pages 230-250, February.
    8. Zhang, Junyi & Timmermans, Harry J. P. & Borgers, Aloys, 2005. "A model of household task allocation and time use," Transportation Research Part B: Methodological, Elsevier, vol. 39(1), pages 81-95, January.
    9. Anderson, Jock R. & Hardaker, J. Brian, 1972. "An Appreciation of Decision Analysis in Management," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 40(04), December.
    10. Dillon, John L. & Perry, Chad, 1977. "Multiattribute Utility Theory, Multiple Objectives And Uncertainty In Ex Ante Project Evaluation," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 45, June.
    11. James E. Smith & Detlof von Winterfeldt, 2004. "Anniversary Article: Decision Analysis in Management Science," Management Science, INFORMS, vol. 50(5), pages 561-574, May.
    12. He, Ying & Huang, Rui-Hua, 2008. "Risk attributes theory: Decision making under risk," European Journal of Operational Research, Elsevier, vol. 186(1), pages 243-260, April.

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