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Modeling Decision-Maker Preferences through Utility Function Level Sets

  • Luciana R. Pedro
  • Ricardo H. C. Takahashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)

Abstract

In this paper, we present a method based on the multiattribute utility theory to approximate the decision-maker preference function. A feature of the proposed methodology is its ability to represent arbitrary preference functions, including functions in which there are non-linear dependencies among different decision criteria. The preference information extracted from the decision-maker involves ordinal description only, and is structured using a partial ranking procedure. An artificial neural network is constructed to approximate the decision-maker preferences, reproducing the level sets of the underlying utility function. The proposed procedure can be useful when recurrent decisions are to be performed, with the same decision-maker over different sets of alternatives. It is shown here that the inclusion/exclusion of information causes only local rank reversals instead of large scale ones that may occur in several existing methodologies. The proposed method is also robust to relatively large levels of wrong answers of the decision maker.

Keywords

Multicriteria decision analysis MAUT artificial neural networks utility function 

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References

  1. 1.
    Angilella, S., Greco, S., Lamantia, F., Matarazzo, B.: Assessing non-additive utility for multicriteria decision aid. European Journal of Operational Research 158, 734–744 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Angilella, S., Greco, S., Matarazzo, B.: Non-additive robust ordinal regression: A multiple criteria decision model based on the Choquet integral. European Journal of Operational Research 201, 277–288 (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Braga, A.P., Takahashi, R.H.C., Costa, M.A., Teixeira, R.A.: Multi-objective algorithms for neural networks learning. In: Multi-Objective Machine Learning, pp. 151–171 (2006)Google Scholar
  4. 4.
    Brans, J.P.: L’ingénièrie de la décision; Elaboration d’instruments d’aide à la décision. La méthode PROMETHEE. In: Nadeau, R., Landry, M. (eds.) L’aide à la décision: Nature, Instruments et Perspectives d’Avenir, Québec, Canada, pp. 183–213. Presses de l’Université Laval (1982)Google Scholar
  5. 5.
    Chen, J., Lin, S.: An interactive neural network-based approach for solving multiple criteria decision-making problems. Decision Support Systems 36, 137–146 (2003)CrossRefGoogle Scholar
  6. 6.
    Edwards, W., Hutton Barron, F.: SMART and SMARTER: Improved simple methods for multiattribute utility measurement. Organizational Behavior and Human Decision Processes 60(3), 306–325 (1994)CrossRefGoogle Scholar
  7. 7.
    Fishburn, P.C.: Additive utilities with incomplete product set: Applications to priorities and assignments. Operations Research (1967)Google Scholar
  8. 8.
    Keeney, R.L., Raiffa, H.: Decisions with multiple objectives: Preferences and value tradeoffs. J. Wiley, New York (1976)zbMATHGoogle Scholar
  9. 9.
    Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1/2), 81–93 (1938)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kwiesielewicz, M., Van Uden, E.: Inconsistent and contradictory judgements in pairwise comparison method in the AHP. Comput. Oper. Res. 31(5), 713–719 (2004)CrossRefzbMATHGoogle Scholar
  11. 11.
    Li, S.: Rank reversal properties of multicriteria decision making models. Master’s thesis, University of Birmingham (July 2008)Google Scholar
  12. 12.
    Miller, D.W., Starr, M.K.: Executive decisions and operations research. Prentice-Hall, Englewood Cliffs (1960)Google Scholar
  13. 13.
    Roy, B.: Classement et choix en présence de points de vue multiples: La méthode ELECTRE. Revue Francaise d’Informatique et de Recherche Opérationnelle 8, 57–75 (1968)CrossRefGoogle Scholar
  14. 14.
    Saaty, T.L.: A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15, 234–281 (1977)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Saaty, T.L.: Decision Making with Dependence and Feedback The Analytic Network Process. RWS Publications, Pittsburgh (1996)Google Scholar
  16. 16.
    Teixeira, R.A., Braga, A.P., Takahashi, R.H.C., Saldanha, R.R.: Improving generalization of mlps with multi-objective optimization. Neurocomputing 35(1-4), 189–194 (2000)CrossRefzbMATHGoogle Scholar
  17. 17.
    Wang, X.: Study of ranking irregularities when evaluating alternatives by using some ELECTRE methods and a proposed new MCDM method based on regret and rejoicing. Master’s thesis, Louisiana State University (May 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luciana R. Pedro
    • 1
  • Ricardo H. C. Takahashi
    • 2
  1. 1.Department of Electrical EngineeringUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Department of MathematicsUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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