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Target-Oriented Decision Analysis with Different Target Preferences

  • Hong-Bin Yan
  • Van-Nam Huynh
  • Yoshiteru Nakamori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5861)

Abstract

Decision maker’s behavioral aspects play an important role in human decision making, and this was recognized by the award of the 2002 Nobel Prize in Economics to Daniel Kahneman. Target-oriented decision analysis lies in the philosophical root of bounded rationality as well as represents the S-shaped value function. In most studies on target-oriented decision making, monotonic assumptions are given in advance to simplify the problems, e.g., the attribute wealth. However, there are three types of target preferences: “the more the better” (corresponding to benefit target preference), “the less the better” (corresponding to cost target preference), and equal/range targets (too much or too little is not acceptable). Toward this end, two methods have been proposed to model the different types of target preferences: cumulative distribution function (cdf) based method and level set based method. These two methods can both induce four shaped value functions: S-shaped, inverse S-shaped, convex, and concave, which represents decision maker’s psychological preference. The main difference between these two methods is that the level set based method induces a steeper value function than that by the cdf based method.

Keywords

Satisfactory-oriented decision S-shaped function Target-oriented decision analysis Cumulative distribution function Level set Target preference type 

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References

  1. 1.
    Abbas, A.E.: Maximum entropy utility. Oper. Res. 54(2), 277–290 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Berhold, M.H.: The use of distribution functions to represent utility functions. Manag. Sci. 19(7), 825–829 (1973)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bordley, R., Kirkwood, C.: Multiattribute Preference Analysis with Performance Targets. Oper. Res. 52(6), 823–835 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Bordley, R., LiCalzi, M.: Decision analysis using targets instead of utility functions. Decis. Econ. Finance 23(1), 53–74 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Castagnoli, E., LiCalzi, M.: Expected utility without utility. Theor. Decis. 41(3), 281–301 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Dubois, D., Foulloy, L., Mauris, G., Prade, H.: Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliab. Comput. 10(4), 273–297 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Garcia, J.N., Kutalik, Z., Cho, K.-H., Wolkenhauer, O.: Level sets and minimum volume sets of probability density functions. Int. J. Approx. Reason. 34(1), 25–47 (2003)zbMATHCrossRefGoogle Scholar
  8. 8.
    Grabisch, M.: A graphical interpretation of the choquet integral. IEEE Trans. Fuzzy Syst. 8(5), 627–631 (2000)CrossRefGoogle Scholar
  9. 9.
    Heath, C., Larrick, R.P., Wu, G.: Goals as Reference Points. Cognit. Psychol. 38(1), 79–109 (1999)CrossRefGoogle Scholar
  10. 10.
    Huynh, V.N., Nakamori, Y., Lawry, J.: A Probability-Based Approach to Comparison of Fuzzy Numbers and Applications to Target-Oriented Decision Making. IEEE Trans. Fuzzy Syst. 16(2), 371–387 (2008)CrossRefGoogle Scholar
  11. 11.
    Huynh, V.N., Nakamori, Y., Ryoke, M., Ho, T.: Decision making under uncertainty with fuzzy targets. Fuzzy Optim. Decis. Making 6(3), 255–278 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Kahneman, D., Tversky, A.: Prospect Theory: An analysis of decision under risk. Econometrica 47(2), 263–291 (1979)zbMATHCrossRefGoogle Scholar
  13. 13.
    Karpak, B., Zionts, S. (eds.): Multiple Criteria Decision Making and Risk Analysis Using Microcomputers. Springer, Berlin (1989)zbMATHGoogle Scholar
  14. 14.
    LiCalzi, M., Sorato, A.: The pearson system of utility functions. Eur. J. Oper. Res. 172(2), 560–573 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Liu, Y.K., Liu, B.: Expected value operator of random fuzzy variable and random fuzzy expected value models. Int. J. Uncertain. Fuzz. 11(2), 195–215 (2003)zbMATHCrossRefGoogle Scholar
  16. 16.
    Manski, C.F.: Ordinal utility models of decision making under uncertainty. Theor. Decis. 25(1), 79–104 (1988)CrossRefMathSciNetGoogle Scholar
  17. 17.
    von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1947)Google Scholar
  18. 18.
    Savage, L.J.: The Foundations of Statistics. John Wiley and Sons, New York (1954)zbMATHGoogle Scholar
  19. 19.
    Simon, H.A.: A Behavioral Model of Rational Choice. Q. J. Eeco. 69(1), 99–118 (1955)CrossRefGoogle Scholar
  20. 20.
    Todorov, A., Goren, A., Trope, Y.: Probability as a psychological distance: Construal and preferences. J. Eep. Soc. Psychol. 43(3), 473–482 (2007)CrossRefGoogle Scholar
  21. 21.
    Yan, H.B., Huynh, V.N., Murai, T., Nakamori, Y.: Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis. Inform. Sciences 178(21), 4080–4093 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hong-Bin Yan
    • 1
  • Van-Nam Huynh
    • 1
  • Yoshiteru Nakamori
    • 1
  1. 1.School of Knowledge ScienceJapan Advanced Institute of Science and TechnologyIshikawaJapan

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