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.
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References
Abbas, A.E.: Maximum entropy utility. Oper. Res. 54(2), 277–290 (2006)
Berhold, M.H.: The use of distribution functions to represent utility functions. Manag. Sci. 19(7), 825–829 (1973)
Bordley, R., Kirkwood, C.: Multiattribute Preference Analysis with Performance Targets. Oper. Res. 52(6), 823–835 (2004)
Bordley, R., LiCalzi, M.: Decision analysis using targets instead of utility functions. Decis. Econ. Finance 23(1), 53–74 (2000)
Castagnoli, E., LiCalzi, M.: Expected utility without utility. Theor. Decis. 41(3), 281–301 (1996)
Dubois, D., Foulloy, L., Mauris, G., Prade, H.: Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliab. Comput. 10(4), 273–297 (2004)
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)
Grabisch, M.: A graphical interpretation of the choquet integral. IEEE Trans. Fuzzy Syst. 8(5), 627–631 (2000)
Heath, C., Larrick, R.P., Wu, G.: Goals as Reference Points. Cognit. Psychol. 38(1), 79–109 (1999)
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)
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)
Kahneman, D., Tversky, A.: Prospect Theory: An analysis of decision under risk. Econometrica 47(2), 263–291 (1979)
Karpak, B., Zionts, S. (eds.): Multiple Criteria Decision Making and Risk Analysis Using Microcomputers. Springer, Berlin (1989)
LiCalzi, M., Sorato, A.: The pearson system of utility functions. Eur. J. Oper. Res. 172(2), 560–573 (2006)
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)
Manski, C.F.: Ordinal utility models of decision making under uncertainty. Theor. Decis. 25(1), 79–104 (1988)
von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1947)
Savage, L.J.: The Foundations of Statistics. John Wiley and Sons, New York (1954)
Simon, H.A.: A Behavioral Model of Rational Choice. Q. J. Eeco. 69(1), 99–118 (1955)
Todorov, A., Goren, A., Trope, Y.: Probability as a psychological distance: Construal and preferences. J. Eep. Soc. Psychol. 43(3), 473–482 (2007)
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)
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Yan, HB., Huynh, VN., Nakamori, Y. (2009). Target-Oriented Decision Analysis with Different Target Preferences. In: Torra, V., Narukawa, Y., Inuiguchi, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2009. Lecture Notes in Computer Science(), vol 5861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04820-3_17
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DOI: https://doi.org/10.1007/978-3-642-04820-3_17
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