Possibilistic approach to Bayes decisions

• Autorzy: Hryniewicz O.
• Języki publikacji: EN

Abstrakty

EN

The decision problems are considered when the prior probabilistic information about the state of nature and decision maker's utility function are imprecisely defined. In such a case the risks (or the expected utility) of considered decisions are also imprecisely defined. We propose two-step procedure for finding the optimal decision. First, we order possible decisions using the lambda-average ranking method by Campos and Gonzalez [1]. Then we use possibilistic possibility of dominance and necessity of strict dominance indices proposed by Dubois and Prade [3] for the comparison of consequences of the most promising solutions.

Słowa kluczowe

EN

optimal decisions; imprecise information; fuzzy risks; possibility indices

PL

decyzja optymalna

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2003
• Tom: nr 3
• Strony: 3--8
• Opis fizyczny: Bibliogr. 6 poz.

Twórcy

autor

Hryniewicz O.

• Systems Research Institute, Polish Academy of Sciences, Warszawa,

Bibliografia

• [1] L. M. Campos and A. Gonzalez, “A subjective approach for ranking fuzzy numbers”, Fuzzy Sets Syst., vol. 29, pp. 145–153, 1989.
• [2] V. Cutell and J. Montero, “An extension of the axioms of utility theory based on fuzzy rationality measures” in Preference and Decisions under Incomplete Knowledge, J. Fodor, P. De Baets, and P. Perny, Eds. Heidelberg: Physica-Verlag, 1999, pp. 33–50.
• [3] D. Dubois and H. Prade, “Ranking fuzzy numbers in the setting of possibility theory”, Inform. Sci., vol. 30, pp. 184–244, 1983.
• [4] M. A. Gil and M. Lopez-Diaz, “Fundamentals and Bayesian analyses of decision problems with fuzzy-valued utilities”, Int. J. Approx. Reason., vol. 15, pp. 203–224, 1996.
• [5] G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic. Theory and Applications. New York: Prentice-Hall, 1995.
• [6] H. Raiffa and R. Schlaifer, Applied Statistical Decision Theory. New York: Wiley, 2000