Publication: The role of fuzziness in decision making
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2007
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Springer- verlag
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In this paper we discuss the true objective of fuzzy decision making models.In particular, we stress that fuzzy models should focus their attention on decision processes, instead of referring to the final output of a decision, which is a crisp act. In fact, while Probability Theory can properly model crisp acts, this is not the case for fuzzy decisions. But in fact human beings manage poorly defined arguments and alternatives, and what we usually call a decision is always ill defined, although we still expect they should produce consistent acts as their consequence. These acts, still being consistent with the previous fuzzy decision, are somewhat unpredictable, being extremely dependant on the specific circumstances at the moment an act is required by decision makers. Therefore, in many cases pursuing consistency of these subsequent crisp acts may be misleading. The main issue should be in principle to check consistency between those acts and the true decision behind them, usually poorly formalized. But this objective may sometimes be unrealistic, since there may be few chances to repeat the experience. We should also focus our attention on the arguments that led us to such a poorly defined decision. This should be a relevant role of fuzziness in decision making, viewed as a decision support problem (acts are supported by a fuzzy decision which is supported in fuzzy arguments).
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A. Amo, D. Gómez, J. Montero and G. Biging (2001): Relevance and redundancy in fuzzy classification systems. Mathware and Soft Computing 8:203–216.
A. Amo, J. Montero, G. Biging and V. Cutello (2004): Fuzzy classification systems. European Journal of Operational Research 156:459–507.
A. Amo, J. Montero and V. Cutello (1999): On the principles of fuzzy classification. Proc. NAFIPS Conference. IEEE Press, Piscataway, NJ; pp. 675–679.
A. Amo, J. Montero, A. Fernàndez, M. López, J. Tordesillas and G. Biging (2002): Spectral fuzzy classification, an application. IEEE Trans on Systems, Man and Cybernetics, Part C, 32:42–48.
K.J. Arrow (1951, 1964): Social Choice and Individual Values. Wiley, New York.
A. Bechara, H. Damasio and A.R. Damasio (2003): Role of the amygdala in decision-making. Annals of the New York Academy of Sciences 985, 356–369.
A. Bechara, D. Tranel and H. Damasio (2000): Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain 123, 2189–2202.
B. Bérard, M. Bidoit, A. Finkel, F. Laroussinie, A. Petit, L. Petrucci and Ph. Shnobelen (2001): Systems and Software Verification: Model-Checking Techniques and Tools. Springer .
Y. Bertot and P. Castéran (2004): Interactive Theorem Proving and Program Development Coq’Art: The Calculus of Inductive Constructions. Springer.
J.C. Bezdek and S. K. Pal (1992): Fuzzy Models for Pattern Recognition. IEEE Press, New York.
D. Black (1958): The Theory of Commitees and Elections. Cambridge U.P., Cambridge.
P. Bonissone (1997): Soft computing, the convergence of emerging reasoning technologies, Soft Computing 1:6–18.
J.P. Brans (1998): The DGS Prometee procedure, Journal of Decision Systems 7:283–307.
J.M. Cleva, J. Leach and F.J. López-Fraguas (2004): A logic programming approach to the verification of functional-logic programs. Proc. Principles and Practice of Declarative Programming. ACM Press; pp. 9–19.
C.A. Coello (2006): Evolutionary multiobjective optimization, a historical view of the field, IEEE Computational Intelligence Magazine 1:28–36. CrossRef
P. Cousot and R. Cousot (1999): Refining model checking by abstract interpretation. Automated Software Engineering Journal 6:69–95.
V. Cutello and J. Montero (1993): A characterization of rational amalgamation operations. International Journal of Approximate Reasoning 8:325–344.
V. Cutello and J. Montero (1994): Fuzzy rationality measures. Fuzzy Sets and Systems 62:39–44. CrossRef
J. Fodor and M. Roubens (1994): Fuzzy Preference Modelling and Multicriteria Decision Support. Kluwer, Dorcrecht.
D. Gómez, J. Montero and J. Yáñez (2006): A coloring algorithm for image classification. Information Sciences 176:3645–3657.
D. Gómez, J. Montero, J. Yáñez and C. Poidomani (2007): A graph coloring approach for image segmentation. OMEGA-Int.J. Management Science 35: 273–183.
J. González-Pachón, D. Gómez, J. Montero and J. Yánez (2003): Soft dimension theory. Fuzzy set and Systems 137:137–149.
J. González-Pachón, D. Gómez, J. Montero and J. Yánez (2003): Searching for the dimension of binary valued preference relations. International Journal of Approximate Reasoning 33:133–157.
M.J.C. Gordon and T.F. Melham (1993): Introduction to HOL. Cambridge University Press.
F. Herrera, L. Martínez and P.J. Sánchez (2005): Managing non-homogeneous information in group decision making. European Journal of Operational Research 166, 115–132.
C.A.R. Hoare (1969): An axiomatic basis for computer programming. Comm. ACM 12:89–100. CrossRef
M. Hsu, M. Bahtt, R. Adolfs, D. Tranel and C.F. Camarer (2005): Neural systems responding to degrees of uncertainty in human decision-making. Science 310, 1680–1683. CrossRef
J.S. Kelly (1975): Arrow Impossibility Theorems. Academic Press, New York.
J. Kounios, J.L. Frymiare, E.M. Bowden, J.I. Fleck, K. Subramaniam, T.B. Parrish and M. Jung-Beeman (in press): The prepared mind, neural activity prior to problem presentation predicts subsequent solution by sudden insight. Psycological Science.
V. López, J.M. Cleva and J. Montero (2006): A functional tool for fuzzy first order logic evaluation. In D. Ruan et al., eds.: Applied Artificial Intelligence, World Scientific, New Jersey; pp. 19–26.
J. Montero (1985): A note on Fung-Fu’s theorem, Fuzzy Sets and Systems 13:259–269.
J. Montero (1987): Arrow’s theorem under fuzzy rationality, Behavioral Science 32:267–273. CrossRef
J. Montero (1987): Social welfare functions in a fuzzy environment, Kybernetes 16:241–245.
J. Montero (2003): Classifiers and decision makers. In D. Ruan et al., eds.: Applied Computational Intelligence. World Scientific, Singapore; pp. 19–24.
J. Montero and M. Mendel (1998): Crisp acts, fuzzy decisions. In S. Barro et al., eds.: Advances in Fuzzy Logic. Universidad de Santiago de Compostela; pp. 219–238.
J. Montero, J. Tejada and V. Cutello (1997): A general model for deriving preference structures from data. European Journal of Operational Research 98: 98–110.
G.J. Myers (1979): The Art of Software Testing. Wiley.
T. Nipkow, L.C. Paulson and M. Wenzel (2002): Isabelle/HOL - A Proof Assistant for Higer-Order Logic. Springer.
G. Owen (1982): Game Theory. Academic Press.
S. Owre, N. Shankar, J.M. Rushby and D.W.J. Stringer-Calvert (2001): PVS System Guide. SRI International.
P.K. Pattanaik (1971): Voting and Collecive Choice. Cambridge University Press.
B. Roy (1993): Decision sciences or decision aid sciences, European Journal of Operational Research 66:184–203.
A.K. Sen (1970): Collective Choice and Social Welfare. Holden-Day, San Francisco.
G. Shafer (1986): Savage revisited. Statistical Science 1:463–501.
W.T. Trotter (1992): Dimension Theory. The Johns Hopkins University Press, Baltimore and London.
J. Yáñez and J. Montero (1999): A poset dimension algorithm, Journal of Algorithms 30:185–208. CrossRef
L.A. Zadeh (1965): Fuzzy Sets, Information and Control 8:338–353.
L.A. Zadeh (1971): Similarity relations and fuzzy orderings. Informations Sciences 3:177–200