As of August 2024, International Journal "Notes on Intuitionistic Fuzzy Sets" is being indexed in Scopus.
Please check our Instructions to Authors and send your manuscripts to nifs.journal@gmail.com. Next issue: September/October 2024.
Open Call for Papers: International Workshop on Intuitionistic Fuzzy Sets • 13 December 2024 • Banska Bystrica, Slovakia/ online (hybrid mode).
Deadline for submissions: 16 November 2024.

Issue:Intuitionistic fuzzy relations and consensus formations

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to navigation Jump to search
shortcut
http://ifigenia.org/wiki/issue:nifs/6/3/1-10
Title of paper: Intuitionistic fuzzy relations and consensus formations
Author(s):
Eulalia Szmidt
Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
szmidt@ibspan.waw.pl
Janusz Kacprzyk
Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
kacprzyk@ibspan.waw.pl
Presented at: Fourth International Conference on IFSs, Sofia, 16-17 September 2000
Published in: "Notes on Intuitionistic Fuzzy Sets", Volume 6 (2000) Number 3, pages 1—10
Download:  PDF (5952  Kb, File info)
Abstract: The use of Zadeh’s (1983) calculus of linguistically quantified statements is proposed for the formalization of a fuzzy majority in the derivation of a degree of consensus under intuitionistic fuzzy preferences. In this article we develop ideas proposed in works of Fedrizzi (1988), Kacprzyk (1987), Kacprzyk and Fedrizzi (1986, 1988, 1989) i.e. a "soft" measure of consensus which is more human-consistent in the sense that it better reflects a real human perception of the essence of consensus in practice. Basically, the proposed consensus measure expresses the degree to which, say "most of the important individuals agree as to almost all of the relevant options". The point of departure is the set of individual testimonies which are here the individual intuitionistic fuzzy preference relations. Useing of intuitionistic fuzzy preference relations instead of fuzzy preference relations (what has been presented in the cited works) let us take into account that individuals can change their preferences during reaching consensus (they are open for new arguments). In effect we obtain final measures of consensus which are given as numbers from some intervals, what means that we are able to foresee the best and the worst of the possible results.
Keywords: consensus, degree of consensus, intuitionistic fuzzy sets, intuitionistic fuzzy preference relation, linguistically quantified statements.
References:
  1. Atanassov K. (1986), Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, 87-96.
  2. Atanassov K. (1999), Intuitionistic Fuzzy Sets: Theory and Applications. Springer-Verlag.
  3. Fedrizzi, M. and Kacprzyk, J. (1988). On measuring consensus in the setting of fuzzy preference relations. In J. Kacprzyk and M. Roubens (Eds.): Non-Conventional Preference Relations in Decision-Making, Springer-Verlag, Heidelberg, pp. 129-141.
  4. Kacprzyk, J. (1987). On some fuzzy cores and "soft" consensus measures in group decision making. In J.C. Bezdek (Ed.): The Analysis of Fuzzy Information, Vol. 2, CRC Press, Boca Raton, pp. 119-130.
  5. Kacprzyk, J. and Fedrizzi, M. (1986). Soft consensus measures for monitoring real consensus reaching processes under fuzzy preferences, Control and Cybernetics, 15, 309-323.
  6. Kacprzyk, J. and Fedrizzi, M. (1988). A ’soft‘ measure of consensus in the setting of partial (fuzzy) preferences, European Journal of Operational Research, 34, 315-325.
  7. Kacprzyk, J. and Fedrizzi, M. (1989). A *human-consistent‘ degree of consensus based on fuzzy logic with linguistic quantifiers, Mathematical Social Sciences, 18, 275-290.
  8. Kacprzyk, J., Fedrizzi, M. and Nurmi, H. (1992a). Fuzzy logic with linguistic quantifiers in group decision making and consensus formation. In R.R. Yager and L.A. Zadeh (Eds.): An Introduction to Fuzzy Logic Applications in Intelligent Systems, Kluwer, Dordrecht, 263-280.
  9. Kacprzyk, J., Fedrizzi, M. and Nurmi, H. (1992b). Group decision making and consensus under fuzzy preferences and fuzzy majority, Fuzzy Sets and Systems, 49, 21-31.
  10. Kacprzyk, J., Nurmi, H. and Fedrizzi, M., Eds. (1996). Consensus under Fuzziness, Kluwer, Boston.
  11. Szmidt E. (2000). Application of Intuitionistic Fuzzy sets in Decision Making, Dissertation (D.Sc.), TU-Sofia.
  12. Szmidt E. and Kacprzyk, J. (1996a). Intuitionistic fuzzy sets in group decision making, Notes on IFS 2, 15-32.
  13. Szmidt E. and Kacprzyk, J. (1996b). Group decision making via intuitionistic fuzzy sets, Proceeding of FUBEST’96 (Sofia, Bulgaria), 107-112.
  14. Szmidt E. and Kacprzyk J. (1996c). Remarks on some applications of intuitionistic fuzzy sets in decision making, Notes on IFS 2, 22-31.
  15. Szmidt E. and Kacprzyk J. (1997a). Intuitionistic fuzzy sets for more realistic group decision making, Proceedings of TRANSITIONS’97 (Warsaw, Poland), 430-433.
  16. Szmidt E. and Kacprzyk J. (1998f). Group Decision Making under Intuitionistic Fuzzy Preference Relations. Proceedings of the 7th Int. Conference IPMU’98 (Paris, La Sorbonne, July 6-10), pp. 172-178.
  17. Szmidt E. and Kacprzyk J. (1998g). Applications of Intuitionistic Fuzzy Sets in Decision Making. Proceedings of the 8th Congreso EUSFLAT’9 (Pamplona, Univ. De Navarra, September 8-10), pp. 150-158.
  18. L.A. Zadeh (1965). Fuzzy sets. Information and Control, 8, 338-353.
  19. Zadeh, L.A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers and Mathematics with Applications, 9, No. 1, 149-184.
Citations:

The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.

Retrieved from "https://ifigenia.org/index.php?title=Issue:Intuitionistic_fuzzy_relations_and_consensus_formations&oldid=12526"