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The Concept of Fairness in the Group Decision Support Systems – a Socio-Psychological Approach

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we present ae general concept of the consensus reaching process supporting by the group decision support systems. We proposed the idea which combines the mathematical direction based on “soft” consensus developed by Kacprzyk and Zadrożny [6] and relevant socio-psychological factor concerning fairness component. Essentially, we divide fairness approach in consensus reaching process on two possible directions: a fair distribution (fair resource allocation) and a fair final decision. We stress the benefits resulting from the implementation of proposed concept in the group decision support systems and point the direction of further model formalization.
Rocznik
Strony
21--29
Opis fizyczny
Bibliogr. 10 poz., rys.
Twórcy
  • Department of Automatic Control and Information Technology, Faculty of Electrical and Computer Engineering, Cracow University of Technology, Ph.D. Studies, Systems Research Institute, Polish Academy of Sciences
autor
  • Department of Automatic Control and Information Technology, Faculty of Electrical and Computer Engineering, Cracow University of Technology, Ph.D. Studies, Systems Research Institute, Polish Academy of Sciences
Bibliografia
  • 1. Camerer C.F., 2003, Psychology and economics. Strategizing in the brain. Science, 300: 1673-1675.
  • 2. Fedrizzi M., Kacprzyk J., and Zadrożny S., 1988, An interactive multi-user decision support system for consensus reaching process using fuzzy logic with linguistic quantifiers, Decision Support Systems, vol.4, no. 3, 313-327.
  • 3. Kacprzyk J., 2008, Neuroeconomics: yet another field where rough sets can be useful?, Chac C.C et al. (Eds.): RSCTC 2008, LNCS vol. 5306, 1-12.
  • 4. Kacprzyk J., Fedrizzi M., 1989, A ‘human-consistent’ degree of consensus based on fuzzy logic with linguistic quantifiers, Mathematical Social Sciences, vol. 18, 275-290.
  • 5. Kacprzyk J., Fedrizzi M., 1988, A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences, European Journal of Operational Research, vol. 34, 315-325.
  • 6. Kacprzyk J., Zadrożny S.,2010, Soft computing and Web intelligence for supporting consensus reaching, Soft Computing, vol.14, no. 8, 833-846.
  • 7. Turban E., Aronson J.E., Liang T.P. (2005) Decision Support Systems and Intelligent Systems, 6th Edition, Prentice Hall, 11-19, 94-101.
  • 8. Tyler T.R., Smith H.J., 1998, The handbook of social psychology, vol. 2, Social justice and social movements, McGraw-Hill, Boston, 595-629.
  • 9. Wierzbicki A. 2010, Trust and Fairness in Open, Distributed Systems. Studies In Computational Intelligence 298, Springer, 11-19, 56-70.
  • 10. Young H.P., 1994, Equity: In Theory and Practice. Princeton University Press.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-85804242-7193-477f-8f33-833e2b7de337
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