How to Support Consensus Reaching Using Action Rules: a Novel Approach

We consider a consensus reaching process in a group of individuals meant as an attempt to make preferences of the individuals more and more similar, that is, getting closer and closer to consensus. We assume a general form of intuitionistic fuzzy preferences and a soft definition of consensus that is basically meant as an agreement of a considerable (e.g., most, almost all) majority of individuals in regards to a considerable majority of alternatives. The consensus reaching process is meant to be run by a moderator who tries to get the group of individuals closer and closer to consensus by argumentation, persuasion, etc. The moderator is to be supported by some additional information, exemplified by more detailed information on which individuals are critical as, for instance, they are willing to change their testimonies or are stubborn, which pairs of options make the reaching of consensus difficult, etc. In this paper we extend this paradigm proposed and employed in our former works with the use of a novel data mining tool, so called action rules which make it possible to more clearly indicate and suggest to the moderator with which experts and with respect to which option it may be expedient to deal. We show the usefulness of this new approach.

[1]  Zbigniew W. Ras,et al.  Action Rules Discovery without Pre-existing Classification Rules , 2008, RSCTC.

[2]  Francisco Herrera,et al.  Theory and Methodology Choice functions and mechanisms for linguistic preference relations , 2000 .

[3]  D. Dubois,et al.  An introduction to bipolar representations of information and preference , 2008 .

[4]  J. Kacprzyk,et al.  Collective choice rules in group decision making under fuzzy preferences and fuzzy majority: a unified OWA operator based approach , 2002 .

[5]  J. Kacprzyk,et al.  A `human-consistent' degree of consensus based on fuzzy login with linguistic quantifiers , 1989 .

[6]  Janusz Kacprzyk,et al.  A consensus‐reaching process under intuitionistic fuzzy preference relations , 2003, Int. J. Intell. Syst..

[7]  Janusz Kacprzyk,et al.  Using intuitionistic fuzzy sets in group decision making , 2002 .

[8]  Tsau Young Lin,et al.  Foundations and Novel Approaches in Data Mining , 2006, Studies in Computational Intelligence.

[9]  Zbigniew W. Ras,et al.  Action-Rules: How to Increase Profit of a Company , 2000, PKDD.

[10]  ViedmaDept,et al.  Choice Functions for Linguistic Preference RelationsF , 2007 .

[11]  Slawomir Zadrozny,et al.  Towards a general and unified characterization of individual and collective choice functions under fuzzy and nonfuzzy preferences and majority via the ordered weighted average operators , 2009, Int. J. Intell. Syst..

[12]  L. Zadeh A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[13]  Luis Martínez-López,et al.  An Adaptive Consensus Support Model for Group Decision-Making Problems in a Multigranular Fuzzy Linguistic Context , 2009, IEEE Transactions on Fuzzy Systems.

[14]  Zeshui Xu,et al.  Intuitionistic and interval-valued intutionistic fuzzy preference relations and their measures of similarity for the evaluation of agreement within a group , 2009, Fuzzy Optim. Decis. Mak..

[15]  Sławomir Zadrożny,et al.  An Approach to the Consensus Reaching Support in Fuzzy Environment , 1997 .

[16]  Angelina A. Tzacheva,et al.  Action rules mining , 2005, Int. J. Intell. Syst..

[17]  Zbigniew W. Ras,et al.  Tree-based Algorithm for Discovering Extended Action-Rules (System DEAR2) , 2004, Intelligent Information Systems.

[18]  J. Kacprzyk,et al.  A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences , 1988 .

[19]  Luis Martínez-López,et al.  A Consensus Support System Model for Group Decision-Making Problems With Multigranular Linguistic Preference Relations , 2005, IEEE Transactions on Fuzzy Systems.

[20]  Slawomir Zadrozny,et al.  Soft computing and Web intelligence for supporting consensus reaching , 2010, Soft Comput..

[21]  Zdzislaw Pawlak,et al.  Information systems theoretical foundations , 1981, Inf. Syst..

[22]  Lotfi A. Zadeh,et al.  A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[23]  Zbigniew W. Ras,et al.  Constraint Based Action Rule Discovery with Single Classification Rules , 2007, RSFDGrC.

[24]  Slawomir Zadrozny,et al.  Computing with Words in Decision Making Through Individual and Collective Linguistic Choice Rules , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[25]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[26]  Slawomir Zadrozny,et al.  Linguistically quantified propositions for consensus reaching support , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[27]  Janusz Kacprzyk,et al.  A New Concept of a Similarity Measure for Intuitionistic Fuzzy Sets and Its Use in Group Decision Making , 2005, MDAI.

[28]  Zbigniew W. Ras,et al.  Tree-Based Algorithms for Action Rules Discovery , 2009, Mining Complex Data.

[29]  Zbigniew W. Ras,et al.  Extracting Rules from Incomplete Decision Systems: System ERID , 2006, Foundations and Novel Approaches in Data Mining.

[30]  Janusz Kacprzyk,et al.  A group decision support system based on linguistic multicriteria assessments , 2002 .

[31]  Francisco Herrera,et al.  Group decision making with incomplete fuzzy linguistic preference relations , 2009, Int. J. Intell. Syst..

[32]  Enrique Herrera-Viedma,et al.  A Consensus Model for Group Decision Making Problems with Unbalanced Fuzzy Linguistic Information , 2009, Int. J. Inf. Technol. Decis. Mak..

[33]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[34]  J. Kacprzyk,et al.  Dealing with imprecise knowledge on preferences and majority in group decision making: Towards a unified characterization of individual and collective choice functions , 2003 .

[35]  Francisco Herrera,et al.  A model of consensus in group decision making under linguistic assessments , 1996, Fuzzy Sets Syst..

[36]  José L. Verdegay,et al.  On aggregation operations of linguistic labels , 1993, Int. J. Intell. Syst..

[37]  Slawomir Zadrozny,et al.  An interactive multi-user decision support system for consensus reaching processes using fuzzy logic with linguistic quantifiers , 1988, Decis. Support Syst..

[38]  Didier Dubois,et al.  Qualitative Heuristics For Balancing the Pros and Cons , 2008 .

[39]  Luis Martínez-López,et al.  An Adaptive Module for the Consensus Reaching Process in Group Decision Making Problems , 2005, MDAI.

[40]  James C. Bezdek,et al.  Analysis of fuzzy information , 1987 .

[41]  Hailiang Huang,et al.  An Internet-based group decision support system for mass customization , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.