Favoring Consensus and Penalizing Disagreement in Group Decision Making

José Luis García-Lapresta

doi:10.20965/jaciii.2008.p0416

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JACIII Vol.12 No.5 pp. 416-421

doi: 10.20965/jaciii.2008.p0416

(2008)

Paper:

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Favoring Consensus and Penalizing Disagreement in Group Decision Making

José Luis García-Lapresta

PRESAD Research Group, Dep. of Applied Economics, University of Valladolid
Avda. Valle de Esgueva 6, 47011 Valladolid, Spain

Received:

October 10, 2007

Accepted:

February 15, 2008

Published:

September 20, 2008

Keywords:

group decision making, consensus, aggregation operators, metrics

Abstract

In this paper we introduce a multi-stage decision making procedure where decision makers' opinions are weighted by their contribution to the agreement after they sort alternatives into a fixed finite scale given by linguistic categories, each one having an associated numerical score. We add scores obtained for each alternative using an aggregation operator. Based on distances among vectors of individual and collective scores, we assign an index to decision makers showing their contributions to the agreement. Opinions of negative contributors are excluded and the process is reinitiated until all decision makers contribute positively to the agreement. To obtain the final collective weak order on the set of alternatives, we weigh the scores that decision makers assign to alternatives by indices corresponding to their contribution to the agreement.

Cite this article as:

J. García-Lapresta, “Favoring Consensus and Penalizing Disagreement in Group Decision Making,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.5, pp. 416-421, 2008.

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References

[1]
M. Dummett, “Voting Procedures,” Clarendon Press, Oxford, 1984.
M. Balinski and R. Laraki, “A theory of measuring, electing and ranking,” Proc. of the National Academy of Sciences of the United States of America, 104, pp. 8720-8725, 2007.
R. R. Yager, “Ordered weighted averaging operators in multicriteria decision making,” IEEE Transactions on Systems, Man and Cybernetics, 8, pp. 183-190, 1988.
R. R. Yager and J. Kacprzyk, “The Ordered Weighted Averaging Operators: Theory and Applications,” Kluwer Academic Publishers, Norwell, 1997.
J. Fodor and M. Roubens, “Fuzzy Preference Modelling and Multicriteria Decision Support,” Kluwer Academic Publishers, Dordrecht, 1994.
T. Calvo, A. Kolesárova, M. Komorn'i ková, and R. Mesiar, “Aggregation operators: Properties, classes and construction methods,” In T. Calvo, G. Mayor, and R. Mesiar (eds.), Aggregation Operators: New Trends and Applications, Physica-Verlag, Heidelberg, pp. 3-104, 2002.
R. Bosch, “Characterizations of Voting Rules and Consensus Measures,” Ph.D. Dissertation, Tilburg University, 2006.
P. Eklund, A. Rusinowska, and H. de Swart, “Consensus reaching in committees,” European Journal of Operational Research, 178, pp. 185-193, 2007.
W. D. Cook, M. Kress, and L. M. Seiford, “A general framework for distance-based consensus in ordinal ranking models,” European Journal of Operational Research, 96, pp. 392-397, 1996.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1]
M. Dummett, “Voting Procedures,” Clarendon Press, Oxford, 1984.
M. Balinski and R. Laraki, “A theory of measuring, electing and ranking,” Proc. of the National Academy of Sciences of the United States of America, 104, pp. 8720-8725, 2007.
R. R. Yager, “Ordered weighted averaging operators in multicriteria decision making,” IEEE Transactions on Systems, Man and Cybernetics, 8, pp. 183-190, 1988.
R. R. Yager and J. Kacprzyk, “The Ordered Weighted Averaging Operators: Theory and Applications,” Kluwer Academic Publishers, Norwell, 1997.
J. Fodor and M. Roubens, “Fuzzy Preference Modelling and Multicriteria Decision Support,” Kluwer Academic Publishers, Dordrecht, 1994.
T. Calvo, A. Kolesárova, M. Komorn'i ková, and R. Mesiar, “Aggregation operators: Properties, classes and construction methods,” In T. Calvo, G. Mayor, and R. Mesiar (eds.), Aggregation Operators: New Trends and Applications, Physica-Verlag, Heidelberg, pp. 3-104, 2002.
R. Bosch, “Characterizations of Voting Rules and Consensus Measures,” Ph.D. Dissertation, Tilburg University, 2006.
P. Eklund, A. Rusinowska, and H. de Swart, “Consensus reaching in committees,” European Journal of Operational Research, 178, pp. 185-193, 2007.
W. D. Cook, M. Kress, and L. M. Seiford, “A general framework for distance-based consensus in ordinal ranking models,” European Journal of Operational Research, 96, pp. 392-397, 1996.