Affiliations: [a]
Department of Statistics and O.R., University of Granada, Granada, Spain
| [b]
Department of Quantitative Methods for Economics and Business, University of Granada, Granada, Spain
| [c]
Department of Informatics, De Montfort University, Leicester, UK
| [d]
Department of Computer Information Systems, Middle East University, Amman, Jordan
| [e]
Department of Computer Science and A.I, University of Granada, Granada, Spain
| [f]
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
Correspondence:
[*]
Corresponding author. Enrique Herrera-Viedma, Department of Computer Science and A.I, University of Granada, 18071 Granada, Spain; Department of Electrical and Computer Engineering, King Abdulaziz University, 21589 Jeddah,
Saudi Arabia. E-mail: viedma@decsai.ugr.es.
Abstract: Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process.
Keywords: Group decision making, fuzzy preference relations, consensus
