Soft consensus models in group decision making
In group decision making problems, when a consensual solution is required, a natural question is how to measure the closeness among experts’ opinions in order to obtain the consensus level. To do so, different approaches have been proposed. Following this research line, several authors have introduced hard consensus measures varying between 0 (no consensus or partial consensus) and 1 (full consensus or complete agreement). However, consensus as a full and unanimous agreement is far from being achieved in real situations. So, in practice, a more realistic approach is to use some softer consensus measures, which assess the consensus degree in a more flexible way reflecting better all possible partial agreements obtained through the process. The aim of this chapter is to identify and describe the different existing approaches to compute soft consensus measures in fuzzy group decision making problems. Additionally, we analyze the current models Additionally, we analyze the current model and new challenges on this field.
Citation : Perez, I.J., Cabrerizo, F.J., Alonso, S., Chiclana, F. and Herrera-Viedma, E. (2016) Soft consensus models in group decision making. Calvo Sanchez, Tomasa and Torrens Sastre, Joan (Eds.): Fuzzy Logic and Information Fusion: To commemorate the 70th birthday of Professor Gaspar Mayor". Springer International Publishing, pp. 135-153
ISBN : 9783319304212
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes