Author
Resumen
Nowadays, consensus is key in Group Decision Making (GDM). Many times, decision makers who participate in a GDM problem discuss and modify their initial opinions in order to reach a consensual solution; this process is known as Consensus Reaching Process (CRP). However, such a process can lead to endless negotiations in which the cost of achieving an agreement is too high. Several researchers have pointed out the importance of considering the cost of shifting the decision makers’ opinions in CRPs. One of the most wide-spread proposals is the Minimum Cost Consensus (MCC) models. These models define consensus as the minimal distance between each decision maker’s preference and the collective opinion and seek to minimize the overall cost of moving the experts’ opinions by using different types of cost functions. However, small distances do not always guarantee an acceptable consensus level. Therefore, there is a need for defining new MCC models that not only consider the distances of each decision maker to the collective opinion, but also achieve a minimum agreement among decision makers to obtain better solutions. Furthermore, these novel MCC models have to be able to deal with preference structures commonly used in GDM problems such as fuzzy preference relations.
Year of Publicaion
2021
Book Title
Computational Intelligence for Business Analytics
Edition
1
Publisher
Springer International Publishing
ISBN
978-3-030-73818-1
DOI
10.1007/978-3-030-73819-8
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