Abstract
In the group decision making (GDM) framework we deal with decision problems where several decision makers try to achieve a common solution about a problem. In the literature, we can find two processes to carry out before obtaining a final solution: the consensus process and the selection one. The consensus process is a discussion process where the experts change their opinions in order to achieve a high agreement. The selection process searches the solution.
The consensus reaching process is a very important task for GDM problems regarding the necessity that the solution achieved will be assumed and shared by all experts involved in the GDM problem. It consists of several consensus rounds where the experts discuss and change their opinions in order to improve the level of agreement among them.
In this paper, we propose an optimization of the consensus reaching process in GDM problems by means of an adaptive module that applies different procedures to identify the experts’ opinions that should be changed according to the level of agreement in each consensus round. Usually at the beginning the agreement is low, so the adaptive module will suggest to many experts to change their opinions. However, after several rounds, the agreement will be higher and hence the number of the changes will be smaller.
This work has been partially supported by the Research Project TIC2002-03348.
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Herrera-Viedma, E., Mata, F., Martínez, L., Pérez, L.G. (2005). An Adaptive Module for the Consensus Reaching Process in Group Decision Making Problems. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2005. Lecture Notes in Computer Science(), vol 3558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526018_10
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DOI: https://doi.org/10.1007/11526018_10
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