Analyzing consensus approaches in fuzzy group decision making: advantages and drawbacks

  • Authors:

  • F. J. Cabrerizo;J. M. Moreno;I. J. Pérez;E. Herrera-Viedma

  • Affiliations:

  • Distance Learning University of Spain, Department of Software Engineering and Computer Systems, 28040, Madrid, Spain;University of Murcia, Department of Information and Communication Engineering, 30100, Murcia, Spain;University of Granada, Department of Computer Science and Artificial Intelligence, 18071, Granada, Spain;University of Granada, Department of Computer Science and Artificial Intelligence, 18071, Granada, Spain

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Soft Computing in Decision Modeling; Guest Editors: Vicenc Torra, Yasuo Narukawa
  • Year:
  • 2009

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Abstract

Two processes are necessary to solve group decision making problems: a consensus process and a selection process. The consensus process is necessary to obtain a final solution with a certain level of agreement between the experts, while the selection process is necessary to obtain such a final solution. Clearly, it is preferable that the set of experts reach a high degree of consensus before applying the selection process. In order to measure the degree of consensus, different approaches have been proposed. For example, we can use hard consensus measures, which vary between 0 (no consensus or partial consensus) and 1 (full consensus), or soft consensus measures, which assess the consensus degree in a more flexible way. The aim of this paper is to analyze the different consensus approaches in fuzzy group decision making problems and discuss their advantages and drawbacks. Additionally, we study the future trends.