Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information

By F. Cabrerizo, Ignacio Javier Pérez, Enrique Herrera-Viedma. Knowledge-Based Systems, 2010

306 citations recorded by the source corpus.

To solve group decision-making problems we have to take in account different aspects. On the one hand, depending on the problem, we can deal with different types of information. In this way, most group decision-making problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts' opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e., using term sets which are not uniformly and symmetrically distributed. On the other hand, there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem and, as a result, they may present incomplete information. The aim of this paper is to present a consensus model to help experts in all phases of the consensus reaching process in group decision-making problems in an unbalanced fuzzy linguistic context with incomplete information. As part of this consensus model, we propose an iterative procedure using consistency measures to estimate the incomplete information. In addition, the consistency measures are used together with consensus measures to guided the consensus model. The main novelty of this consensus model is that it supports the management of incomplete unbalanced fuzzy linguistic information and it allows to achieve consistent solutions with a great level of agreement.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/622c911f54142c4c8100af184de95f0d66ac4623

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Citations in the corpus

Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions (2015, background): “Generally, both of the two processes are used to address such problems: the consensus process and the selection process [12,13].”
A statistical comparative study of different similarity measures of consensus in group decision making (2013, background): “In each round a consensus support system calculates two consensus parameters [4, 22, 23]: (i) the consensus measure to guide the consensus process and (ii) the proximity measure to support the group discussion phase of the consensus process.”
An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges (2012, unknown): “Consensus reaching processes [12,13] [48,49,53] [87,94]”
An extended TOPSIS for determining weights of decision makers with interval numbers (2011, background): “The decision information provided by the DMs may take the various representation formats in group decision making problems, such as exact numerical values [20–22], interval numbers [23–26], fuzzy numbers [27–30], fuzzy linguistic [31,32], rough set theory [33] and evidence theory [34].”
A web based consensus support system for group decision making problems and incomplete preferences (2010, background): “Current decision support models and systems incorporate mechanisms to maximize the consensus in the decision process [5,6,4,17,25,24,34–36,65].”
A review of soft consensus models in a fuzzy environment (2014, methodology): “The automatic consensus models existing in the literature act in a similar way during all consensus rounds although the conditions of GDM problem change [30,48,50,56].”