Approximate reasoning in the modeling of consensus in group decisions

Chiclana

et al. 2010

Int. J. Intell. Syst.

Yager's ordered weighted averaging (OWA) operator has been widely used in soft decision making to aggregate experts' individual opinions or preferences for achieving an overall decision. The traditional Yager's OWA operator focuses exclusively on the aggregation of crisp numbers. However, human experts usually tend to express their opinions or preferences in a very natural way via linguistic terms. Type-2 fuzzy sets provide an efÞcient way of knowledge representation for modeling linguistic terms. In order to aggregate linguistic opinions via OWA mechanism, we propose a new type of OWA operator, termed type-2 OWA operator, to aggregate the linguistic opinions or preferences in human decision making modeled by type-2 fuzzy sets. A Direct Approach to aggregating interval type-2 fuzzy sets by type-2 OWA operator is suggested in this paper. Some examples are provided to delineate the proposed technique. C

Section: Introductionmentioning

confidence: 99%

On aggregating uncertain information by type-2 OWA operators for soft decision making

Zhou

Chiclana

et al. 2010

Int. J. Intell. Syst.

“…Since then, OWA based aggregation strategies have been widely investigated and have achieved successful applications in many domains, such as decision making [4,13,14,28,38,39], fuzzy control [41,42], market analysis [43], image compression [29], etc.…”

Section: Yager's Owa Operatormentioning

confidence: 99%

“…Zimmermann and Zysno developed a family of compensatory operators for aggregating type-1 fuzzy sets by combining a t-norm and a t-conorm to produce certain compensation between criteria [51,52]. This family of compensatory operators has been extended to aggregate weighted fuzzy sets in heterogeneous decision making problems [28], in which different experts were assigned different importance weights in the form of crisp numbers. In order to evaluate an overall linguistic value, a weighted average of the membership function values associated with the linguistic labels was first computed, and then this aggregation result was translated into linguistic terms via a linguistic approximation.…”

Section: Introductionmentioning

confidence: 99%

Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers

Zhou

Chiclana

et al. 2008

Fuzzy Sets and Systems

149

120

The OWA operator proposed by Yager has been widely used to aggregate experts' opinions or preferences in human decision making. Yager's traditional OWA operator focuses exclusively on the aggregation of crisp numbers. However, experts usually tend to express their opinions or preferences in a very natural way via linguistic terms. These linguistic terms can be modelled or expressed by (type-1) fuzzy sets. In this paper, we define a new type of OWA operator, the type-1 OWA operator that works as an uncertain OWA operator to aggregate type-1 fuzzy sets with type-1 fuzzy weights, which can be used to aggregate the linguistic opinions or preferences in human decision making with linguistic weights. The procedure for performing type-1 OWA operations is analysed. In order to identify the linguistic weights associated to the type-1 OWA operator, type-2 linguistic quantifiers are proposed. The problem of how to derive linguistic weights used in type-1 OWA aggregation given such type of quantifier is solved. Examples are provided to illustrate the proposed concepts. Crown

“…In [8], arithmetic operations on linguistic terms in the representations of trapezoidal fuzzy numbers were obtained by using the Extension Principle, and triangular norms were used to aggregate the fuzzy numbers. In [66], in order to evaluate an overall linguistic value of every expert's performance for each alternative with respect to the criteria, a weighted average of the membership function values associated with the linguistic performance labels was first computed, then the aggregation result was translated into linguistic terms via linguistic approximation. In the case of decision making applications, the importance weights in aggregation may be uncertain rather than represented by precise numerical values.…”

Section: B Linguistic Information Aggregationmentioning

confidence: 99%

“…By minimizing a sum of weighted dissimilarity among aggregated consensus and individual opinions, a method of aggregating individual fuzzy opinions into an optimal group consensus was suggested in [57]. An approach to consensus reaching for group decision with linguistic preference values was proposed by Mich et al [66]. In this approach, an opinion changing aversion function was defined for each expert to represent his/her resistance to opinion changing, the measure of consensus varies along with each expert's aversion to opinion change.…”

mentioning

confidence: 99%

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

Zhou²,

Garibaldi

et al. 2008

IJCIR

Abstract:In the real world, group decision making is one of the most significant and omnipresent human decision making activities. The central problem of group decision making is to develop "fair" methods for aggregating individual alternatives (options, variants, etc.) to yield a consensus decision that is most acceptable to the group as a whole. It has become a subject of intensive research due to its practical and academic significance. But group decision making is innately uncertain, so a promising framework of decision making in groups is to consider uncertainty measures in the processes of model construction, so as to achieve optimal solutions in terms of best degrees of consensus of final decisions. This paper aims to provide an in-depth overview of group decision making incorporating uncertainty from the perspective of fuzzy sets (including type-1 and type-2 fuzzy sets). Some potential new research directions on fuzzy group decision making are suggested.