A majority model in group decision making using QMA-OWA operators

Abstract

Group decision-making problems are situations where a number of experts work in a decision process to obtain a final value that is representative of the global opinion. One of the main problems in this context is to design aggregation operators that take into account the individual opinions of the decision makers. One of the most important operators used for synthesizing the individual opinions in a representative value of majority in the OWA operator, where the majority concept used aggregation processes, is modeled using fuzzy logic and linguistic quantifiers. In this work the semantic of majority used in OWA operators is analyzed, and it is shown how its application in group decision-making problems does not produce representative results of the concept expressed by the quantifier. To solve this type of problem, two aggregation operators, QMA-OWA, are proposed that use two quantification strategies and a quantified normalization process to model the semantic of the linguistic quantifiers in the group decision-making process.

Evaluating the compatibility of multi-functional and intensive urban land uses

“…The most common operators used in the aggregation process (Yager, 1993) overemphasize the opinion of the minority at the expense of those of the majority and create an aggregation that may be considered imprecise for the majority (Pelaez and Dona, 2006). In fact, classical aggregation processes all elements independently, but traditional majority primarily groups the elements by their similarities and then aggregating the groups (first and second part of Fig.…”

A fast Iris location based on aggregating gradient approximation using QMA-OWA operator

“…Consequently, the operator of quantified majority QMA-OWA [20] was selected in order to obtain a representative value of each arc. This operator obtains the final value as a result of an aggregation process of gradient variation approximations.…”

“…The proposed approach relies on the analysis of gradient approximations on points of interest of successive arcs. It allows calculating the representative value on each successive arc as a result of the fusion of points of interest among the gradients points using the quantified majority operator QMA-OWA [20]. The identification of an Iris circular boundary in an image portion will be given by obtaining the arc with the greatest representative value.…”

A priori ordering protocols to support consensus-building in multiple stakeholder contexts

“…The OWA [20,32,33,47,48] is primarily concerned with the problem of aggregating multicriteria to form an overall decision function. This approach is different from the classical weighted average in that coefficients are not associated directly with a particular attribute but rather to an ordered position.…”