Group consensus measurement in MADM with multiple preference formats

An approach is proposed for measuring the group consensus in multiple attribute decision-making (MADM) problems with experts’ various preference information on alternatives. In the approach, multiple decision-makers give their preference information on alternatives in different formats. The uniformities and aggregation process with fuzzy majority method are employed to obtain the social fuzzy preference relation on the alternatives. Accordingly, the ranking values of the alternatives are obtained based on the obtained individual expert’s fuzzy preference relation, and the social one. The group consensus can be measured based on the ranking values of the alternatives that are derived from the individual expert’s preference information and the social one. An example of selecting robots is presented as an illustration

The dynamics of consensus in group decision making: investigating the pairwise interactions between fuzzy preferences.

In this paper we present an overview of the soft consensus model in group decision making and we investigate the dynamical patterns generated by the fundamental pairwise preference interactions on which the model is based. The dynamical mechanism of the soft consensus model is driven by the minimization of a cost function combining a collective measure of dissensus with an individual mechanism of opinion changing aversion. The dissensus measure plays a key role in the model and induces a network of pairwise interactions between the individual preferences. The structure of fuzzy relations is present at both the individual and the collective levels of description of the soft consensus model: pairwise preference intensities between alternatives at the individual level, and pairwise interaction coefficients between decision makers at the collective level. The collective measure of dissensus is based on non linear scaling functions of the linguistic quantifier type and expresses the degree to which most of the decision makers disagree with respect to their preferences regarding the most relevant alternatives. The graded notion of consensus underlying the dissensus measure is central to the dynamical unfolding of the model. The original formulation of the soft consensus model in terms of standard numerical preferences has been recently extended in order to allow decision makers to express their preferences by means of triangular fuzzy numbers. An appropriate notion of distance between triangular fuzzy numbers has been chosen for the construction of the collective dissensus measure. In the extended formulation of the soft consensus model the extra degrees of freedom associated with the triangular fuzzy preferences, combined with non linear nature of the pairwise preference interactions, generate various interesting and suggestive dynamical patterns. In the present paper we investigate these dynamical patterns which are illustrated by means of a number of computer simulations.

Consensus in a fuzzy environment: a bibliometric study

In today’s organizations, group decision making has become a part of everyday organizational life. It involves multiple individuals interacting to reach a decision. An important question here is the level of agreement or consensus achieved among the individuals before making the decision. Traditionally, consensus has been meant to be a full and unanimous agreement. However, it is often not reachable in practice. A more reasonable approach is the use of softer consensus measures, which assess the consensus in a more flexible way, reflecting the large spectrum of possible partial agreements and guiding the discussion process until widespread agreement is achieved. As soft consensus measures are more human-consistent in the sense that they better reflect a real human perception of the essence of consensus, consensus models based on these kind of measures have been widely proposed. The aim of this contribution is to present a bibliometric study performed on the consensus approaches that have been proposed in a fuzzy environment. It gives an overview about the research products gathered in this research field. To do so, several points have been studied, among others: countries, journals, top contributing authors, most cited keywords, papers and authors. This allows us to show a quick shot of the state of the art in this research area

Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039 71421001,71910107002,71771037,71874023 71871149Sichuan University sksyl201705 2018hhs-5

A Variance-Based Consensus Degree in Group Decision Making Problems

The variance is a well-known statistical measure and is frequently used for the calculation of variability. This concept can be used to obtain the degree of agreement in groups that have to make decisions. In this study, we propose the use of a variance derivative as an alternative for the calculation of the degree of consensus for Group Decision Making problems with fuzzy preference relations. As revealed by a subsequent comparative study, the values obtained by this new method are comparable to the values obtained by means of frequently used methods that employ distance functions and aggregation operators, while it turns out to be a simpler application method

Multi-criteria decision making with linguistic labels: a comparison of two methodologies applied to energy planning

This paper compares two multi-criteria decision making (MCDM) approaches based on linguistic label assessment. The first approach consists of a modified fuzzy TOPSIS methodology introduced by Kaya and Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. Both approaches are applied to a case of assessment and selection of the most suitable types of energy in a geographical area.Peer ReviewedPostprint (published version

A group decision making support system for the Web: how to work in environments with a high number of participants and alternatives

One of the main challenges that the appearance of Web 2.0 and the overall spreading of the Internet have generated is how to tackle with the high number of users and information available. This problem is also inherited by the group decision making problems that can be carried out over the Web. In this article, to solve this issue, a group decision making support system that allows the use of a high number of participants and alternatives is presented. This method allows any number of participants to join the decision making process at any time. Furthermore, they let them provide information only about a certain subset of alternatives. The high participation rate can provide enough information for the decision process to be carried out even if the participants do not provide information about all the high number of available alternatives.This paper has been developed with the financing of FEDER funds in the project TIN2016-75850-R