Soft consensus model for the group fuzzy AHP decision making

The fuzzy analytic hierarchy process (AHP) is an extension to the classical AHP that enables dealing with the impreciseness and vagueness of judgments. It has been frequently used for handling complex decision making problems that demand a group rather than a single decision maker. Group decision making aggregates the judgments of individuals into a joint decision. Although consensus is the desired result in group decision making, it is difficult to achieve due to the diversity of opinions, knowledge and experiences of the decision makers. Therefore, the concept of soft consensus can be applied. We propose a new soft consensus based model for fuzzy AHP group decision making. The judgments in the model are presented as triangular fuzzy numbers. The closeness between judgments of two decision makers is measured by the individual fuzzy consensus index which in turn is based on the compatibility index from classical AHP. In each iteration, two decision makers with the most dissimilar opinions are identified and their judgments are adapted. The process is repeated until the desired consensus level is reached. The model can also take into account the weights of importance of individual decision makers. A fuzzy extension of the geometric mean method is employed for deriving fuzzy weights from a group fuzzy pairwise comparison matrix. The application of the model is provided in an example from the literature

The need of fairness in group consensus reaching process in a fuzzy environment

W niniejszym artykule wyjaśniono potrzebę „sprawiedliwości” w systemie obliczeniowym wspomagającym proces osiągania konsensusu w grupie decydentów. Autorki zaproponowały model łączący podejście matematyczne oparte na środowisku rozmytym oraz czynniki społeczno-psychologiczne wyjaśniające opisywaną koncepcję. Pojęcie „sprawiedliwości” rozpatrywane jest tu w dwóch kategoriach: rozkładu zasobów oraz decyzji ostatecznej. Założenia poparte są wnioskami na podstawie obserwacji grup studentó

Different aspects of supporting group consensus reaching process under fuzziness

In this paper we present human-consistent approach of multi-model consensus reaching process supporting by group decision support systems. We consider the idea developed by Kacprzyk and Zadrożny [9, 10, 12] which is related to the “soft” consensus, and where the core of the system is based on fuzzy logic. Essentially, we attempt to stress the multi-model architecture of considering system and distinguish several aspects, i.e. model of agent, model of moderator, model of consensus achievement. Moreover, we present a novel concept based on fair consensus as a meaningful point of further development

Changing attitude patterns in group decision making process – a socio-technological approach

This paper proposes a novel approach to consensus reaching process, which combines a mathematical model and socio-psychological factors. We attempt to correlate two decision support systems: one with the teacher managing the process of reaching consensus within the laboratory groups of individuals, and computer-based system, with mathematical presumptions as for the consistency of the model with the reality of a particular social group

Impact of Decision Rules and Non-Cooperative Behaviors on Minimum Consensus Cost in Group Decision Making

The file attached to this record is the author's final peer reviewed version.In group decision making (GDM), it is sensible to achive minimum consensus cost (MCC) because the consensus reaching process (CRP) resources are often limited. In this endeavour, though, there are still two issues that require paying attention to: (1) the impact of decision rules, including decision weights and aggregation functions, on MCC; and (2) the impact of non-cooperative behaviors on MCC. Hence, this paper analytically reveals the decision rules to minimize MCC or maximize MCC. Furthermore, detailed simulation experiments show the joint impact of non-cooperative behavior and decisions rules on MCC, as well as revealing the effect of the consensus within the established MCC target

Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors

This work was supported in part by the NSF of China under grants 71171160 and 71571124, in part by the SSEM Key Research Center at Sichuan Province under grant xq15b01, in part by the FEDER funds under grant TIN2013-40658-P, and in part by Andalusian Excellence Project under grant TIC-5991.The consensus reaching process (CRP) is a dynamic and iterative process for improving the consensus level among experts in group decision making. A large number of non-cooperative behaviors exist in the CRP. For example, some experts will express their opinions dishonestly or refuse to change their opinions to further their own interests. In this study, we propose a novel consensus framework for managing non-cooperative behaviors. In the proposed framework, a self-management mechanism to generate experts' weights dynamically is presented and then integrated into the CRP. This self-management mechanism is based on multi-attribute mutual evaluation matrices (MMEMs). During the CRP, the experts can provide and update their MMEMs regarding the experts' performances (e.g., professional skill, cooperation, and fairness), and the experts' weights are dynamically derived from the MMEMs. Detailed simulation experiments and comparison analysis are presented to justify the validity of the proposed consensus framework in managing the non-cooperative behaviors.National Natural Science Foundation of China 71171160 71571124SSEM Key Research Center at Sichuan Province xq15b01European Union (EU) TIN2013-40658-PAndalusian Excellence Project TIC-599

A Personalized Feedback Mechanism based on Bounded Confidence to Support Consensus Reaching in Group Decision Making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Different feedback mechanisms have been reported in consensus reaching models to provide advices for preference adjustment to assist decision makers to improve their consensus levels. However, most feedback mechanisms do not consider the willingness of decision makers to accept these advices. In the opinion dynamics discipline, the bounded confidence model justifies well that in the process of interaction a decision maker only considers the preferences that do not exceed a certain confidence level compared to his own preference. Inspired by this idea, this article proposes a new consensus reaching model with personalized feedback mechanism to help decision makers with bounded confidences in achieving consensus. Specifically, the personalized feedback mechanism produces more acceptable advices in the two cases where bounded confidences are known or unknown, and the unknown ones are estimated by a learning algorithm. Finally, numerical example and simulation analysis are presented to explore the effectiveness of the proposed model in reaching consensus

A self-management mechanism for non-cooperative behaviors in large-scale group consensus reaching processes

In large-scale group decision making (GDM), non-cooperative behavior in the consensus reaching process (CRP) is not unusual. For example, some individuals might form a small alliance with the aim to refuse attempts to modify their preferences or even to move them against consensus to foster the alliance’s own interests. In this paper, we propose a novel framework based on a self-management mechanism for non-cooperative behaviors in large-scale consensus reaching processes (LCRPs). In the proposed consensus reaching framework, experts are classified into different subgroups using a clustering method, and experts provide their evaluation information, i.e., the multi-criteria mutual evaluation matrices (MCMEMs), regarding the subgroups based on subgroups’ performance (e.g., professional skills, cooperation, and fairness). The subgroups’ weights are dynamically generated from the MCMEMs, which are in turn employed to update the individual experts’ weights. This self-management mechanism in the LCRP allows penalizing the weights of the experts with non-cooperative behaviors. Detailed simulation experiments and comparison analysis are presented to verify the validity of the proposed framework for managing non-cooperative behaviors in the LCRP

Consensus Reaching in Social Network Group Decision Making: Research Paradigms and Challenges

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In social network group decision making (SNGDM), the consensus reaching process (CRP) is used to help decision makers with social relationships reach consensus. Many CRP studies have been conducted in SNGDM until now. This paper provides a review of CRPs in SNGDM, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) the CRP paradigm based on opinion evolution. Furthermore, identified research challenges are put forward to advance this area of research