Hengjie Zhang

Consensus reaching model in the complex and dynamic MAGDM problem

In classical multiple attribute group decision making (MAGDM), decision makers evaluate predefined alternatives based on predefined attributes. In other words, the set of alternatives and the set of attributes are fixed throughout the decision process. However, real-world MAGDM problems (e.g., the decision processes of the United Nations Security Council) frequently have the following features. (1) Decision makers have different interests, and they thus use individual sets of attributes to evaluate the individual alternatives. In some situations, the individual sets of attributes may be heterogeneous. (2) In the decision process, decision makers do not have to reach a consensus regarding the use of the set of attributes. Instead, decision makers hope to find an alternative that is approved by all or most of them. (3) Finally, both the individual sets of attributes and the individual sets of alternatives can change dynamically in the decision process. By incorporating the above practical features into MAGDM, this study defines a complex and dynamic MAGDM problem, and proposes its resolution framework. In the resolution framework, a selection process in the context of heterogeneous attributes is proposed that obtains the ranking of individual alternatives and a collective solution. In addition, a consensus process is developed that generates adjustment suggestions for individual sets of attributes, individual sets of alternatives and individual preferences, thus helping decision makers reach consensus. Compared with existing MAGDM models, this study provides a flexible framework to form an approximate decision model to real-world MAGDM problems.

Multiperson decision making with different preference representation structures: A direct consensus framework and its properties

This study proposes a direct consensus framework for multiperson decision making (MPDM) with different preference representation structures (preference orderings, utility functions, multiplicative preference relations and fuzzy preference relations). In this framework, the individual selection methods, associated with different preference representation structures, are used to obtain individual preference vectors of alternatives. Then, the standardized individual preference vectors are aggregated into a collective preference vector. Finally, based on the collective preference vector, the feedback adjustment rules, associated with different preference representation structures, are presented to help the decision makers reach consensus. This study shows that the proposed framework satisfies two desirable properties: (i) the proposed framework can avoid internal inconsistency issue when using the transformation functions among different preference representation structures; (ii) it satisfies the Pareto principle of social choice theory. The results in this study are helpful to complete Chiclana et al.s MPDM with different preference representation structures.

Multi-granular unbalanced linguistic distribution assessments with interval symbolic proportions

We define the linguistic distribution assessments and their operations.We propose the transformations in the multi-granular unbalanced context.We discuss the application of this proposal in the MAGDM. Linguistic distribution assessments with exact symbolic proportions have been recently presented. Due to various subjective and objective conditions, it is often difficult for decision makers to provide exact symbolic proportions in linguistic distribution assessments. In some situations, decision makers will express their preferences in multi-granular unbalanced linguistic contexts. Therefore, in this study, we propose the concept of linguistic distribution assessments with interval symbolic proportions under multi-granular unbalanced linguistic contexts. First, the weighted averaging operator and the ordered weighted averaging operator for the linguistic distribution assessments with interval symbolic proportions are presented. Then, we develop the transformation functions among the multi-granular unbalanced linguistic distribution assessments with interval symbolic proportions. Finally, we present the application of the proposed linguistic distribution assessments in multiple attribute group decision making.

Consensus Building for the Heterogeneous Large-Scale GDM With the Individual Concerns and Satisfactions

Nowadays, societal and technological trends demand the management of large scale of decision makers in group decision-making (GDM) contexts. In a large-scale GDM, decision makers often have individual concerns and satisfactions, and also they will use heterogeneous preference representation structures to express their preferences. Meanwhile, it is difficult to set the numerical consensus threshold to judge whether a consensus degree can be acceptable or not in the consensus reaching process in a large-scale GDM. This study proposes a novel consensus reaching model for the heterogeneous large-scale GDM with the individual concerns and satisfactions. In this consensus reaching model, a selection process is proposed to obtain the individual preference vectors, to divide decision makers into different clusters, and to yield the preference vector of the large group. Following this, a consensus measure method that considers the individual concerns on alternatives is defined for measuring the consensus degree, and a linguistic approach is developed to measure the individual and collective satisfactions regarding the consensus degree. Finally, a feedback adjustment process is proposed and utilized to help decision makers adjust their preferences. A practical example and a simulation analysis are presented to demonstrate the validity of the proposed consensus reaching model.

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

Abstract 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.

Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: An approach based on social network analysis

Abstract In consensus-based multiple attribute group decision making (MAGDM) problems, it is frequent that some experts exhibit non-cooperative behaviors owing to the different areas to which they may belong and the different (sometimes conflicting) interests they might present. This may adversely affect the overall efficiency of the consensus reaching process, especially when some uncooperative behaviors by experts arise. To this end, this paper develops a novel consensus framework based on social network analysis (SNA) to deal with non-cooperative behaviors. In the proposed SNA-based consensus framework, a trust propagation and aggregation mechanism to yield experts’ weights from the social trust network is presented, and the obtained weights of experts are then integrated into the consensus-based MAGDM framework. Meanwhile, a non-cooperative behavior analysis module is designed to analyze the behaviors of experts. Based on the results of such analysis during the consensus process, each expert can express and modify the trust values pertaining other experts in the social trust network. As a result, both the social trust network and the weights of experts derived from it are dynamically updated in parallel. A simulation and comparison study is presented to demonstrate the efficiency of the SNA-based consensus framework for coping with non-cooperative behaviors.

Managing consensus and self-confidence in multiplicative preference relations in group decision making

Abstract Preference relations have been widely used in Group Decision Making (GDM) to represent decision makers’ preferences over alternatives. Recently, a new kind of preference relation called the self-confident multiplicative preference relation has been presented, which is formed considering multiple self-confidence levels into the multiplicative preference relation. This paper proposes an iteration-based consensus building framework for GDM problems with self-confident multiplicative preference relations. In this consensus building framework, an extended logarithmic least squares method is presented to derive the individual and collective priority vectors from the self-confident multiplicative preference relations. Then, a two-step feedback adjustment mechanism is used to assist the decision makers to improve the consensus level, which adjusts both the preference values and the self-confidence levels. The simulation experiments are devised to testify the efficiency of the proposed consensus building framework. Simulation results show that compared with only adjusting the preference values in the iteration-based consensus model, adjusting both the preference values and the self-confidence levels can accelerate the consensus success ratio and improve the consensus success ratio.

Linguistic Distribution Assessments with Interval Symbolic Proportions

In linguistic distribution assessments, symbolic proportions are assigned to all the linguistic terms. As a natural generation, we propose the concept of distribution assessments with interval symbolic proportion in a linguistic term set, and then study the operational laws of linguistic distribution assessments with interval symbolic proportion. Then, the weighted averaging operator and the ordered weighted averaging operator for linguistic distribution assessments with interval symbolic proportion are presented. Finally, two examples are presented for demonstrating the applicability of the proposed approach for computing with words.

Consensus reaching model with individual satisfactions in group decision making

In the consensus reaching process (CRP), different decision makers will concern with different alternatives. As a result, decision makers will naturally use individual consensus methods to measure individual consensus degrees. Meanwhile, it is difficult to set the consensus threshold to judge whether a consensus degree can be acceptable. In order to develop the individual consensus methods and avoid setting the consensus threshold, this study proposes a novel consensus reaching model with individual satisfactions in group decision making (GDM). In this consensus reaching model, the novel individual consensus measure methods are defined by taking into account the individual concerns on the alternatives. Then, based on numerical individual consensus degrees, a linguistic index is developed to evaluate the satisfaction degree. Finally, the feedback adjustment process is used to help decision makers to modify their opinions. A numerical example is provided to show the application of the proposed consensus reaching model.