Classical Dynamic Consensus and Opinion Dynamics Models: A Survey of Recent Trends and Methodologies

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.Consensus reaching is an iterative and dynamic process that supports group decision-making models by guiding decision-makers towards modifying their opinions through a feedback mechanism. Many attempts have been recently devoted to the design of efficient consensus reaching processes, especially when the dynamism is dependent on time, which aims to deal with opinion dynamics models. The emergence of novel methodologies in this field has been accelerated over recent years. In this regard, the present work is concerned with a systematic review of classical dynamic consensus and opinion dynamics models. The most recent trends of both models are identified and the developed methodologies are described in detail. Challenges of each model and open problems are discussed and worthwhile directions for future research are given. Our findings denote that due to technological advancements, a majority of recent literature works are concerned with the large-scale group decision-making models, where the interactions of decision-makers are enabled via social networks. Managing the behavior of decision-makers and consensus reaching with the minimum adjustment cost under social network analysis have been the top priorities for researchers in the design of classical consensus and opinion dynamics models

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

Consensus Reaching in Multiple Attribute Group Decision Making: A Multi-Stage Optimization Feedback Mechanism with Individual Bounded Confidences

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.Existing consensus models focus on improving the group consensus level, but ignore whether a higher group consensus level means higher mutual acceptance of decision makers. In the field of opinion dynamics, the bounded confidence model asserts that the decision makers will accept the preferences of others within a neighborhood of theirs with width a certain confidence level. Inspired by this research methodology, this paper develops a consensus model to address the acceptance issue based on individual bounded confidences. Specifically, a bounded confidence-based consensus measure is designed to measure the level of group mutual acceptance, and a multi-stage optimization feedback mechanism based on individual bounded confidences is proposed to maximize the group mutual acceptance and minimize the amount of preference adjustment. A numerical example and a simulation analysis are included to illustrate the use of the model and to justify its effectiveness, respectively

A Comprehensive Star Rating Approach for Cruise Ships Based on Interactive Group Decision Making with Personalized Individual Semantics

This article proposes a comprehensive star rating approach for cruise ships by the combination of subject and objective evaluation. To do that, it firstly established a index system of star rating for cruise ships. Then, the modified TOPSIS is adopted to tackle objective data for obtaining star ratings for basic cruise indicators and service capabilities of cruise ships. Thus, the concept of distributed linguistic star rating function (DLSRF) is defined to analyze the subjective evaluation from experts and users. Hence, a novel weight calculation method with interactive group decision making is presented to assign the importance of the main indicators. Particularly, in order to enable decision makers to effectively deal with the uncertainty in this star rating process, it adopts the personalized individual semantics (PIS) model. Finally, data of nine cruise ships is collected to obtain their final star rating results and some suggestions for improving cruise service capabilities and star indicators were put forward.National Natural Science Foundation of China (NSFC) 71971135,72001134,72071056 China Scholarship Council 202108310183 Innovative Talent Training Project of Graduate Students in Shanghai Maritime University of China 2021YBR00

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

Multicriteria Consensus Models to Support Intelligent Group Decision-Making

The development of intelligent systems is progressing rapidly, thanks to advances in information technology that enable collective, automated, and effective decision-making based on information collected from diverse sources. Group decision-making (GDM) is a key part of intelligent decision-making (IDM), which has received considerable attention in recent years. IDM through GDM refers to a decision-making problem where a group of intelligent decision-makers (DMs) evaluate a set of alternatives with respect to specific attributes. Intelligent communication among DMs aims to give orders to the available alternatives. However, GDM models developed for IDM must incorporate consensus support models to effectively integrate input from each DM into the final decision. Many efforts have been made to design consensus models to support IDM, depending on the decision problem or environment. Despite promising results, significant gaps remain in research on the design of such support models. One major drawback of existing consensus models is their dependence on the type of decision environment, making them less generalizable. Moreover, these models are often static and cannot respond to dynamic changes in the decision environment. Another limitation is that consensus models for large-scale decision environments lack an efficient communication regime to enable DM interactions. To address these challenges, this dissertation proposes developing consensus models to support IDM through GDM. To address the generalization issue of existing consensus models, reinforcement learning (RL) is proposed. RL agents can be built on the Markov decision process to enable IDM, potentially removing the generalization issue of consensus support models. Contrary to most consensus models, which assume static decision environments, this dissertation proposes a computationally efficient dynamic consensus model to support dynamic IDM. Finally, to facilitate secure and efficient interactions among intelligent DMs in large-scale problems, Blockchain technology is proposed to speed up the consensus process. The proposed communication regime also includes trust-building mechanisms that employ Blockchain protocols to remove enduring and limitative assumptions on opinion similarity among agents

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

Consensus Reaching with Time Constraints and Minimum Adjustments in Group with Bounded Confidence Effects

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 the bounded confidence model it is widely known that individuals rely on the opinions of their close friends or people with similar interests. Meanwhile, the decision maker always hopes that the opinions of individuals can reach a consensus in a required time. Therefore, with this idea in mind, this paper develops a consensus reaching model with time constraints and minimum adjustments in a group with bounded confidence effects. In the proposed consensus approach, the minimum adjustments rule is used to modify the initial opinions of individuals with bounded confidence, which can further influence the opinion evolutions of individuals to reach a consensus in a required time. The properties of the model are studied, and detailed numerical examples and comparative simulation analysis are provided to justify its feasibility