Social Network Group Decision Research Articles

Social network group decision-making method based on stochastic multi-criteria acceptability analysis for probabilistic linguistic term sets

In social network group decision-making (SNGDM) problems, decision-makers (DMs) often express their opinions or preferences using probabilistic linguistic term sets (PLTSs). In this paper, a novel SNGDM method for probabilistic linguistic information is proposed. Firstly, to obtain the prioritization of DMs in the clustering process, a DM clustering method for SNGDM is developed considering the influence of trust relationships and opinions similarity among DMs. Then, to satisfy the requirements of the consensus reaching process in SNGDM, a dynamic consensus threshold calculation method based on an optimization model is introduced. Furthermore, in the consensus measure stage, a novel consensus measure method for both DMs and subgroups is proposed, using a stochastic multi-criteria acceptability analysis (SMAA) method. Based on the consensus measure method, a novel SNGDM method based on SMAA for PLTSs is proposed. Finally, a case study of service quality evaluation in institutional pensions is used to illustrate the effectiveness of the proposed method. The results of case show that the proposed method can adjust consensus thresholds dynamically based on each round of collective opinions, and can solve the SNGDM problems when DMs can not provide their preferences for criteria weights.

Two-stage group decision making methodology with hesitant fuzzy preference relations under social network: Multiplicative consistency determination and personalized feedback

Some social network group decision making (SNGDM) researches may overlook two issues: (1) the impact of consistency on decision reliability and decision makers’ (DMs’) status in social network, and (2) DMs’ personalization during consensus reaching process. In response to these two issues, a two-stage SNGDM model is proposed for hesitant fuzzy preference relations (HFPRs). In the first stage, multiplicative consistency determination and improvement of HFPRs are achieved. For complete and incomplete HFPRs, several programming models are constructed to classify consistency types. When it comes to inconsistent HFPRs, an inconsistency fixing method is designed to ensure the original HFPRs as much as possible. With the improvement of DMs’ consistency, their status in social network will be dynamically enhanced. In the second stage, two programming models are devised to elicit the reasonable individual priority weight vector in consistent HFPR. Further, minimum adjustment cost-based personalized feedback mechanism is invented to attain the optimal personalized adjustment coefficient and recommendation, and achieve the harmonious unity of individual personalization and group consensus. Finally, the superiority of our proposal is verified by comparative analysis and illustrative example.

A Feedback Mechanism With Unknown Bounded Confidence-Based Optimization Model for Consensus Reaching in Social Network Group Decision Making

A Feedback Mechanism With Unknown Bounded Confidence-Based Optimization Model for Consensus Reaching in Social Network Group Decision Making

Adaptive opinion evolution process with opinion dynamics for large-scale group decision making: A novel approach based on overlapping community detection in social networks

The rapid development of Web2.0 technologies and social media platforms has developed a new paradigm that allows many individuals to participate in decision-making processes within online social networks, leading to the rise of social network group decision making (SNGDM). Existing research in SNGDM primarily focus on small-scale DMs, which may not be suitable for large-scale SNGDM problems due to the high costs and time constraints of adjustments. Moreover, the non-overlapping community structure in social network encounter several limitations, further complicating the resolution of large-scale SNGDM problems. In this study, we propose an adaptive opinion evolution process with opinion dynamics for large-scale SNGDM. The proposed approach comprises four stages: classification of decision makers (DMs), determination of community weights, consensus reaching process, and alternative selection. A real-world application is utilized to demonstrate the effectiveness of the proposed method, and a comparison with existing related works highlights the advantages and innovation of the proposed model.

Maximum Utility Consensus with Inequity Aversion in Social Network Group Decision Making

Maximum Utility Consensus with Inequity Aversion in Social Network Group Decision Making

Trust exploration- and leadership incubation- based opinion dynamics model for social network group decision-making: A quantum theory perspective

Social network (SN) holds significant sway over the consciousness and behavior of involved individuals, serving as an evolutionary medium for opinion dynamics models. Consensus is a fundamental concept in group decision-making (GDM). How to effectively explore the opinion evolution during the consensus-reaching process through SN is of paramount significance for decision-making science. Therefore, a dual-mechanism containing trust exploration and leadership incubation is developed for modeling opinion dynamics, creating a favorable condition for achieving consensus. First, a novel mechanism for analyzing the completeness of SN is proposed, encompassing a trust propagation process that considers trust discount and stability, as well as a trust aggregation method grounded by quantum theory. Second, a trust screening rule is discussed to retain the valid indirect trust relationships (TRs), and then a leadership incubation mechanism is developed to promote the effective achievement of consensus opinions in group decision-making. Finally, a numerical study is presented to elucidate the superiority and rationality of the proposed methods, and some simulation experiments and comparative analyses demonstrating the effectiveness and advantages of which are covered.

A personalized bidirectional feedback mechanism by combining cooperation and trust to improve group consensus in social network

In the context of social network group decision making, it is an essential step to exploit the feedback mechanism for consensus reaching process (CRP) to obtain group-supported decision results. The CRP typically involves both concordant and discordant decision-makers (DMs), and the bidirectional interaction between them is regarded as a preferable approach to promote consensus. However, the existing bidirectional feedback models cannot provide personalized recommendation advice for DMs without objectively considering the DMs’ trust relationship during the interaction process. To address this issue, this paper utilizes the Uninorm propagation operator to establish the trust relationship among group DMs, which is used to generate personalized recommendation advices to modify their preference. In addition, it defines the concept of the cooperation degree index (CDI) to explore the ‘cooperative behavior’ of DMs during interaction, and then it is able to prevent over adjusting behavior. Therefore, this article establishes a novel personalized bidirectional feedback mechanism, which can reduce the opinion loss of individuals while enhancing fairness in the CRP. Finally, through case studies and discussions, the effectiveness and feasibility of the proposed approach is validated.

Game-Theoretic Expert Importance Evaluation Model Guided by Cooperation Effects for Social Network Group Decision Making

Game-Theoretic Expert Importance Evaluation Model Guided by Cooperation Effects for Social Network Group Decision Making

Decayed Trust Propagation Method in Multiple Overlapping Communities for Improving Consensus Under Social Network Group Decision Making

Decayed Trust Propagation Method in Multiple Overlapping Communities for Improving Consensus Under Social Network Group Decision Making

Three-dimensional dynamic trust-driven consensus model for social network group decision-making with application to sustainable supplier selection in a circular economy

The trust degree between individuals plays an important role in social network group decision-making (SNGDM). The majority of current literature assumes that the trust degree between individuals is constant. However, the trust values among decision-makers (DMs) are subject to change over time. Thus, it is necessary to identify potential dynamic trust that is compatible with the actual SNGDM to support the consensus reaching. For this purpose, this article investigates consensus building that considers dynamic trust among DMs in SNGDM with linguistic distribution assessments (LDAs). Firstly, the three-dimensional trust degree among DMs is constructed from three perspectives: the trust relationship from social networks, the confidence level of DMs, and the similarity of DMs' preferences. Secondly, an optimization model is developed with the objective of maximizing consensus to determine how trust degree is assigned to each perspective, thereby determining the weights of DMs. Then, a double feedback-based consensus mechanism incorporating both opinion evolution and trust evolution is developed. Under the guidance of consensus mechanism, an improved SNGDM approach with LDAs is presented. Finally, we demonstrate our proposed approach through a case study of sustainable supplier selection in a circular economy. Comparative analysis and sensitive analysis verify our approach’s effectiveness.

Consensus Model of Social Network Group Decision-Making Based on Trust Relationship Among Experts and Expert Reliability

Due to people's increasing dependence on social networks, it is essential to develop a consensus model considering not only their own factors but also the interaction between people. Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making. This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making (SN-GDM). A concept named matching degree is proposed to measure expert reliability. Meanwhile, linguistic information is applied to manage the imprecise and vague information. Matching degree is expressed by a 2-tuple linguistic model, and experts' preferences are measured by a probabilistic linguistic term set (PLTS). Subsequently, a hybrid weight is explored to weigh experts' importance in a group. Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus. Finally, a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

Heterogeneous Opinion Dynamics Considering Consensus Evolution in Social Network Group Decision-Making

Heterogeneous Opinion Dynamics Considering Consensus Evolution in Social Network Group Decision-Making

A minimum cost-maximum consensus jointly driven feedback mechanism under harmonious structure in social network group decision making

This article investigates a minimum cost-maximum consensus jointly driven feedback mechanism under a harmonious power structure by twofold group and individual attention recommendations for building social network consensus. Harmonious power structure is first constructed with subgroup-centrality-IOWA operator by (i) extracting subgroup importance rankings through social network analysis, and (ii) minimising group structure conflict to search the harmony weight allocation. Subsequently, a twofold attention recommendation approach that balances group attention and individual attention is proposed to generate feedback recommendations for the feedback recipients. Based on this, optimisation models that minimise individual adjustment cost and maximise group consensus are constructed, jointly driving the feedback mechanism. Finally, a demonstration example is provided to illustrate the efficacy of the proposed model.

Managing fairness and consensus based on individual consciousness of preventing manipulation

Decision makers (DMs) within a social network can communicate and compare with each other on recommendations and compensations received from moderator, which may arise the feelings of being manipulated and unfairness respectively. To prevent these two feelings from destroying the consensus, this paper proposes a maximum fairness consensus model based on individual consciousness of preventing manipulation (MFCM-PM) to provide reliable solutions in the social network group decision making. Specifically, we measure DMs’ fairness level based on compensations and trust relationships, which is inspired by social comparison and Gini coefficient. Furthermore, we propose a mechanism that triggers DMs' consciousness of preventing manipulation which will lead to the rejection regarding recommendations in consensus reaching process. Then, we construct the MFCM-PM model and investigate its several properties. Finally, for the proposed model, a numerical example is used to illustrate its applicability and simulation analyses are conducted to explore its effectiveness.

The reconciliation mechanism by cooperative intention index for managing non-cooperative behaviors in social network group decision making

Social network group consensus is a hybrid cooperative and non-cooperative problem, with the management of non-cooperative behavior as a key topic. In response, this study investigates the reconciliation mechanism to manage non-cooperative behaviors based on a cooperative intention index (CII). First, a CII index driven by preference similarity and trust relationships is constructed, which predicts the compromise domain of decision makers (DMs). Subsequently, this study recognizes three types of non-cooperative behaviors from the group and individual proposal perspectives in turn: (i)Type-1 cooperation with the group; (ii)Type-2 non-cooperation with the group but cooperation with the individual; and (iii)Type-3 non-cooperation with both the group and the individual. Then, reconciliation mechanisms applicable to different types of mediation are proposed. Then, group-orientated, individual-orientated and compulsory reconciliation mechanisms are proposed for different types. Finally, a demonstration example and discussions are provided to illustrate the efficacy of the proposed model.

The Overlapping Community-Driven Feedback Mechanism to Support Consensus in Social Network Group Decision Making

Social network group decision-making (SN-GDM) provides valuable support for obtaining agreed decision results by effectively utilizing the connected social trust relationships among individuals. However, the impact of intricately overlapped social trust relationships within overlapping communities on evaluation modifications in the SN-GDM consensus reaching process is seldom considered. To alleviate this issue, this study attempts to construct an overlapping community driven feedback mechanism for improving consensus in SN-GDM. The Lancichinetti-Fortunato method (LFM) is used to detect the overlapping community structures under social trust networks. Subsequently, the trusted recommendation advice is conducted within overlapping communities, which guides the inconsistent subgroups to make an interaction with each other to reach higher consensus level. Then, an associated feedback mechanism for SN-GDM with overlapping communities is proposed, which enables the inconsistent subgroups to minimize the consensus cost by selecting personalized feedback parameters. Moreover, it shows that the overlapping communities based feedback mechanism is superior to the feedback mechanisms with non-overlapping communities. Finally, an illustrative example is included, which is also used to testify the efficacy of proposal by comparing the consensus cost under different representative recommendation advice in overlapping social trust networks.

Grey relational analysis- and clustering-based opinion dynamics model in social network group decision making

In combination with social networks, opinion dynamics models can effectively describe the interaction and exchange of opinions between experts in social network group decision-making (SNGDM). Usually, the opinion dynamics model allows all experts to be involved in the interaction process simultaneously. However, if a subgroup has a dominant influence in SNGDM, the authoritative subgroup can easily control the group opinion. Therefore, we propose the grey relational analysis (GRA)- and clustering-based opinion dynamics model to avoid this situation. First, this paper applies a new operator to social network analysis (SNA) to obtain complete social relationships among experts. Next, we propose an improved k-means clustering method based on experts’ social relationships and opinion similarity. Then, based on SNA, GRA, and clustering results, a novel opinion dynamics model is proposed to weaken the influence of the authoritative subgroup. Finally, the validity of the proposed model is demonstrated through numerical examples and comparative analyses.

A social network group decision making framework with opinion dynamics considering opinion reliability

Opinion dynamics play an important role in the consensus reaching process (CRP) when tackling social network group decision making (SNGDM) problems. Opinion reliability can be regarded as an important characteristic of expert to indicate the reliability of experts’ opinions in social trust network. However, it has rarely been considered into the opinion evolution process in SNGDM. To explore the impact of opinion reliability on group consensus reaching, a SNGDM framework with opinion dynamics considering opinion reliability is proposed in this paper. Firstly, opinion reliability of experts in social trust network is defined, and the comprehensive trust degree based on the social network structure and individual characteristic is proposed. Secondly, trust propagation and aggregation mechanisms are designed to obtain the social trust matrix. Thirdly, considering opinion similarity and opinion reliability, social network evolution rules and opinion evolution rules based on the extended Hegselmann-Krause (HK) model are presented. Finally, the proposal is applied to two numerical experiments about supplier performance evaluation and Zachary's karate club, and the simulation and comparison analyses are provided to demonstrate the convergence and illustrate the feasibility and effectiveness of the proposed model.

Consensus manipulation in social network group decision making with value-based opinion evolution

Opinion evolution is a common phenomenon in social network group decision-making (SNGDM), where the key issue is modelling trust weights among individuals. In social and economic networks, the links among individuals represent trust relationships that can offer value. However, extant studies on trust weight distribution have failed to consider individual network values. Moreover, in some scenarios, the external stakeholder may want to manipulate the opinion evolution process to achieve the desired consensus outcome. Therefore, this study proposes value-based opinion evolution and discusses consensus manipulation in SNGDM. First, we measured the total value of an individual in a network, which consisted of two parts: intrinsic value and network value. Notably, we used the constant elasticity of substitution from economics as an operator to aggregate intrinsic and network values. Based on this, we described value-based opinion evolution and established an optimisation-based consensus manipulation model for SNGDM with value-based opinion evolution (CM-VBOE). Furthermore, we conducted some sensitive analyses, which indicate that an individual’s value in the network increases with the decay factor and decreases with the elasticity of substitution, and provide a comparative analysis, which expands on the importance of accounting for the decay factor and elasticity of substitution when modelling value-based opinion evolution. Finally, a hypothetical application for crowdfunding project selection is presented to illustrate the applicability of the proposed CM-VBOE.

Minimum Cost Consensus With Altruism Utility Constraints in Social Network Group Decision Making

In group decision making (GDM), considering the individuals’ satisfaction (i.e., utilities) about the consensus result is important. However, this issue is ignored by the extant minimum cost consensus models (MCCMs). Altruism describes a way of behaving that the individuals unselfishly concern for the utilities of others. Inspired by this, in this article we discuss the minimum cost consensus problem in social network GDM (SNGDM), where the utility constraints based on altruism are first incorporated. In SNGDM, altruism is reflected as the individuals will take both their personal utility and the utilities of others who connected with them into account to form their total utility. Based on this, we first define the individuals’ altruistic utility functions in the consensus process of SNGDM, and we propose several interesting properties of the proposed utility function. Afterward, we present a novel MCCM in SNGDM with opinion dynamics and altruistic utility (MCCM-OD-AU). Notably, in the proposed model, the deviation of opinion adjustment in the consensus process is measured as a psychological distance perceived by the individuals. Furthermore, some simulation studies and comparative analysis are conducted to investigate the effects of the altruistic behavior and the psychological distance of opinion adjustment on the consensus reaching results. Finally, two numerical studies, including an illustrative example and an application in energy-saving target formulation with real-world social network data, are provided to justify the performance of our proposal.