Browsing by Author "Sun, Qi"

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    A self-esteem driven feedback mechanism with diverse power structures to prevent strategic manipulation in social network group decision making
    (Elsevier, 2025-01-02) Sun, Qi; Zhang, Xiang; Chiclana, Francisco; Ji, Feixia; Long, Qingqi; Wu, Jian
    In social network group decision-making (SNGDM), the distribution of power structures and strategic manipulation behaviors pose challenges to the fairness and efficiency of the decision-making process. This paper introduces a novel consensus theoretical framework, specifically designed for analyzing power structures and preventing strategic manipulation behavior in SNGDM. It proposes a centrality measures-based influence index and a structural holes and graph density-based power index, respectively, to identify opinion leaders and power dynamics of subgroups in social trust networks. Then, a maximum entropy-based model is presented to explore power dynamics for preference aggregation in SNGDM. Furthermore, this paper introduces a feedback model based on the boundary maximum consensus degree, addressing issues that existing consensus methods tend to overlook, including the self-esteem of decision-makers and the risks of manipulation behavior. The model considers the self-esteem of subgroups when adjusting preferences, aiming to prevent potential strategic manipulation and enhance the fairness and efficiency of decision-making. Finally, thorough numerical evaluations and comparative assessments have been conducted to substantiate the effectiveness of the proposed methodology. Experiment results show that concentrated power can speed up consensus formation but may harm fairness, while dispersed power, although it slows consensus, increases participation and diversity, reducing the risk of power abuse.
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    A tolerance index based non-cooperative behaviour managing method with minimum cost in social network group decision making
    (Elsevier, 2024-06-26) Sun, Qi; Wu, Jian; Chiclana, Francisco; Ji, Feixia
    This paper introduces a novel consensus theoretical framework designed to effectively manage non-cooperative behavior in social network group decision making (SNGDM). It addresses the challenge by considering both individuals’ willingness to adjust preferences and the associated costs of achieving consensus. To deal with this issue, the personalized individual semantics (PIS) model is employed to handle original evaluation matrices by converting linguistic terms into numerical values based on experts’ personalized opinions. Subsequently, a tolerance index (TI) is defined to reflect the willingness of experts to adjust their preferences. An improved minimum cost (MC) feedback model based on TI is established. The novelty of the proposed approach is that its integration of individual preference adjustment willingness and consensus efficiency, effectively preventing groupthink. In addition, a maximum group consensus degree optimisation model is proposed to detect non-cooperative behaviour of experts. To ensure an optimal solution for the minimum cost feedback model, a weight update method is proposed, considering the trust relationship between experts. A detailed analysis regarding the selection of tolerance thresholds to prevent over-penalisation of weights of non-collaborators is reported. Finally, comprehensive numerical and comparative analyses are presented to validate the proposed method.
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    A Trust Incentive Driven Feedback Mechanism With Risk Attitude for Group Consensus in Social Networks
    (IEEE, 2025-01-01) Ji, Feixia; Wu, Jian; Chiclana, Francisco; Sun, Qi; Herrera-Viedma, Enrique
    Trust relationships can facilitate cooperation in collective decisions. Using behavioral incentives via trust to encourage voluntary preference adjustments improves consensus through mutual agreement. This article aims to establish a trust incentive-driven framework for enabling consensus in social network group decision making (SN-GDM). First, a trust incentive mechanism is modeled via interactive trust functions that integrate risk attitude. The inclusion of risk attitude is crucial as it reflects the diverse ways decision makers (DMs) respond to uncertainty in trusting others’ judgments, capturing the varied behaviors of risky, neutral, and insurance DMs in the consensus process. Inconsistent DMs then adjust opinions in exchange for heightened trust. This mechanism enhances the importance degrees via a new weight assignment method, serving as a reward to motivate DMs to further align with the majority. Subsequently, a trust incentive-driven bounded maximum consensus model is proposed to optimize cooperation dynamics while preventing over-compensation of adjustments. Simulations and comparative analysis demonstrate the model’s efficacy in facilitating cooperation through tailored trust incentive mechanisms that account for these diverse risk preferences. Finally, the approach is applied to evaluate candidates for the Norden Shipping Scholarship, providing a cooperation-focused SN-GDM framework for achieving mutually agreeable solutions while acknowledging the impact of individual risk attitude on trust-based interactions.
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    An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making
    (Springer, 2022-12-13) Sun, Qi; Wu, Jian; Chiclana, Francisco; Wang, Sha; Herrera-Viedma, Enrique; Yager, Ronald R.
    In social network group decision making (SN-GDM) problem, subgroup weights are mostly unknown, many approaches have been proposed to determine the subgroup weights. However, most of these methods ignore the weight manipulation behavior of subgroups. Some studies indicated that weight manipulation behavior hinders consensus efficiency. To deal with this issue, this paper proposes a theoretical framework to prevent weight manipulation in SN-GDM. Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the power index (PI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’ manipulation behavior. The minimum adjustment rule aims for ‘efficiency’ while the maximum entropy rule aims for ‘justice’. The experimental results show that the proposed model can guarantee the rationality of weight distribution to reach consensus efficiently, which is achieved by maintaining a balance between ‘efficiency’ and ‘justice’ in the mechanism of assigning weights. Finally, the detailed numerical and simulation analyses are carried out to verify the validity of the proposed method.
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    An attitudinal consensus degree to control feedback mechanism in group decision making with different adjustment cost
    (Elsevier, 2018-11-03) Wu, Jian; Sun, Qi; Fujita, Hamido; Chiclana, Francisco
    This article aims to study the influence of the group attitude on the consensus reaching process in group decision making (GDM). To do that, the attitudinal consensus index (ACI) is defined to aggregate individual consensus levels to form a a collective one. This approach allows for the implementation of the group attitude in a continuous state ranging from a pessimistic attitude to an optimistic attitude. Then, ACI is used to build a stop policy to control feedback for consensus, which can be regarded as a generation of the traditional polices: `\emph{minimum disagreement policy}' and `\emph{indifferent disagreement policy}'. A sensitivity analysis method with visual simulation is proposed to check the adjustment cost and consensus level with different attitudinal parameters. The main conclusion from this analysis is that the bigger the attitudinal parameter implemented is, the bigger the adjustment cost and consensus level are. The visual information facilitates the inconsistent expert keeping a balance between the attitudinal parameter to implement and the adjustment cost and consensus level, which in practice translates into full control of such implementation based on the decision maker's willingness.
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    Decayed Trust Propagation Method in Multiple Overlapping Communities for Improving Consensus Under Social Network Group Decision Making
    (IEEE, 2024-05-09) Ji, Feixia; Wu, Jian; Chiclana, Francisco; Sun, Qi; Liang, Changyong; Herrera-Viedma, Enrique
    This article proposes a decayed trust propagation method among multiple overlapping communities, and establishes a trust-driven consensus model for social network group decision making (SN-GDM). On the one hand, the use of overlapping nodes simplifies trust propagation by bridging complex connections among multiply overlapping communities. On the other hand, trust models' accuracy and realism are enhanced with the concept of trust decay, which accounts for the temporal dynamics of trust propagation. Thus, a first objective of this paper is to develop a trust propagation operator based on trust decay among multiple overlapping communities by leveraging overlapping nodes. By incorporating overlapping nodes' diverse trust relationships and perspectives, this approach allows to achieve reliable sources for generating recommendations in SN-GDM. A second objective of this paper is to design a decayed trust propagation induced consensus model to determine the optimal combination of overlapping nodes and feedback parameters, while balancing consensus efficiency and interaction willingness. The innovation of this approach is grounded in its ability to avoid excessive group adjustment to reach consensus. Numerical examples and comparative analysis demonstrate the model's performance in achieving efficient consensus under various representative recommendations.
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    A dynamic feedback mechanism with attitudinal consensus threshold for minimum adjustment cost in group decision making
    (IEEE, 2021-02-08) Sun, Qi; Wu, Jian; Chiclana, Francisco; Fujita, Hamido; Herrera-Viedma, Enrique
    This article presents a theoretical framework for a dynamic feedback mechanism in group decision making (GDM) by the implementation of an attitudinal consensus threshold (ACT) to generate recommendation advice for the identified inconsistent experts with the aim to increase consensus. The novelty of the approach resides in its ability to implement the ACT continuously, which allows the covering of all possible consensus states of the group from its minimum to maximum consensus degrees. Therefore, it can be flexibly applied to GDM problems with different consistency requirements. A sensitivity analysis method with visual simulation is proposed to support the checking of the numbers of experts involved in the feedback process and the minimum adjustment cost associated with the different ACT intervals. Experimental results show that an increase in the ACT value will lead to an increase in the number of experts and adjustment cost involved in the feedback process. Eventually, a numerical example is included to simulate the feedback process under various decision making scenarios with different ACT intervals.
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    Supporting group cruise decisions with online collective wisdom: An integrated approach combining review helpfulness analysis and consensus in social networks
    (Elsevier, 2024-10-28) Ji, Feixia; Wu, Jian; Chiclana, Francisco; Sun, Qi; Liang, Changyong; Herrera-Viedma, Enrique
    Online cruise reviews provide valuable insights for group cruise evaluations, but the vast quantity and varied quality of reviews pose significant challenges. Further complications arise from the intricate social network structures and divergent preferences among decision-makers (DMs), impeding consensus on cruise evaluations. This paper proposes a novel two-stage methodology to address these issues. In the first stage, an inherent helpfulness level–personalized helpfulness level (IHL–PHL) model is devised to evaluate review helpfulness, considering not only inherent review quality but also personalized relevance to the specific DMs’ contexts. Leveraging deep learning techniques like Sentence-BERT and neural networks, the IHL–PHL model identifies high-quality, highly relevant reviews tailored as decision support data for DMs with limited cruise familiarity. The second stage facilitates consensus among DMs within overlapping social trust networks. A binary trust propagation method is developed to optimize trust propagation across overlapping communities by strategically selecting key bridging nodes. Building upon this, a constrained maximum consensus model is proposed to maximize group agreement while limiting preference adjustments based on trust-constrained willingness, thereby preventing inefficient iterations. The proposed model is verified with a dataset of 7481 reviews for four cruise alternatives. Finally, some comparisons, theoretical and practical implications are provided. Overall, this paper offers a comprehensive methodology for real-world group cruise evaluation, using online reviews from platforms like CruiseCritic as a form of collective wisdom to support decision-making.
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    Two stage feedback mechanism with different power structures for consensus in large-scale group decision-making
    (IEEE, 2022-01-21) Wang, Sha; Wu, Jian; Chiclana, Francisco; Sun, Qi; Herrera-Viedma, Enrique
    This paper investigates a two stage consensus feedback mechanism that considers different power structures in large scale group decision making (LSGDM) environments. A Louvain algorithm type is used to detect subgroups in LSGDM by trust relationship. The concepts of internal subgroups and external subgroup consensus levels are defined, and an approach to identify the inconsistent individual/subgroup is developed to avoid the issue of pseudoconsensus. Combined with the background of the companys shareholder power management regulations, three power structures are constructed: absolute power, relative power and democratic power. A two stage feedback mechanism is investigated with minimum adjustments to achieve the optimal power allocation under each power structure. This mechanism supports individuals reach consensus both inside and outside their subgroup. An illustrative example and discussions to verify the validity of the proposed method are reported.
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    Weight Penalty Mechanism for Noncooperative Behavior in Large-Scale Group Decision Making With Unbalanced Linguistic Term Sets
    (IEEE, 2023-03) Sun, Qi; Chiclana, Francisco; Wu, Jian; Liu, Yujia; Liang, Changyong; Herrera-Viedma, Enrique
    This paper proposes a novel framework to manage subgroups’ non-cooperative behavior by weight penalty in large scale group decision making (LSGDM). To do that, a trust consensus index (TCI) is defined by combining trust score and consensus degree among experts expressed by unbalanced linguistic term sets. A Louvain algorithm clustering process based on undirected graph composed of TCI is introduced to detect the subgroups in large network. Hence, a weight penalty feedback model is established to manage the subgroups detected as discordant and non-cooperative. The proposed method novelty resides in that the minimum adjustment cost can be obtained with respect to the penalty parameter. A detail analysis regarding the computation of the optimal penalty parameter to prevent excessive penalization is reported. Finally, a detailed numerical and comparative analyses are provided to verify the validity of the proposed method.