Browsing by Author "Liang, Changyong"

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    An Incentive Mechanism-Based Minimum Adjustment Consensus Model Under Dynamic Trust Relationship
    (IEEE, 2024-01-24) Xing, Yumei; Wu, Jian; Chiclana, Francisco; Liang, Changyong; Yager, Ronald R.
    In traditional group decision making, the inconsistent experts are usually forced to make compromises toward the group opinion to increase the group consensus level. However, the strategy of reaching group consensus via an incentive mechanism encouraging adjustment of preferences is more effective than forcing, which is the aim of this article. Specifically, this article establishes a novel incentive mechanism to support group consensus under dynamic trust relationship. First, the supremum and infimum incentives-based rule driven by trust relationship is defined. Based on the assumption that if incentive conditions are met, then experts will be willing to adjust their preferences, the incentive behavior-driven minimum adjustment consensus model is developed to generate optimal incentive-based recommendation preferences. Thus, the proposed incentive mechanism can effectively reduce the preference adjustment cost and promote group consensus reaching. Third, the updated trust relationships between experts are shown to be strengthen by the proposed incentive-driven preference revision. Consequently, the optimization model based on trust interaction relationship is constructed to obtain the final group preference matrix. Finally, a supplier selection case of high-end medical equipment is provided to illustrate the proposed method and show the rationality and advantages of the proposed methodology with both a sensitivity analysis and a comparison analysis.
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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 decentralized feedback mechanism with compromise behavior for large-scale group decision making with application in smart logistics supplier selection
    (Elsevier, 2022-05-18) Gai, Tiantian; Cao, Mingshuo; Chiclana, Francisco; Wu, Jian; Liang, Changyong; Herrera-Viedma, Enrique
    This paper proposes a decentralized feedback mechanism to help large-scale decision makers (DMs) reach consensus considering the limited compromise behavior of subgroups. First, a novel decentralized opinion interaction mechanism is designed to identify the most inconsistent subgroups pair in the group and make them interact with each other. Then it is different from the centralized interaction mechanism, which adopts the aggregation of the opinions of other subgroups. The former is suitable for situations with low initial consensus level while the later is for high consensus level. Secondly, the compromise behavior of subgroups in the feedback process is explored, and then the concept of ‘compromise threshold’ (CT) is defined to analyze the limited compromise behavior of subgroups. Finally, an illustrative example regarding the smart logistics supplier selection (SLEC) is described to demonstrate the effectiveness and advantages of the proposed mechanism.
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    A knowledge coverage-based trust propagation for recommendation mechanism in social network group decision making
    (Elsevier, 2020-12-16) Liu, Yujia; Liang, Changyong; Chiclana, Francisco; Wu, Jian
    Trust is a typical relationship in social network, which in group decision making problems relates to the inner relationship among experts. To obtain a complete trust relationship of a networked group of experts, firstly, a novel knowledge coverage-based trust propagation operator is proposed to estimate the trust relationship between pairs of unknown experts. The novelty of this trust propagation operator resides in its account of the domain knowledge coverage of experts. Desirable properties regarding boundary conditions, generalisation and knowledge coverage absorption are studied. The comparison with existing operators of boundary conditions shows the rationality of the proposed operator. Next, a knowledge coverage-based multi-paths trust propagation model for constructing complete trust network is investigated. The proposed approach aggregates all trust paths to collect all trust information and penalise trust decay. Secondly, a trust order induced recommendation mechanism is proposed by combining subjective and objective weights. Thus, experts can accept consensus recommendations by subjective and objective trust. This recommendation mechanism allows the inconsistent experts to accept the advices they trust. The validity and rationality of the proposed recommendation mechanism is mathematically proved, and a numerical example is utilised to illustrate the calculation process of the proposed method.
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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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    A trust induced recommendation mechanism for reaching consensus in group decision making
    (Elsevier, 2016-12-18) Chiclana, Francisco; Liu, Yujia; Liang, Changyong; Wu, Jian
    This article addresses the inconsistency problem in group decision making caused by disparate opinions of multiple experts. To do so, a trust induced recommendation mechanism is investigated to generate personalised advices for the inconsistent experts to reach higher consensus level. The concept of trust degree (TD) is defined to identify the trusted opinion from group experts, and then the visual trust relationship is built to help experts ‘see’ their own trust preferences within the group. Consequently, trust based personalised advices are generated for the inconsistent experts to revisit their opinions. To model the uncertainty of experts, an interval-valued trust decision making space is defined. It includes the novel concepts of interval-valued trust functions, interval-valued trust score (IVTS) and interval-valued knowledge degree (IVKD). The concepts of consensus degree (CD) between an expert and the rest of experts in the group as well as the harmony degree (HD) between the original opinion and the revised opinion are developed for interval-valued trust functions. Combining HD and CD, a more reasonable policy for group consensus is proposed as it should arrive at the threshold value with the maximum value of harmony and consensus degrees simultaneously. Furthermore, because the trust induced recommendation mechanism focuses on changing inconsistent opinions using only opinions from the trusted experts and not from the distrusted ones, the HD based changes cost to reach the threshold value of consensus is lower than previous mechanisms based on the average of the opinion of all experts. Finally, once consensus has been achieved, a ranking order relation for interval-valued trust functions is constructed to select the most appropriate alternative.
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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.