Browsing by Author "Wu, Jian"

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    A bilateral negotiation mechanism by dynamic harmony threshold for group consensus decision making
    (Elsevier, 2024-03-16) Cao, Mingshuo; Chiclana, Francisco; Liu, Yujia; Wu, Jian; Herrera-Viedma, Enrique
    This article proposes a framework for bilateral negotiation mechanism to deal the case the concordant decision-makers (DMs) coalition cannot be constructed, which resolves the limitations of the existing group decision making methods. Bilateral negotiation means a process in which any two involved DMs change their own opinions based on each other’s opinions, avoiding the formation of group coalitions and the coercion of individual DMs. It can not only improve group consensus by interaction between individual DMs, but also considers the limited compromise behavior of DMs in the consensus bargaining process. The key contributions of this article contain: (1) It investigates the concept of ‘harmony threshold’ by combining the consensus levels of individual DMs and the number of group members to explain the limited compromise behavior of DMs. (2) it proposes a novel bilateral negotiation consensus mechanism with personalized compromise behavior with the group consensus threshold as the objective function and personalized harmony thresholds as constraints to help any two discording DMs partly to adopt each other’s opinions. And (3) It develops the ranking difference level (RDL) to measure the deviation degree between the final ranking of alternatives and all the DMs’ original rankings of alternatives. The research found that the proposed mechanism can reduce consensus cost by 40% and ranking difference by 5%.
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    A dynamic trust and prospect theory driven bilateral feedback mechanism for maximizing consensus income in social network group decision making
    (Elsevier, 2024-12-30) Zhu, Zhaoguang; Zhang, Xiang; Cao, Mingshuo; Chiclana, Francisco; Wu, Jian
    This article proposes a prospect theory-based bilateral feedback mechanism with dynamic trust to reach group consensus under social network. A trust evolution model is developed by the concept of trust gap to reflect the dynamic changes in the trust relationships between DMs. The concept of a loss prospect threshold is then proposed, combining dynamic trust and consensus index, to quantitatively describe the maximum acceptable psychological loss for DMs in each round of feedback. Additionally, two indexes are defined to study feedback behavior: the improvement of consensus level as an income prospect and the preference adjustment as a loss prospect. Therefore, a bilateral feedback optimization model is constructed by maximizing the consensus income prospect under the limitation of the loss prospect threshold. To explore the role of dynamic trust and psychological behavior on the consensus-reaching process, three different feedback mechanisms are designed and compared with the proposed model, demonstrating that the proposed model can reduce preference adjustment costs and improve satisfaction with the final decision. A numerical example with sensitivity analysis of parameters is provided to illustrate the feasibility of the proposed model.
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    A minimum cost-maximum consensus jointly driven feedback mechanism under harmonious structure in social network group decision making
    (Elsevier, 2023-10-30) Wang, Sha; Chiclana, Francisco; Chang, Jia Li; Xing, Yumei; Wu, Jian
    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.
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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 transformation method of non-cooperative to cooperative behavior by trust propagation in social network group decision making
    (IEEE, 2025-04-04) Gai, Tiantian; Chiclana, Francisco; Jin, Weidong; Zhou, Mi; Wu, Jian
    In the consensus reaching process (CRP) of social network group decision making (SN-GDM), the non-cooperative behavior exhibited by experts will hinder the achievement of group consensus. This paper develops a non-cooperative behavior management framework based on trust propagation and dynamic cooperation index under bidirectional feedback context. On the one hand, a trust propagation operator with trust decay is established to enhance the trust relationship between non-cooperative experts; On the other hand, the fuzzy preference relations are utilized as preference expression structure, and the mutual reinforcing effect between consensus and trust is explored to achieve the dynamic enhancement of cooperation index, thereby facilitating the transformation of non-cooperative behavior. Specifically, a cooperation index is formulated to identify the non-cooperation behavior. Subsequently, a non-cooperative behavior transformation method by dynamic cooperation index is investigated. Finally, a bidirectional feedback mechanism is provided for group consensus reaching. This paper provides an innovative strategy for detecting and managing non-cooperative behavior, an illustrative example and some analyses are presented to verify the validity of 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 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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    An inter-subgroup compensation mechanism by Nash bargaining game for managing non-cooperative behavior in group decision making
    (Elsevier, 2025-04-19) Yang, Jie; Wu, Jian; Chiclana, Francisco; Cao, Mingshuo; Yager, Ronald R.
    Non-cooperative behavior exhibited by DMs when they must make excessive interest compromises hinders the achievement of group consensus. This study develops an inter-subgroup compensation mechanism using the Nash bargaining game under the minimum cost consensus model (MCCM) framework to managing non-cooperative behavior. First, a cooperative acceptability index (CAI) based on compromise limit costs is proposed to objectively identify non-cooperative behavior. By quantifying the acceptable compromise limit costs, the CAI ensures that consensus adjustments remain within acceptable bounds. Then, an inter-subgroup compensation mechanism is designed using the Nash bargaining game from the perspective of Kaldor–Hicks improvement. This mechanism enables cooperative DMs to incentivize non-cooperative peers via resource transfers, achieving dual optimization by minimizing collective costs and ensuring individual acceptability. Finally, a community renewal application example and comparison analysis are provided to illustrate the efficacy of the proposed approach.
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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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    An attitudinal trust recommendation mechanism to balance consensus and harmony in group decision making
    (IEEE, 2019-01-25) Wu, Jian; Li, Xue; Chiclana, Francisco; Yager, Ronal R.
    This article puts forward a trust based framework for building a recommendation mechanism for consensus in group decision making with interval-valued intuitionistic fuzzy information. To do that, it first presents an attitudinal trust model where experts assign trust weights to others considering the concept of attitude of the group. This approach allows for the implementation of the group attitude in a continuous scale ranging from a pessimistic attitude to an indifferent attitude. Thus, it can express the continuous trust status, and consequently it generalizes the traditional simplified trust model: ‘trusting’ and ‘distrusting’. In particular, three typical policies are defined as: ‘extreme trust policy’, ‘bounded trust policy’ and ‘indifferent trust policy’. Secondly, the attitudinal trust induced recommendation mechanism is established by a reasonable rule: the closer the experts, the higher their trust degree. This can guarantee that the consensus level of the inconsistent expert is increased after adopting the recommended advices. In addition to group consensus, experts envisage to keep their original opinions as much as possible. A harmony degree (HD) is defined to determine the extent of the difference between an original opinion and the corresponding revised opinion after adopting the recommended advices. Combining the HD index and the consensus index, a sensitivity analysis with attitudinal parameter is proposed to verify the rationality of the proposed attitudinal trust recommendation mechanism. In practice this will facilitate the inconsistent experts to achieve a balance between consensus degree and harmony degree by selecting an appropriate attitudinal parameter.
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    A bargaining game based feedback mechanism to support consensus in dynamic social network group decision making
    (Elsevier, 2023-01-14) Xing, Yumei; Wu, Jian; Chiclana, Francisco; Yu, Gaofeng; Cao, Mingshuo; Herrera-Viedma, Enrique
    A bargaining game is used to develop feedback mechanism for dynamic social networks group decision making (SN-GDM). The dynamic trust relationships between experts are updated by the change of their consensus state after each round of interaction. Then, a maximum entropy model based on individual interactive relationship and fairness is established to determine the comprehensive weight of each expert, which considers: (1) the individual weight by influence of expert; (2) the interaction weight by social relationships of experts. Hence, 2-tuple linguistic collective evaluation matrix of the 2-additive Choquet integral under Möbius transform is put forward. Further, the equilibrium solution of two experts in the bargaining game is established, and then this equilibrium recommendation will be accepted by both experts. Consequently, a bargaining game based feedback mechanism driven by trust relationship is proposed to reflect the interaction behaviors between the inconsistent expert and her/his most trusted consistent one, and therefore the recommendation advices are generated for them to promote consensus in SN-GDM. Finally, a sustainable supplier selection example demonstrates the effectiveness of the proposed approach.
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    A bidirectional feedback mechanism for balancing group consensus and individual harmony in group decision making
    (Elsevier, 2021-06-01) Cao, Mingshuo; Wu, Jian; Chiclana, Francisco; Herrera-Viedma, Enrique
    This article proposes a bidirectional feedback mechanism for consensus in group decision making (GDM) driven by the behavior of decision makers (DMs), which is discriminated with a flexible harmony degree as one of three possible states: (1) ‘tolerance behavior’; (2) ‘rationalist behavior’; and (3) ‘conflict behavior’. The first two states are possible to be resolved in the consensus reaching process with one round of feedback recommendations to the discordant DMs. However, in the conflict state, which implies the lack of harmony between the group aim of ‘consensus’ and the individual benefit, it is unreasonable to be resolved with only discordant DMs’ feedback recommendations, and concordant DMs are also expected to make concessions at some degree. To address this not so unusual research problem, a theoretical bidirectional feedback mechanism framework for consensus is developed. Firstly, a maximum consensus driven feedback model is proposed to resolve ‘conflict behavior’ between the concordant and discordant DMs. Secondly, a maximum harmony driven feedback model is activated to support the discordant DMs to reach the threshold values of group consensus. A numerical example is provided to illustrate and verify the proposed mechanism usefulness and how it compares against other existent feedback mechanisms in terms of the extent up to which DMs’ preferences are changed for reaching consensus.
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    A consensus group decision making method for hotel selection with online reviews by sentiment analysis
    (Springer, 2022-01-17) Wu, Jian; Ma, Xiaoao; Chiclana, Francisco; Liu, Yujia; Wu, Yang
    This paper proposes a framework for hotel selection based on online reviews by sentiment analysis from the perspective of consensus group decision making. To identify multi-granularity sentiment strength in text reviews, a sentiment analysis method based on the Word2Vec algorithm and one-vs-one strategy based Support Vector Machine (OVO-SVM) algorithm is provided. Then, richer information content can be derived from online text reviews, which are used as the data source of this study. To help members make an aggregation on the preference of hotel attributes, a consensus model with an improved feedback mechanism is proposed, which can reasonably control the adjustment cost in the consensus reaching process. Combining the hotel performance obtained from online reviews and the group preference consensus, the optimal hotel for members can be selected. At the end of this paper, a case study is presented to illustrate the use of the proposed method.
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    Consensus in Group Decision Making and Social Networks
    (ICI Publishing House, 2017-09) Herrera-Viedma, Enrique; Cabrerizo, Francisco Javier; Chiclana, Francisco; Wu, Jian; Cobo, Manuel Jesus; Konstantin, Samuylov
    The consensus reaching process is the most important step in a group decision making scenario. This step is most frequently identified as a process consisting of some discussion rounds in which several decision makers, which are involved in the problem, discuss their points of view with the purpose of obtaining the maximum agreement before making the decision. Consensus reaching processes have been well studied and a large number of consensus approaches have been developed. In recent years, the researchers in the field of decision making have shown their interest in social networks since they may be successfully used for modelling communication among decision makers. However, a social network presents some features differentiating it from the classical scenarios in which the consensus reaching processes have been applied. The objective of this study is to investigate the main consensus methods proposed in social networks and bring out the new challenges that should be faced in this research field.
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    Consensus-trust driven bidirectional feedback mechanism in social network group decision making.
    (Springer, 2022-09-17) Gai, Tiantian; Cao, Mingshuo; Chiclana, Francisco; Zhang, Zhen; Dong, Yucheng; Herrera-Viedma, Enrique; Wu, Jian
    This paper proposes a consensus-trust driven framework of bidirectional interaction for social network large-group decision making. Firstly, the concepts of interaction consensus threshold and interaction trust threshold are defined, which are used to discriminate the interaction modes between subgroups into four categories. Corresponding hybrid feedback strategies are designed in which the consensus level and trust level of subgroups are regarded as reliable resources to facilitate the achievement of group consensus. Secondly, a minimum adjustment bidirectional feedback model considering cohesion is developed to help the interacting subgroups reach mutual consensus with minimum opinion modification. Finally, the proposed consensus framework is applied to a blockchain platform selection problem in supply chain to demonstrate the effectiveness and applicability of the model.
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    Consistency based estimation of fuzzy linguistic preferences. The case of reciprocal intuitionistic fuzzy preference relations
    (IEEE, 2014-07) Chiclana, Francisco; Herrera-Viedma, Enrique; Wu, Jian
    The decision-making assumption of all experts being able to express their preferences on all available alternatives of a decision-making problem might be considered unrealistic. This is specially true when the number of alternatives is considerable high and/or when sources of information are conflicting and dynamic. Thus, the presence of incomplete information, which is not equivalent to low quality information, is worth investigation and its processing within decision-making processes desirable. A consistency based approach to deal with incomplete fuzzy linguistic preferences is the focus of this contribution. Consistency is considered here as linked to the transitivity of preferences, and in particular to Tanino’s multiplicative transitivity property of reciprocal fuzzy preference relations. The first result presented is the formal modelling and representation of Tanino’s multiplicative transitivity property to the case of fuzzy linguistic preference relations. This is done via Zadeh’s extension principle and the representation theorem of fuzzy sets. The second result derives the multiplicative transitivity property of reciprocal intuitionistic fuzzy preference relations, which can be isomorphically mapped to a particular type of linguistic preference relation: reciprocal interval-valued fuzzy preference relations. The third result is the computation of the consistency based estimated reciprocal intuitionistic fuzzy preference values using an indirect chain of alternatives, which can be used to address incomplete information in decision-making problems with this type of preference relations.
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    Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets
    (Wiley, 2019-01-29) Urena, Raquel; Kou, Gang; Wu, Jian; Chiclana, Francisco; Herrera-Viedma, Enrique
    Nowadays in the social network based decision making processes, as the ones involved in e-commerce and e-democracy, multiple users with di erent backgrounds may take part and diverse alternatives might be involved. This diversity enriches the process but at the same time increases the uncertainty in the opinions. This uncertainty can be considered from two di erent perspectives: (i) the uncertainty in the meaning of the words given as preferences, that is motivated by the heterogeneity of the decision makers, (ii) the uncertainty inherent to any decision making process that may lead to an expert not being able to provide all their judgments. The main objective of this contribution is to address these two type of uncertainty. To do so the following approaches are proposed: Firstly, in order to capture, process and keep the uncertainty in the meaning of the linguistic assumption the Interval Type 2 Fuzzy Sets are introduced as a way to model the experts linguistic judgments. Secondly, a measure of the coherence of the information provided by each decision maker is proposed. Finally, a consistency based completion approach is introduced to deal with the uncertainty presented in the expert judgments. The proposed approach is tested in an e-democracy decision making scenario.
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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.