基于信任关系和互补判断矩阵的群决策方法

随着信息和网络技术的不断发展,基于社会网络的群决策问题受到越来越多研究者的关注.针对社会网络环境下模糊互补判断矩阵的群决策问题,研究群体共识调整过程和方案选择方法.首先,融合决策者之间的社会关系、身份地位、知识能力3个方面信息来构建决策者两两之间的信任关系;其次,提出一种尽可能减少元素间共识补偿的共识度度量方法,在此基础上建立基于信任关系的共识调整模型,并从理论上证明该模型的有效性;最后通过信任关系矩阵的特征向量中心度分别求出专家的重要性权重,用以集结专家的偏好信息和对方案进行排序选择,算例分析表明了所提出方法的有效性.     

Consensus reaching process for group decision-making based on trust network and ordinal consensus measure

Due to the urgent nature of emergency decision making, it is necessary to reach the consensus requirement quickly. Ordinal consensus measure explores the relation between the rankings and helps to intuitively know which alternative needs to be adjusted to accelerate the improvement of consensus. Moreover, decision makers (DMs) in the decision making problem are often connected through trust relationships which affect the DMs’ judgments in the process of DMs’ interaction. Therefore, this paper explores trust network-based group decision-making in which the consensus level is estimated by an ordinal consensus measure. We first focus on the supplementation of an incomplete trust network. One of the most common methods is to design the trust propagation operator, whereas the intensity of information propagation may be different in various scenarios. Therefore, considering the different numerical scale of the linguistic term set, a trust propagation operator with different intensity of trust propagation is designed to obtain the indirect trust relationship. In the process of supplementing the incomplete trust network, the contribution of DMs to propagating information is concerned, which can be described by the betweenness centrality, and the importance weights of DMs are determined by combining the betweenness centrality and trust in-degree. In the consensus reaching process, we first propose an improved ordinal consensus measure, which takes into account the consistency of orders of the same alternative in different rankings as well as the importance of positions of alternatives. Then, we design the identification rule and the feedback mechanism for those with low consensus levels. The identification rule is used to select the DMs which first few alternatives in the ranking are different with those in the ranking of group. And in the feedback mechanism, the referenced preference relation (FPR) obtained by the trust network is provided for the identified DMs. Afterwards, combining the referenced FPR, an optimization model is designed to give the adjustment opinion. Finally, a numerical example elaborates on the feasibility of the trust propagation operator and consensus model. The comparative analysis demonstrates the rationality and effectiveness of the proposed model.     

Trust based consensus model for social network in an incomplete linguistic information context

A theoretical framework to consensus building within a networked social group is put forward. This article investigates a trust based estimation and aggregation methods as part of a visual consensus model for multiple criteria group decision making with incomplete linguistic information. A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network and the trust score induced order weighted averaging operator is presented to aggregate the orthopairs of trust/distrust values obtained from different trust paths. Then, the concept of relative trust score is defined, whose use is twofold: (1) to estimate the unknown preference values and (2) as a reliable source to determine experts’ weights. A visual feedback process is developed to provide experts with graphical representations of their consensus status within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feedback process also includes a recommendation mechanism to provide advice to those experts that are identified as contributing less to consensus on how to change their identified preference values. It is proved that the implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process.     

A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network

This paper focuses on consensus reaching process (CRP) under social network in which the trust relationship expressed by linguistic information. A new feedback mechanism in social network group decision making (SN-GDM) is proposed, which mainly consists of the following two aspects: (1) The propagation of distributed linguistic trust is investigated to study trust relation among experts; (2) A maximum self-esteem degree based feedback mechanism is developed to produce personalized advice for reaching higher group consensus. To do so, a novel linguistic trust propagation method is proposed to obtain the complete trust relationship among group. The self-esteem degree is used to define the extent that an individual makes concessions. Then, a maximum self-esteem degree based optimal feedback mechanism is built to produce personalized advice to help inconsistent experts make change of their opinion. Its novelty lies in the establishment of an optimization model with the nonlinear group self-esteem degree function as the objective function while group consensus threshold as the restrictions. Therefore, the inconsistent experts will reach a group consensus with the minimum loss of self-esteem degree, and then, it achieves the optimal balance between individual self-esteem and group consensus. Finally, a ranking process is applied to derive the appropriate consensus solution.     

Multi-attribute group decision making with opinion dynamics based on social trust network

Social trust network (STN) has facilitated information exchange between experts during interactions. Some feedback mechanisms have been used to provide advices for opinion change to improve their consensus levels. However, they do not consider the experts’ willingness and their self-confidence values. To analyze the influence of the relationship between experts on the decision-making results, this paper proposes a multi-attribute group decision making (MAGDM) with opinion dynamics based on STN. Three stages are included in the proposed approach: trust propagation, consensus reaching process and alternative selection. In the trust propagation stage, the social weight influence matrix and the weights of experts are obtained based on the complete social trust matrix which is constructed by trust aggregation and the given self-confidence values of experts. In the consensus reaching process, the consensus measure is used to determine the consensus between the experts or not, and the feedback mechanism based on opinion dynamics is used to adjust the opinions which do not reach consensus. The appropriate alternative is selected based on the assessable value of the alternative in the selection process. Finally, a numerical experiment about supplier selection is introduced to illustrate the efficiency of the proposed approach and comparison analyses show that the proposed approach can improve efficiency compared with the MAGDM in the social network.     

A dynamic hybrid trust network-based dual-path feedback consensus model for multi-attribute group decision-making in intuitionistic fuzzy environment

This paper proposes a dual-path feedback consensus model based on dynamic hybrid trust relationships to solve multi-attribute group decision-making problems in intuitionistic fuzzy environment. This model comprises two main parts: (a) the construction of a dynamic hybrid trust network among decision makers (DMs) and (b) the formation of a dual-path feedback mechanism to improve the group consensus. In the first part, a hybrid trust network is constructed by combining DMs’ prior knowledge of each other and the preference similarities between them. Then, the hybrid trust network is dynamically updated iteratively to reflect the changes in the trust relationships in the process of joint decision-making. In the second part, DMs with low consensus degrees are identified and provided with either a preference or weight adjustment path to improve the group consensus. The preference adjustment path is activated for DMs who agree to modify their preferences, and a nonlinear programming model is proposed to help DMs improve consensus degrees while minimizing adjustment cost. The weight adjustment path is activated for DMs who stick to their own opinions and refuse to make changes, and their weights is adjusted accordingly. An illustrative example along with the related sensitivity analysis and comparative study are used to verify the effectiveness of the proposed model.     

A consistency and consensus based decision support model for group decision making with multiplicative preference relations

In group decision making (GDM) with multiplicative preference relations (also known as pairwise comparison matrices in the Analytical Hierarchy Process), to come to a meaningful and reliable solution, it is preferable to consider individual consistency and group consensus in the decision process. This paper provides a decision support model to aid the group consensus process while keeping an acceptable individual consistency for each decision maker. The concept of an individual consistency index and a group consensus index is introduced based on the Hadamard product of two matrices. Two algorithms are presented in the designed support model. The first algorithm is utilized to convert an unacceptable preference relation to an acceptable one. The second algorithm is designed to assist the group in achieving a predefined consensus level. The main characteristics of our model are that: (1) it is independent of the prioritization method used in the consensus process; (2) it ensures that each individual multiplicative preference relation is of acceptable consistency when the predefined consensus level is achieved. Finally, some numerical examples are given to verify the effectiveness of our model.     

两种基于个体偏好集结的多属性群决策共识方法

针对多属性群决策中的共识问题, 提出两种使群体达成共识的方法. 假设群体决策的结果以从个体偏好通过集结得到的群体偏好为基础, 在使用算术加权集结算子和几何加权集结算子的条件下, 分别设计相应的共识达成算法, 并对算法的收敛性进行分析. 与已有方法相比, 所提出算法能够体现决策个体的差异和决策个体对群体的影响. 通过某市政图书馆空调系统安装方案的选择表明了所提出方法的合理性和可行性.

     

社会网络环境下基于公众行为大数据属性挖掘的大群体应急决策方法及应用

针对社会网络环境下复杂大群体应急决策中决策属性信息难以获得问题,提出社会网络环境下公众行为大数据驱动的大群体应急决策方法.首先,通过挖掘社交平台上的公众行为大数据,利用TF-IDF、Word2vec技术进行关键词提取、聚类及其影响力分析,从大量行为数据中挖掘大群体决策属性信息以辅助专家决策,使决策结果具有更高的科学性和有效性;其次,构建决策者间基于信任关系和观点相似度的社会网络,采用同时考虑信任和相似度的聚类方法对决策者进行聚类,并基于社会网络分析获得决策者权重;然后,提出基于决策者间信任关系的共识调整方法进行共识调整以获得最终群体决策矩阵和方案排序,通过引入决策者客观自信度避免个别决策者过分自信行为的影响;最后,通过一个新冠疫情案例分析说明方法的可行性和有效性.     

社会网络环境下基于共识的风险性大群体应急决策非合作行为管理研究

针对大群体应急决策中可能存在的非合作行为,考虑决策专家的社会网络关系,提出一种基于共识的管理大群体应急决策中非合作行为的方法.首先,引入社会网络中基于模块度的Louvain聚类方法对大群体决策专家进行聚类,降低大群体应急决策复杂性;其次,定义两种非合作行为,并建立非合作行为的识别和检测模型;再次,定义信任风险系数、偏好风险系数以及综合风险系数,通过风险系数对不同程度的非合作行为聚集进行偏好调节,从而得到共识水平较高的决策方案;最后,利用“4.25西藏地震的案例”验证所提出方法的可行性和有效性.     

基于重要度引导偏好识别修正的多粒度语言共识模型

研究多粒度语言偏好信息下的群体共识决策问题. 首先, 从个体和群体两个角度充分挖掘偏好信息下隐含的专家重要度信息, 基于个体一致度及个体与群体的相似度构建确定专家重要度的优化模型; 其次, 以专家重要度引导非共识偏好的识别和修正过程, 提出一种自适应的语言共识模型; 然后, 给出一种群决策方法, 确保在集结专家意见前群体达成一定程度的共识; 最后, 通过算例验证所提出方法的可行性和有效性.

     

基于信任机制的不完全信息大群体决策方法

针对不完全偏好信息大群体决策问题, 引入访问控制中的信任机制, 建立直接信任度与推荐信任度, 提出一种基于信任机制的补值方法; 分析了基于距离相似度存在的问题, 定义了一种新的距离相似度, 并与余弦相似度结合, 构建了决策偏好二元相似度的相聚模型; 利用聚类方法求解决策成员的权重, 并与补值后的完整偏好矩阵进行合成, 求得决策方案排序. 最后, 利用一个现有的文献案例验证了所提出方法的有效性和优越性.

     

Consistency and consensus reaching process for group decision making based on complete interval distributed preference relations under social network analysis

Group decision-making (GDM) problems often consist of many indeterminacy factors in realistic situation. How to cope with consistency and consensus under uncertain circumstance are two critical issues in pairwise comparison based GDM problems. In this paper, we firstly propose the model of complete interval distributed preference relation (CIDPR) based on the concept of linguistic distribution with interval symbolic proportions, distribution linguistic preference relation (DLPR) and IDPR. Secondly, the additive consistency index of CIDPR is defined to measure the consistency level of expert's judgment, and an adjustment algorithm is proposed for converting inconsistent CIDPR to an acceptable consistent level. Thirdly, since trust relation is a critical factor in the generation of experts’ weights and the adjustment of experts’ opinions, consensus reaching process (CRP) is designed to take into account distributed linguistic trust relations under social network analysis (SNA). In the proposed adjustment mechanism, non-consensus individual should modify opinion towards his/her trusted and highly weighted expert. The advantage of the proposed inconsistent CIDPR adjustment model can maximally retain the information in the original distribution, while the CRP has a relatively fast convergent speed and good practicality. An illustrative example of strategic new product selection is conducted to demonstrate the applicability of the proposed method and its potential in supporting realistic GDM problems.     

Fairness concern: An equilibrium mechanism for consensus-reaching game in group decision-making

For many group decision-making (GDM) issues, such as water-resource allocation, urban resettlement, and traffic-route planning, the benefits of the decision makers (DMs) are closely related to the collective decision-making result. In fact, the consensus-reaching process is a game between the decision makers and the moderator. The fairness concern in GDM impacts DMs’ preference modification and influences consensus achievement, but it has long been ignored because most studies have focused on the consensus mechanism or cost. We introduced the fairness concern into the consensus-reaching process in GDM and established an equilibrium mechanism for GDM. Specifically, we proposed a general consensus game that considers the decision fairness concern. Then, we proved the existence of the Nash Equilibrium between the decision makers and the moderator and established a consensus model with a minimum compensation cost. A simulated annealing algorithm was designed to solve for an equilibrium solution. Through numerical examples used in previous studies and an application example of green supply chain management, we analyzed the relationship between fairness concern, the degree of coordination, and effectiveness in GDM and demonstrated the impact of the fairness concern on consensus achievement.     

Optimal consensus models for group decision making under linguistic preference relations

This paper proposes an optimal consensus model to derive weights for linguistic preference relations (LPRs). Two indexes, an individual‐to‐group consensus index (ICI) and a collective consensus index (CCI), are introduced. An iterative algorithm is presented to describe the consensus reaching process. By changing the weights and modifying a pair of individuals' comparison judgments—which have largest deviation value to the group judgments—the consensus reaching process can terminate, while both ICI and CCI are controlled with predefined thresholds. The algorithm aims to preserve the decision makers’ original information as much as possible. The model and algorithm are then extended to handle the uncertain additive LPRs. Finally, two examples are given to show the effectiveness of the proposed methods.     

An adaptive group decision making framework: Individual and local world opinion based opinion dynamics

Opinion dynamics (OD) models, which simulate individuals’ opinion evolution process on social network to analyze the final state of opinion distribution in a group, usually differ from each other due to the differences in social network evolution rules and opinion evolution rules. However, most existing social network evolution rules and opinion evolution rules usually cannot characterize the comprehensive influence of key factors such as neighbors and opinion differences in social relationships. To fully consider the properties of social network evolution and improve the efficiency of consensus reaching process in group decision making, this paper introduces the concept of local world opinion derived from individuals’ common friends, and then proposes an individual and local world opinion-based OD model. In the proposed model, social network evolution is jointly determined by the distance between individual opinions and network structure similarity. The pair of individuals with the largest consensus improvement space are then suggested to adjust their opinions by using an adaptive individual opinion adjustment mechanism. Finally, detailed simulation results are provided to demonstrate the convergence of the proposed model and analyze different parameters’ effects on the stabilized time steps and the number of stable state opinion clusters.     

犹豫模糊语言群决策的共识性模型

针对犹豫模糊语言群决策问题,研究其共识性调整方法.首先,定义犹豫模糊语言术语集的距离测度;然后,基于该距离测度定义犹豫模糊决策矩阵间的共识性水平及其相关概念,建立共识性调整模型,该模型采用反馈机制,并且尽可能提供给专家较多的信息,以方便专家进行信息修正,达到群体共识;最后,通过具体实例说明了所提出的共识性方法的可行性和实用性.     

考虑群体信任的网络安全态势多维偏好评级方法

现有网络安全态势评级方法难以同时兼顾专家间信任信息、偏好信息以及客观异质信息等多源信息,为此,建立基于群体信任的网络安全态势多维偏好评级模型.在描述网络安全态势多维偏好评级问题的基础上,提出社会网络中信任信息不确定程度度量方法,进一步建立一种考虑单链信任信息不确定性和内部差异性、多链间信任非补偿性和内部差异性的不完整信任网络构造模型,据此确定专家权重;定义基于级别特征值的客观排序、专家偏好的群体一致性程度和群体非一致性程度,进一步建立包含专家信任信息、偏好信息和评级信息等在内的网络安全态势评级多维偏好分段变权综合优化模型,获得基于二元语义的网络安全态势等级和等级区分度.所提出方法为构建和检验网络安全态势评级提供理论依据,有助于研发先进的网络安全态势评级系统,提高网络安全防护能力.     

基于DEMATEL和信任网络的毕达哥拉斯模糊多属性应急决策方法

针对突发公共卫生事件应急决策过程中属性权重和决策者权重均未知的问题,提出一种基于DEMATEL和信任网络的毕达哥拉斯模糊还原性BM算子决策方法,该方法兼顾主客观关系保障了应急评估体系的完整性。首先考虑到关键因素对突发公共卫生事件应急决策的重要影响,通过DEMATEL方法识别并确定属性权重,以应急决策背景复杂多样为切入点,利用信任传播路径完善信任关系并构建初始信任度和偏好相似度相融合的混合信任网络得到决策者权重;其次,考虑到应急评估的属性间具有强关联性,在改进的相似度测度和得分函数的基础上引入毕达哥拉斯模糊还原性BM算子,计算方案综合评估值及排序;最后,将该方法运用到常态化疫情防控时期医院对于零星散发病例的应急管理评估中,验证了该方法的可靠性和合理有效性。     

DTM-SIR模型上信任机制对信息传播的影响

信任是连接人与人之间复杂社交关系的桥梁。通过网络分层机制将个体信任水平动态转变策略与社交网络上信息动态传播过程分层研究,从两个网络层节点独立传播和交互影响角度来研究个体信任水平博弈对信息传播过程的影响,改进了单层网路研究的局限性。两层网络节点符合层内独立传播、层间相互影响的规则。信任层节点的传播采用博弈演化动力学方法来处理,信息传播层节点传播则符合流行病动力学SIR传播模型,但其受信任层信任因子的影响。并且文中给出了DTM-SIR模型各层元素动态变化的具体分析过程,通过实例仿真表明信任层的引入对信息传播扩大化影响具有积极的意义。