With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that consid...With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.展开更多
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 am...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.展开更多
Background: The aim of this study is to gain a better understanding of the true importance of trust in clinical practice by looking at how it is formed, how it affects clinical practice, and how to improve it. Methods...Background: The aim of this study is to gain a better understanding of the true importance of trust in clinical practice by looking at how it is formed, how it affects clinical practice, and how to improve it. Methods: Using the PRISMA-ScR checklist, a review of the literature was performed to identify research evaluating the importance of trust in the doctor-patient relationship. After thorough screening and removal of duplicates, 21 articles were used in the literature review. Results: The classifying themes that emerged in the selected articles were What Makes Trust and Effects of Trust. The theme of What Makes Trust garnered two subthemes as well: Impact of Doctor-Patient Relationship on Trust and Impact of Shared Decision-Making on Trust. Further to that, the overarching themes found were slightly more specific. They were Traits of Trust, Mistrust and Barriers to Trust, Positive Effects of Trust and the Effects of a Lack of Trust. We found that the best way to improve trust was to improve communication between the patient and the doctor. Additionally, we found that the biggest barrier to a trusting doctor patient relationship was a stigmatised condition, followed by a perception of a financially-motivated doctor. Finally, we found that a lack of trust can prevent patients from seeking and receiving proper treatment. Conclusions: With a better understanding of how trust is built and the extent of the role it plays in clinical practice, we hope that this growing knowledge can improve the practice of many doctors in the future. It is certain that more research needs to be done in this area, especially focusing on vulnerable and stigmatised populations such as chronic pain patients.展开更多
社交关系网络的复杂性和动态性为观点演化研究带来三大挑战:一是研究者在确定个体的观点交互集合时没有考虑个体的信任阈值,导致观点交互集合的准确性不足;二是现有研究通常忽略了非邻居节点之间的交互对社会群体观点演化的影响;三是现...社交关系网络的复杂性和动态性为观点演化研究带来三大挑战:一是研究者在确定个体的观点交互集合时没有考虑个体的信任阈值,导致观点交互集合的准确性不足;二是现有研究通常忽略了非邻居节点之间的交互对社会群体观点演化的影响;三是现有研究通常基于个体间的观点距离来更新社交网络结构,没有考虑个体间的信任关系对网络结构的影响.为了应对上述挑战,提出一种社交网络中动态信任感知的观点演化模型(Dynamic Trust-Aware Opinion Evolution Model in Social Networks,DTAOE).具体地,首先基于信任传播规则构建出社交群体的信任矩阵;之后,基于引入的信任度阈值和信任矩阵,从邻居节点以及非邻居节点中确定当前个体的信任集合,进而基于信任集合中观点相似的个体更新当前个体的观点;最后,根据个体间的观点距离和信任关系,动态地调整社交网络的拓扑结构.上述演化步骤被重复执行直到群体的观点达到稳定状态.开展了大量的仿真实验,实验结果证明了DTAOE模型的有效性和合理性,并揭示了网络结构和信任关系对观点传播的影响机制.展开更多
基金The work was supported by Humanities and Social Sciences Fund of the Ministry of Education(No.22YJA630119)the National Natural Science Foundation of China(No.71971051)Natural Science Foundation of Hebei Province(No.G2021501004).
文摘With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.
基金the National Natural Science Foundation of China(71871121).
文摘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.
文摘Background: The aim of this study is to gain a better understanding of the true importance of trust in clinical practice by looking at how it is formed, how it affects clinical practice, and how to improve it. Methods: Using the PRISMA-ScR checklist, a review of the literature was performed to identify research evaluating the importance of trust in the doctor-patient relationship. After thorough screening and removal of duplicates, 21 articles were used in the literature review. Results: The classifying themes that emerged in the selected articles were What Makes Trust and Effects of Trust. The theme of What Makes Trust garnered two subthemes as well: Impact of Doctor-Patient Relationship on Trust and Impact of Shared Decision-Making on Trust. Further to that, the overarching themes found were slightly more specific. They were Traits of Trust, Mistrust and Barriers to Trust, Positive Effects of Trust and the Effects of a Lack of Trust. We found that the best way to improve trust was to improve communication between the patient and the doctor. Additionally, we found that the biggest barrier to a trusting doctor patient relationship was a stigmatised condition, followed by a perception of a financially-motivated doctor. Finally, we found that a lack of trust can prevent patients from seeking and receiving proper treatment. Conclusions: With a better understanding of how trust is built and the extent of the role it plays in clinical practice, we hope that this growing knowledge can improve the practice of many doctors in the future. It is certain that more research needs to be done in this area, especially focusing on vulnerable and stigmatised populations such as chronic pain patients.
文摘社交关系网络的复杂性和动态性为观点演化研究带来三大挑战:一是研究者在确定个体的观点交互集合时没有考虑个体的信任阈值,导致观点交互集合的准确性不足;二是现有研究通常忽略了非邻居节点之间的交互对社会群体观点演化的影响;三是现有研究通常基于个体间的观点距离来更新社交网络结构,没有考虑个体间的信任关系对网络结构的影响.为了应对上述挑战,提出一种社交网络中动态信任感知的观点演化模型(Dynamic Trust-Aware Opinion Evolution Model in Social Networks,DTAOE).具体地,首先基于信任传播规则构建出社交群体的信任矩阵;之后,基于引入的信任度阈值和信任矩阵,从邻居节点以及非邻居节点中确定当前个体的信任集合,进而基于信任集合中观点相似的个体更新当前个体的观点;最后,根据个体间的观点距离和信任关系,动态地调整社交网络的拓扑结构.上述演化步骤被重复执行直到群体的观点达到稳定状态.开展了大量的仿真实验,实验结果证明了DTAOE模型的有效性和合理性,并揭示了网络结构和信任关系对观点传播的影响机制.
