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
For omnivores to determine whether an unfamiliar item is an appropriate food, they could rely on personal information from sampling it themselves or rely on less risky observation of whether other individuals eat the ...For omnivores to determine whether an unfamiliar item is an appropriate food, they could rely on personal information from sampling it themselves or rely on less risky observation of whether other individuals eat the item. Availability of information about food from social companions in group-living species is one of the benefits of group life. Adults of solitary-living species, however, seem typically less likely to rely on social information about food choice. If an individual faced a nutritional deficit, it would seem to increase the value of public information. This study addresses whether dietary restriction from certain nutrients (sodium, potassium, protein, carbohydrates) affects reliance on information about food from conspecifics. Without nutrient restriction, group-living Norway rats (Rattus norvegicus) preferred the diet that they smelled on the breath of a conspecific demonstrator, but solitary-living Syrian hamsters (Mesocricetus auratus) avoided it. Protein restriction yielded similar results as measured one hour into a diet choice test. Potassium restriction, however, reversed the pattern: rats avoided the demonstrator’s diet but hamsters preferred it. Clearly, the valence of social information depended on the nutrient from which individuals were restricted and the species under study. This could be related to the contrasting social organization that members of each species generate. Neither species relied on social information about the availability of a nutrient from which they were restricted if they could taste that nutrient for themselves (sodium, carbohydrates).展开更多
A number of natural experiments have recently found that COVID-19 restrictions imposed in nations worldwide are correlated with short-term reductions—in some cases dramatic reductions—in mobile-source air pollutants...A number of natural experiments have recently found that COVID-19 restrictions imposed in nations worldwide are correlated with short-term reductions—in some cases dramatic reductions—in mobile-source air pollutants. Noticeably absent from these studies are estimates of the social net benefits associated with the changes in human behavior underlying the pandemic-induced effects. Using readily available data provided by the state of Utah and the U.S. Environmental Protection Agency’s Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA), we find that daily social net benefit was positive during a pandemic-induced shutdown from March to April, 2020 in Utah’s Wasatch Front region solely when COBRA’s “high” health benefit estimate from combined reductions in PM<sub>2.5</sub> and NO<sub>x</sub> concentrations are weighed against the region’s “low” vehicle-trip cost estimate. All other scenarios correspond with negative net benefit estimates, <i>i.e.</i>, when high and low benefit estimates of reductions solely in PM<sub>2.5</sub> concentrations as well as for combined reductions in PM<sub>2.5</sub> and NO<sub>x</sub> concentrations are weighed against the region’s high vehicle-trip cost estimate. Generally speaking, social net benefits are higher for two of the Wasatch Front’s four counties.展开更多
The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which over...The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.展开更多
Social Network Extraction(SNE)is an emerging research field which focuses on automatic extraction of hidden social networks from a wide variety of information sources.The articles of online encyclopedia contain massiv...Social Network Extraction(SNE)is an emerging research field which focuses on automatic extraction of hidden social networks from a wide variety of information sources.The articles of online encyclopedia contain massive information about persons as well as their interpersonal relationships,from which a people social network can be extracted and used for the research of digital humanities and social computing.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.
文摘For omnivores to determine whether an unfamiliar item is an appropriate food, they could rely on personal information from sampling it themselves or rely on less risky observation of whether other individuals eat the item. Availability of information about food from social companions in group-living species is one of the benefits of group life. Adults of solitary-living species, however, seem typically less likely to rely on social information about food choice. If an individual faced a nutritional deficit, it would seem to increase the value of public information. This study addresses whether dietary restriction from certain nutrients (sodium, potassium, protein, carbohydrates) affects reliance on information about food from conspecifics. Without nutrient restriction, group-living Norway rats (Rattus norvegicus) preferred the diet that they smelled on the breath of a conspecific demonstrator, but solitary-living Syrian hamsters (Mesocricetus auratus) avoided it. Protein restriction yielded similar results as measured one hour into a diet choice test. Potassium restriction, however, reversed the pattern: rats avoided the demonstrator’s diet but hamsters preferred it. Clearly, the valence of social information depended on the nutrient from which individuals were restricted and the species under study. This could be related to the contrasting social organization that members of each species generate. Neither species relied on social information about the availability of a nutrient from which they were restricted if they could taste that nutrient for themselves (sodium, carbohydrates).
文摘A number of natural experiments have recently found that COVID-19 restrictions imposed in nations worldwide are correlated with short-term reductions—in some cases dramatic reductions—in mobile-source air pollutants. Noticeably absent from these studies are estimates of the social net benefits associated with the changes in human behavior underlying the pandemic-induced effects. Using readily available data provided by the state of Utah and the U.S. Environmental Protection Agency’s Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA), we find that daily social net benefit was positive during a pandemic-induced shutdown from March to April, 2020 in Utah’s Wasatch Front region solely when COBRA’s “high” health benefit estimate from combined reductions in PM<sub>2.5</sub> and NO<sub>x</sub> concentrations are weighed against the region’s “low” vehicle-trip cost estimate. All other scenarios correspond with negative net benefit estimates, <i>i.e.</i>, when high and low benefit estimates of reductions solely in PM<sub>2.5</sub> concentrations as well as for combined reductions in PM<sub>2.5</sub> and NO<sub>x</sub> concentrations are weighed against the region’s high vehicle-trip cost estimate. Generally speaking, social net benefits are higher for two of the Wasatch Front’s four counties.
文摘The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.
文摘Social Network Extraction(SNE)is an emerging research field which focuses on automatic extraction of hidden social networks from a wide variety of information sources.The articles of online encyclopedia contain massive information about persons as well as their interpersonal relationships,from which a people social network can be extracted and used for the research of digital humanities and social computing.
