Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooper...Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.展开更多
Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose ch...Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical applications.To improve the conventional FMEA,many modified FMEA models have been suggested.However,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes.In this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure modes.Firstly,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA experts.Then,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus.Next,failure modes are categorized into several risk clusters using a density peak clustering algorithm.Finally,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems.The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.展开更多
In recent years,large-scale group decision-making(LSGDM)has garnered significant scholarly attention.Given that decision-makers(DMs)may come from various departments and have distinct knowledge backgrounds,they often ...In recent years,large-scale group decision-making(LSGDM)has garnered significant scholarly attention.Given that decision-makers(DMs)may come from various departments and have distinct knowledge backgrounds,they often use heterogeneous information to express their assessments.To address these challenges,this paper proposes a new decision-making model for large groups in a social network environment.Initially,the large group is divided into communities based on the trust relationships among DMs using community detection algorithm.Subsequently,a direct method is used to process DMs’heterogeneous information.Simultaneously,we integrate the weight information of DMs and communities to calculate the preferences of the group using a weighted averaging operator.Additionally,an innovative feedback mechanism is designed,which takes into account the bounded confidence of experts and the influence of community leaders,to enhance the consensus level.Finally,the feasibility and effectiveness of the proposed model are demonstrated through a specific case study.展开更多
Under the bounded rationality assumption,a principal rarely provides an optimal contract to an agent.Learning from others is one way to improve such a contract.This paper studies the efficiency of social network learn...Under the bounded rationality assumption,a principal rarely provides an optimal contract to an agent.Learning from others is one way to improve such a contract.This paper studies the efficiency of social network learning(SNL)in the principal–agent framework.We first introduce the Cobb-Douglas production function into the classic Holmstrom and Milgrom(1987)model with a constant relative risk-averse agent and work out the theoretically optimal contract.Algorithms are then designed to model the SNL process based on profit gaps between contracts in a network of principals.Considering the uncertainty of the agent's labor output,we find that the principals can reach a consensus that tends to result in overcompensation compared to the optimal contract.Then,this study examines how network attributes and model parameters impact learning efficiency and posits several summative hypotheses.The simulation results validate these hypotheses,and we discuss the relevant economic implications of the observed changes in SNL efficiency.展开更多
基金supported by the National Natural Science Foundation of China (Nos.71771156,71971145,72171158).
文摘Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.
基金supported by the Fundamental Research Funds for the Central Universities(22120240094)Humanities and Social Science Fund of Ministry of Education China(22YJA630082).
文摘Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical applications.To improve the conventional FMEA,many modified FMEA models have been suggested.However,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes.In this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure modes.Firstly,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA experts.Then,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus.Next,failure modes are categorized into several risk clusters using a density peak clustering algorithm.Finally,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems.The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.
基金supported by National Natural Science Foundation of China:[Grant Number 72201066].
文摘In recent years,large-scale group decision-making(LSGDM)has garnered significant scholarly attention.Given that decision-makers(DMs)may come from various departments and have distinct knowledge backgrounds,they often use heterogeneous information to express their assessments.To address these challenges,this paper proposes a new decision-making model for large groups in a social network environment.Initially,the large group is divided into communities based on the trust relationships among DMs using community detection algorithm.Subsequently,a direct method is used to process DMs’heterogeneous information.Simultaneously,we integrate the weight information of DMs and communities to calculate the preferences of the group using a weighted averaging operator.Additionally,an innovative feedback mechanism is designed,which takes into account the bounded confidence of experts and the influence of community leaders,to enhance the consensus level.Finally,the feasibility and effectiveness of the proposed model are demonstrated through a specific case study.
基金the support of the National Natural Science Foundation of China(Grant number:72371202)the Fundamental Research Funds for the Central Universities(Grant number:JBK2207051).
文摘Under the bounded rationality assumption,a principal rarely provides an optimal contract to an agent.Learning from others is one way to improve such a contract.This paper studies the efficiency of social network learning(SNL)in the principal–agent framework.We first introduce the Cobb-Douglas production function into the classic Holmstrom and Milgrom(1987)model with a constant relative risk-averse agent and work out the theoretically optimal contract.Algorithms are then designed to model the SNL process based on profit gaps between contracts in a network of principals.Considering the uncertainty of the agent's labor output,we find that the principals can reach a consensus that tends to result in overcompensation compared to the optimal contract.Then,this study examines how network attributes and model parameters impact learning efficiency and posits several summative hypotheses.The simulation results validate these hypotheses,and we discuss the relevant economic implications of the observed changes in SNL efficiency.
