Chance-constrained Robust Large-scale Group Consensus for Probabilistic Linguistic Empathy Network
Empathy relationship,as a social relationship,objectively exists in some realistic group decision-making problems,so group size becomes an important factor affecting group consensus.It increases the uncer-tainty of group decision-making problems and leads to a costly and time-consuming consensus reaching process.Therefore,this paper aims to investigate a large-scale group consensus decision-making method in a probabilistic linguistic empathy network environment.A robust cost consensus model is then developed using the chance-constrained robust optimization method during the feedback adjustment process to account for the effects caused by the uncertainty of the unit adjustment cost.Considering that it is difficult for people to clearly evaluate their empathy relationship with others in real life,it is more expressed in natural language.We first define a probabilistic linguistic empathy function to evaluate the empathy relationship among decision makers,and thus build a probabilistic linguistic empathy network.Based on such a network,the decision makers'preferences can be decomposed into their intrinsic prefer-ence,representing their true opinion,and their empathetic preference for other decision makers in the network.Secondly,a fuzzy C-means clustering method based on preference relationship is used to divide decision makers with high similarity into several subgroups.Since the empathy relationship among decision makers should be used as a reliable indicator to assign weights to each cluster,this paper considers the size,cohesion,and overall empathy degree of each cluster to determine the importance of clusters.In addition,since decision makers in the same cluster have highly similar preferences,the degree of attitudinal empathy based on the empathy relationship is utilized to determine the weight of decision makers within a class.When the group consensus degree cannot reach a predefined consensus threshold,to improve the consensus quality,opinion adjustment becomes a natural phenomenon.At this point,an efficient feedback adjustment process needs to be implemented.This paper designs an optimization-based feedback mechanism incorporating a minimum cost consensus model,which is guided by empathetic relationship.Affected by factors such as social experience and educational background,the unit adjustment cost of decision makers from different organizations may present uncertainty.Robust optimization,as a powerful tool for dealing with uncertainty,is often combined into minimum cost consensus models to deal with uncertain unit adjustment costs.However,most of these robust consensus models pre-set uncertainty level parameters to control the fluctuation range of uncertain parameters,resulting in results that may be too conservative.In order to make full use of the stochastic nature of fluctuating data when establishing the uncertainty immune solution,this paper uses the chance-constrained robust optimiza-tion method to develop a robust cost consensus model.Then the appropriate confidence level can be determined to deal with uncertainty according to the actual situation,so as to guarantee the stability of the model while reducing its conservatism.Finally,since decision makers tend to accept the preferences of decision makers with whom they have an empathetic relationship,they are important reference information for generating preference adjustment recommen-dations.This paper combines empathetic evolutionary preferences with optimal adjustment preferences to generate adjustment recommendations for decision makers.This paper abstracts the government(the moderator)and 20 emergency experts(decision makers)from the realistic group decision-making problems in the formulation of the epidemic prevention and control plan to conduct consultations.Then a concrete implementation of the proposed framework is demonstrated,in which decision makers'preferences and empathy relationships are randomly generated by computer.Through computer simulation experiments and comparison with other consensus models,the practicability and effectiveness of the chance-constrained robust optimization method in large-scale group consensus decision-making problems are verified.The research results show that considering that the empathy relationship can promote the group consen-sus,the chance-constrained robust consensus model can better balance economy and conservatism.
group decision makingchance-constrained robust optimizationminimum cost consensusprobabi-listic linguistic empathy networkfeedback mechanism
国家科技期刊平台
NSTL
万方数据
