The Method And Application Of Large-scale Group Decision Making Based On Probabilistic Linguistic Information In Social Network

Decision-making is a highly interdisciplinary research field.With the rapid development of information technology and social media,decision-making problems have become increasingly complex,which may involve more and more decision-makers with different social backgrounds such as government,business and academia,and their social networks may affect the result of decision-making.Traditional group decisionmaking theory is no longer sufficient to solve such problems.Therefore,the research on large-scale group decision making under the background of complex social networks has attracted more and more attention.This paper aims to propose a reasonable and scientific method for large-scale group decision making.It starts with how to identify and deal with the uncertainty of decision makers’ evaluation information,how to solve the complexity of large-scale group decision making under the background of complex social networks,and how to improve the consensus degree and efficiency of decision makers.This paper studies the method and application of large-scale group decision making based on probabilistic language information in complex social network environment.The specific research work and results of this paper are as follows:(1)A decision method based on probabilistic linguistic term set in the context of social networks is proposed to solve the complexity and uncertainty problems in largescale group decision making.Firstly,a subgroup partitioning algorithm considering both node similarity and opinion similarity is proposed.The algorithm divides decision makers into several subgroups and identifies the leaders and followers in each subgroup,reducing the dimension of decision makers and providing a basis for reaching consensus.Then,inspired by the artificial bee colony algorithm,a new consensus reaching process considering following probability is established.Based on the following probability,we calculate the degree of consensus within and between subgroups,and construct the corresponding adjustment mechanism to reach consensus.Next,we create a new selection process based on the artificial bee colony algorithm,which not only evaluates the quality of the alternatives,but also takes into account the probability that decision makers choose them.(2)For trust relationships in social networks,a large-scale group decision-making method with multi-strategy persuasion feedback mechanism is constructed.Existing studies are limited in terms of the establishment,sources and transmission of trust relationships.Therefore,this paper integrates three sources of trust,namely direct trust relationships,transmission trust relationships and mutual evaluation trust relationships containing probabilistic linguistic information,and considers the influence of affective trust and cognitive trust to progressively construct a complete comprehensive trust scoring matrix.Information entropy indicates the uncertainty and randomness of evaluation information,which plays an important role in measuring the weight of information.This paper establishes the calculation method of decision maker weight and attribute weight based on information entropy.Firstly,the fused information entropy is calculated using the combined trust score and decision maker’s opinion to obtain objective decision maker weights.Secondly,the attribute weights are obtained by defining probabilistic linguistic information entropy,which avoids the subjectivity of direct decision.In the consensus reaching process,the feedback of decision makers’ opinions is a key stage that contributes to the rapid consensus reaching of large-scale groups.But in previous consensus models,there are few methods to adjust according to different evaluation states of decision makers.Therefore,this paper proposes a multi-strategy persuasion feedback model,which classifies persuasion strategies into four categories:encourage,appeal,punish and ignore,based on the deviation of individual and collective opinions of decision makers.Decision makers will adjust their opinions according to the four different persuasion strategies.The method ensures the level of consensus and efficiency of large-scale decision makers by adjusting feedback in a targeted manner.(3)To explore the application of the above two large-scale group decisionmaking methods to real-life applications.The two methods proposed in this paper are applied to carbon capture,utilization and storage project assessment and urban resilience evaluation problems respectively.Based on the cases,this paper conducts a comparative analysis and sensitivity analysis with existing decision-making methods to verify the practicality and superiority of the decision model proposed in this paper.In summary,the large-scale group decision-making method based on probabilistic linguistic information in the context of the social networks established in this paper enriches the theory of large-scale group decision-making to a certain extent and provides tools to support the solution of decision-making problems in the context of complex social networks in real life.