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Chinese Journal of Management Science ›› 2021, Vol. 29 ›› Issue (10): 178-190.doi: 10.16381/j.cnki.issn1003-207x.2019.1061

• Articles • Previous Articles    

Social Network Group Decision-Making Model Based on Trust Relationship and Information Measures with Probabilistic Linguistic Information

JIN Feifei1,2, LIU Jinpei1, CHEN Huayou3, DU Pengcheng1   

  1. 1. School of Business, Anhui University, Hefei 230601, China;2. Anhui University Center for Applied Mathematics, Anhui University, Hefei 230601, China;3. School of Mathematical Sciences, Anhui University, Hefei 230601, China
  • Received:2019-07-20 Revised:2019-10-30 Online:2021-10-20 Published:2021-10-21

Abstract: The probabilistic linguistic term sets are useful tool to address situations, in which the decision makers (DMs) are more comfortable providing their evaluation information linguistically rather than numerical values, and it is more reasonable and convenient to present the linguistic values by including an occurrence probability. Designing the calculation model of expert weights and attribute weights are two significant and challenging research issues in recent years. A probabilistic linguistic social network group decision-making model is investigated based on trust relationship and information measures to solve the group decision-making problem, in which evaluation information is a probabilistic linguistic trust function. Firstly, a probabilistic linguistic decision space based on trust relationship is constructed, and the trust propagation model among experts is explored to identify the incomplete trust relationship in the probabilistic linguistic trust relationship matrix. The weights of experts are calculated by using the trust relationship among experts. Then, two axiomatic definitions of information measures for probabilistic linguistic trust functions are presented, including the probabilistic linguistic trust function entropy and the probabilistic linguistic trust function similarity measure, to measure the uncertainty degree of probabilistic linguistic trust functions and the similarity degree between probabilistic linguistic trust functions. Subsequently, the calculating methods of probabilistic linguistic trust function’s entropy and similarity measure are designed by using triangular function. Finally, based on trust relationship and information measures, a probabilistic linguistic social network group decision-making model is constructed to derive the reasonable and reliable decision-making results. The proposed social network group decision-making model is applied to the selection of electric vehicle suppliers, and the comparison with existing approach is performed to validate the rationality and effectiveness of the proposed model.

Key words: group decision-making, social network analysis, probabilistic linguistic trust function, trust relationship, information measures

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