Evaluating Information Risk Propagation in Complex Public Opinion Environments Based on the Improved Grey Relational Analysis—Decision Making Trial and Evaluation Laboratory Method - Zhanyang Luo; Yumei Xue; Jiafu Su (Systems, 2023)
The propagation of information risk in complex public opinion environments not only leads to severe direct reputational losses for companies but also results in significant economic damages. Therefore, during the nascent stage of information risk, identifying potential propagation pathways, determining key dissemination channels, and taking timely measures become crucial. To address this issue, this paper proposes a multi-criteria decision-making method for evaluating information risk propagation in complex public opinion environments. In this method, this paper utilizes probabilistic hesitant fuzzy sets to express the evaluation information, and provide several distance and similarity measurement methods for probabilistic hesitant fuzzy elements. To ensure the rationality of the evaluation indicator weights, this study first applies these distance measurement methods to improve the Grey Relational Analysis—Decision Making Trial and Evaluation Laboratory (GRA-DEMATEL) method for determining the objective weights of evaluation indicators. Next, this paper uses the Delphi method to establish the subjective weights of each evaluation indicator. Finally, by employing a weight synthesis operator, this paper combines the subjective and objective weights to obtain the final indicator weights. Additionally, this paper utilizes the similarity measurement methods for probabilistic hesitant fuzzy elements to improve the combined compromise solution (CoCoSo) method in evaluating and ranking potential information risk propagation pathways. Furthermore, this paper incorporates the “Probability Splitting Algorithm” to handle probabilistic hesitant fuzzy elements, enabling their application in these methodologies. Finally, based on a case study of information risk propagation in the catering industry, we conducted a sensitivity analysis and effectiveness verification of the proposed approach. The results demonstrate the effectiveness of the method and its ability to address real-world issues.