Incomplete Information Complement Method Based on the Hesitation of Experts in Large-group Emergency Decision Making

In this study, we propose a novelemergency decision-making method for large groups with incomplete preferential in formationto solve the problem associated with the availability of incomplete information fora large group of experts during emergency decision making. First, we use the term frequency-inverse document frequency (TF-IDF) algorithm for extracting keywords from microblog texts related to anemergency to obtain attributes related to the event and theirweights. Second, we evaluate the hesitation of the experts according to the preferential information provided by the experts to obtain the expert weights. Third, we propose a new complement model to obtain complete preferential information matricesby measuring the correlation between the attributes and the proximity between alternatives based on the incomplete preferential information matrices. Fourth, we aggregate information and select alternatives by combining the attributes' weights and experts' weights. Finally, the feasibility and effectiveness of the proposed method are verified based on theflood disaster events in Jiangxi.