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• Develop a complete problem-solving approach for group decision making. • Orient complex large-scale groups and time-sensitive emergency decision scenarios. • Design a feasible mechanism of group consensus strategies under time pressure. • Propose feedback recommendation strategies with a subgroup identification method. • A real case study of China's fight against the COVID-19 epidemic. Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards. [ABSTRACT FROM AUTHOR]

Abstract: Floods are indeed one of the most serious natural hazards for human societies, especially in China. In this paper, we firstly introduce the interior-outer-set model (IOSM) based on information diffusion theory in detail. Then taking consideration its deficiencies, we represent the diffused-interior-outer-set model (DIOSM) to obtain the possibility–probability distribution (PPD). Based on the PPD, we analyze and calculate the risk results. To illustrate the procedure of the proposed method, we apply DIOSM to describe the flood disaster risk quantitatively in China by using statistical data respectively, such as the time of floods, the number of the deaths as well as the economic losses each year from 1990 to 2009. The outcomes of this research offer new insights and moreover new possibility to carry out an efficient way for various future flood disaster prevention and mitigation. [Copyright &y& Elsevier]