A hesitation-feedback recommendation approach and its application in large-scale group emergency decision making

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 sce-narios. 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 mech-anism 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 ex-pected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards.

Automated summary

This paper addresses the challenges of large-scale group consensus and decision-making under risk and emergency conditions in Group Decision Making (GDM). A problem-solving approach is proposed, which includes a feasible mechanism of group consensus strategies and a subgroup identification method. The approach is illustrated through a real case study and shows effective and efficient decision-making in complex large-scale GDM under risk and emergency.