A Social Network Group Decision-Making Method for Flood Disaster Chains Considering Evolutionary Trends and Decision-Makers’ Risk Preferences

To address the impact of the dynamic evolution of flood disaster chains and decision-makers’ (DMs’) risk preference heterogeneity on group decision-making, this study proposes a social network group decision-making method that integrates the evolutionary trend of the flood disaster chain with DMs’ risk preferences. First, a Bayesian network is constructed to quantify the disaster chain’s evolution, dynamically adjusting DMs’ evaluation values. Second, DMs’ risk preference types are identified based on the evaluation values, and a bounded confidence (BC) model, incorporating risk preferences, self-confidence and trust networks, is developed to promote consensus formation. Then, the optimal alternative is selected through weighted aggregation and used to update the Bayesian network dynamically during implementation. Finally, the effectiveness and superiority of the proposed method are verified using the flood disaster chain from the “7∙20” extreme rainfall disaster in Zhengzhou, Henan Province, China. The results show that risk-seeking DMs reduce BC values and resist consensus, whereas risk-averse DMs enlarge BC values and accelerate convergence. Moreover, worsening flood disaster chain trends drive DMs to update the optimal alternative. These findings show that the method captures both dynamic disaster evolution and behavioral heterogeneity, providing realistic and adaptive decision support in flood emergency scenarios.