In flood disaster emergency decision-making, the dynamic changes in expert trust relationships directly affects the efficiency of reaching a decision consensus. This paper constructs a dynamic evolution model of expert trust relationships in flood disaster emergency decision-making from the perspective of preference distance: the initial trust matrix and weights of experts based on four dimensions including cooperation intensity, social relations, background similarity, and subjective initial trust; the cognitive trust is quantified by using the intuitionistic fuzzy Hamming distance, and the trust relationship is dynamically update through the exponential fusion method; the Louvain community discovery algorithm is introduce to achieve dynamic clustering of experts and weight updates of experts in combination with the dynamic changes in trust relationships; and a consensus feedback adjustment mechanism is designed to optimize the preferences of experts with lower consensus. At the same time, the dynamic trust model is verified by combining a flood disaster case. Case validation shows that the model completes consensus iteration in just four rounds, with the maximum increase in cognitive trust due to opinion convergence reaching 0.18 during the evolution process. The model effectively captures changes in trust among experts during decision-making, improving consensus convergence speed while ensuring that the final solution aligns with the comprehensive considerations required in emergency scenarios. This study provides a quantitative tool for large-group decision-making in flood emergencies under high-pressure, information-poor environments; one that balances dynamic trust evolution with efficient consensus building.
Li et al. (Sat,) studied this question.