Fuzzy Quantum Group Decision Making and Its Application in Meteorological Disaster Emergency

Abstract

This article proposes a quantum group decision model that integrates intuitionistic fuzzy sets to represent the uncertainty of meteorological disaster information, addressing both vagueness and probabilistic uncertainty. This makes it particularly suitable for modeling the complex and dynamic decision-making processes during emergency responses. The model employs regret theory for attribute weight determination and constructs a quantum-like Bayesian network (QLBN), where Deng entropy is applied to measure the mutual interference effects among decision-makers. Decision-makers' weights are determined using grey relational analysis and incorporated as the initial layer in the Bayesian network. The conditional probabilities within the QLBN are derived by integrating attribute weights and regret utility functions, and the alternatives are ranked based on their final quantum probabilities. The effectiveness and stability of the model are demonstrated through its application in emergency alternative selection for meteorological disasters, confirmed by sensitivity and comparison analyzes.