Objective Calibration‐Based Quantum Group Decision Model and Its Application in Ecological Restoration

Multi‐attribute group decision‐making (MAGDM) in complex environments often involves intertwined attribute dependencies and opinion interference among experts, which are frequently oversimplified in existing models. This paper introduces a novel quantum group decision model that pioneers the integration of objective expert calibration via a factor analysis‐based common factor extraction method. This core innovation departs from subjective weighting and mitigates the noise inherent in expert evaluations, providing a more reliable prior for the quantum framework. Rigorous validation through stochastic simulation and multi‐scenario analysis demonstrates that the proposed method achieves statistically significant performance improvements and superior robustness when compared to traditional methods. Building on this calibrated foundation, a quantum‐like Bayesian network incorporating phase angles is constructed to quantitatively capture interference effects. This architecture not only enables alternative ranking through quantum probability calculus but also introduces a novel capability for generating dynamic ranking intervals via phase adjustments, offering decision‐makers adaptive strategies in evolving scenarios. A case study on ecological restoration plan selection confirms the model's practical effectiveness, where sensitivity and comparative analyses substantiate its stability and superiority. The study ultimately establishes an integrated framework that not only captures complex decision dynamics more authentically but also provides a systematic, evidence‐based validation of its advancements.