Sensitivity Analysis on Policy‐Augmented Graphical Hybrid Models With Shapley Value Estimation
针对生物制造中高复杂性和高不确定性的挑战，提出了一种基于沙普利值的敏感性分析框架，用于评估政策增强知识图形混合模型中各输入的重要性，并采用线性高斯近似和方差缩减技术提高计算效率。
ABSTRACT Driven by the critical challenges in biomanufacturing, including high complexity and high uncertainty, we propose a comprehensive and computationally efficient sensitivity analysis framework for general nonlinear policy‐augmented knowledge graphical (pKG) hybrid models that characterize the risk‐ and science‐based understandings of underlying stochastic decision process mechanisms. The criticality of each input (i.e., random factors, policy parameters, and model parameters) is measured by applying Shapley value (SV) sensitivity analysis to pKG (called SV‐pKG), accounting for process causal interdependences. To quickly assess the SV for heavily instrumented bioprocesses, we approximate their dynamics with linear Gaussian pKG models and improve the SV estimation efficiency by utilizing the linear Gaussian properties. In addition, we propose an effective permutation sampling method with TFWW transformation and variance reduction techniques, namely the quasi‐Monte Carlo and antithetic sampling methods, to further improve the sampling efficiency and estimation accuracy of SV for both general nonlinear and linear Gaussian pKG models. Our proposed framework can benefit efficient interpretation and support stable optimal process control in biomanufacturing.