用图神经网络模拟空袭策略如何影响战略延迟，提升决策透明度。
Strategic Counterfactual Modeling of Deep-Target Airstrike Systems via Intervention-Aware Spatio-Causal Graph Networks
- 构建干预感知的时空图网络，打通战术到战略的因果链。
- 误差降低12.8%，前5%预测准确率提升18.4%，因果路径更稳定。
- 适合军方战略推演、核威慑模拟与外交窗口评估等高阶决策场景。
现有战略级仿真缺乏战术打击行为与战略延迟之间的结构化因果建模，尤其难以捕捉‘韧性—节点压制—谈判窗口’链条中的中间变量。本文提出干预感知时空图神经网络（IA-STGNN），通过图注意力机制、反事实模拟单元与空间干预节点重构，实现打击配置与协同策略的动态模拟。训练数据基于符合NIST SP 800-160标准的多物理场仿真平台（GEANT4 + COMSOL）生成，保障结构可追溯性与政策层面有效性。实验表明，相比基线模型（ST-GNN、GCN-LSTM、XGBoost），IA-STGNN在平均绝对误差（MAE）上降低12.8%，前5%准确率提升18.4%，同时增强因果路径一致性与干预稳定性。该模型支持战略延迟的可解释预测，可用于核威慑模拟、外交窗口评估与多策略优化，为高层政策建模提供结构化、透明化的智能决策支持。
原文摘要 · Abstract (English)
This study addresses the lack of structured causal modeling between tactical strike behavior and strategic delay in current strategic-level simulations, particularly the structural bottlenecks in capturing intermediate variables within the "resilience - nodal suppression - negotiation window" chain. We propose the Intervention-Aware Spatio-Temporal Graph Neural Network (IA-STGNN), a novel framework that closes the causal loop from tactical input to strategic delay output. The model integrates graph attention mechanisms, counterfactual simulation units, and spatial intervention node reconstruction to enable dynamic simulations of strike configurations and synchronization strategies. Training data are generated from a multi-physics simulation platform (GEANT4 + COMSOL) under NIST SP 800-160 standards, ensuring structural traceability and policy-level validation. Experimental results demonstrate that IA-STGNN significantly outperforms baseline models (ST-GNN, GCN-LSTM, XGBoost), achieving a 12.8 percent reduction in MAE and 18.4 percent increase in Top-5 percent accuracy, while improving causal path consistency and intervention stability. IA-STGNN enables interpretable prediction of strategic delay and supports applications such as nuclear deterrence simulation, diplomatic window assessment, and multi-strategy optimization, providing a structured and transparent AI decision-support mechanism for high-level policy modeling.
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