Failures in complex systems often emerge through gradual degradation and the propagation of stress across interacting components rather than through isolated shocks. Democratic systems exhibit similar dynamics, where weakening institutions can trigger cascading deterioration in related institutional structures. Traditional reliability and survival models typically estimate failure risk based on the current system state but do not explicitly capture how degradation propagates through institutional networks over time. This paper introduces a trajectory-aware reliability modeling framework based on Dynamic Causal Neural Autoregression (DCNAR). The framework first estimates a causal interaction structure among institutional indicators and then models their joint temporal evolution to generate forward trajectories of system states. Failure risk is defined as the probability that predicted trajectories cross predefined degradation thresholds within a fixed horizon. Using longitudinal institutional indicators, we compare DCNAR-based trajectory risk models with discrete-time hazard and Cox proportional hazards models. Results show that trajectory-aware modeling consistently outperforms Cox models and improves risk prediction for several propagation-driven institutional failures. These findings highlight the importance of modeling dynamic system interactions for reliability analysis and early detection of systemic degradation.

翻译：复杂系统的失效往往源于各组件间逐渐退化与应力传播，而非孤立冲击。民主系统展现出类似动力学特征：制度弱化可能引发关联制度结构的级联退化。传统可靠性与生存模型通常基于当前系统状态评估失效风险，但未能明确捕捉退化如何在制度网络中随时间传播。本文提出了一种基于动态因果神经自回归（DCNAR）的轨迹感知可靠性建模框架。该框架首先估计制度指标间的因果交互结构，进而对其联合时间演化进行建模以生成系统状态的前向轨迹。失效风险定义为在固定时间范围内，预测轨迹越过预定退化阈值的概率。利用纵向制度指标，我们将基于DCNAR的轨迹风险模型与离散时间风险模型及Cox比例风险模型进行比较。结果表明，轨迹感知建模持续优于Cox模型，并能改善若干传播驱动型制度失效的风险预测。这些发现凸显了建模动态系统相互作用对于可靠性分析与系统性退化早期检测的重要性。