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组件间渐进式退化与应力传播，而非孤立性冲击。民主系统呈现类似动态特征：制度弱化可能引发相关制度结构的级联式恶化。传统可靠性与生存模型通常基于系统当前状态估计失效风险，但未能明确刻画退化在制度网络中随时间传播的机制。本文提出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，进而建模其联合时序演化以生成系统状态的远期轨迹。失效风险定义为预测轨迹在固定时间窗内跨越预设退化阈值的概率。基于纵向制度指标，我们将DCNAR轨迹风险模型与离散时间风险模型及Cox比例风险模型进行了比较。结果表明，轨迹感知建模方法持续优于Cox模型，并能有效提升对多种传播驱动型制度失效的风险预测能力。这些发现揭示了建模动态系统交互对于可靠性分析与系统性退化早期检测的重要价值。