Abstract: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.
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY) |
| MSC classes: | 68-T07 |
| ACM classes: | I.2.4 |
| Cite as: | arXiv:2604.20127 [cs.LG] |
| (or arXiv:2604.20127v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.20127 arXiv-issued DOI via DataCite (pending registration) |
From: Valentina Kuskova [view email]
[v1]
Wed, 22 Apr 2026 02:47:31 UTC (479 KB)
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