Socio-Economic-Technical Synthesis: A Novel Triple Bridge Framework for Quantifying the Impact of Organizational Knowledge Gaps on Technical System Failures

The primary challenge in managing systemic risk is the policy-to-physics gap; the inability to quantitatively link high-level organizational decisions to low-level technical system outcomes. This paper addresses this gap by proposing the SD-DoE-ML Triple Bridge that shows how project failures are symptoms of a deeper systemic failure: the inability of traditional methodologies to effectively integrate and manage socio-economic-technical factors and risks. Furthermore, engineering disasters, from the Challenger space shuttle to the Titan submersible, confirm that fragmented information and organizational blind spots are the ultimate cause of failures. Preventing project failures and engineering disasters requires a fundamental paradigm shift in methodology. This paper proposes the SD-DoE-ML Triple Bridge, a novel approach to enable robust design optimization (RDO), improve enterprise communication, and establish systemic resilience. This Triple Bridge fuses system dynamics (SD), design of experiments (DoE), and AI machine learning (ML) to create a high-fidelity predictive digital twin of a coupled management flight simulator (MFS) and model-based systems engineering (MBSE) ecosystem. This expanded ecosystem is uniquely capable of achieving causal discovery by translating non-linear socio-economic-technical factors into quantitative integrated DoE and ML features. These features play into standard project management (PM) and systems engineering (SE) management curves. The proposed framework is validated through non-linear response surface methodology (RSM) that maps the interaction between management-side knowledge investment and system-level probability of failure. Results indicate that strategic gaming serves as a critical policy lever, where earlystage resource allocation yields a significant reduction in systemic risk. By quantifying the diminishing returns of these interventions, the model provides a decision-support system (DSS) for balancing organizational policy with technical resilience requirements. This creates a bidirectional digital thread that provides prescriptive policy levers to ensure systemic robustness and resilience against escalating project stressors