R.B. Hayes
North Carolina State University,
United States
Keywords: resilience, agility, systems engineering, risk assessment, decision support
Summary:
Resilience and agility have emerged as critical attributes for managing large-scale, interconnected systems during times of disruption. This work presents a novel mathematical framework for assessing the agility of complex Systems of Systems (SoS) by extending traditional resilience metrics. The model introduces a structured methodology to quantify outcome risk and agility using pliable (controllable) and uncontrollable state variables, connected through a time-dependent sensitivity matrix. The proposed framework can be adapted to assess resilience in diverse sectors such as energy, manufacturing, public health, or infrastructure by modeling how perturbations in system drivers affect operational stability and recovery. The agility metric—defined as the second derivative of system state response—enables decision-makers to identify which parameters most influence rapid recovery and system robustness. Additionally, the approach supports customization to optimize for equity, sustainability, or cost-effectiveness by introducing weighted priorities in state vectors. This contribution provides a scalable, modular foundation for predictive modeling, risk-informed investment, and policy guidance in future-proofing critical societal functions. The method can be enhanced with expert-informed parameters or integrated with machine learning for big data applications.