Biopsychosocial Resilience through a Complex Adaptive Systems Lens: A Narrative Review of Nonlinear Modeling Approaches.

Abstract

Human resilience is often considered as static traits using a reductionist approach. More recent work has demonstrated it to be a dynamic and emergent property of complex systems. This narrative review explores human resilience through a self-organizing framework with a specific emphasis on the application of nonlinear modeling approaches. Four classes of approaches are examined: univariate dynamics, bivariate coupling, topological modeling, and network modeling. Univariate dynamics capture the temporal structure and flexibility within a single time series, while bivariate coupling approaches quantify the interaction dynamics and coordination between two time series. Topological modeling identifies bifurcations and attractor dynamics as signals of critical transitions relative to emergence and system stability. Network modeling represents system structure with a focus on connectivity, flexibility, and system integrity. Applying a complex systems framework, this review provides insights into data modeling opportunities for characterizing important features of a system's capacity to bounce back and recover from stress. These characteristics are connected to meta-flexibility, which characterizes a system's adaptive responsiveness to stressors, including post-traumatic growth, and the relation between meta-flexibility and metastability is discussed. Overall, this review provides a foundation of tools for researchers interested in under-standing human resilience through a complex systems framework.