Malignant Patterning Theory is a cross-disciplinary theoretical framework that explains why certain complex systems—particularly political systems—exhibit persistent regression despite repeated shocks, reform attempts, or moments of apparent openness. The theory argues that stability should not always be interpreted as equilibrium or systemic health. In some systems, stability functions as a pathological condition sustained by a closed structural core that absorbs disruption and reproduces the dominant pattern. Change may occur at the surface level, but underlying dynamics remain intact, causing systems to revert to prior configurations. By conceptualizing systems as operating within a constrained phase space structured around structural attractors, the theory explains why shocks often fail to produce transformation. Instead, disturbances are internalized through feedback mechanisms that reinforce regression rather than enable transition. Malignant Patterning Theory is articulated through a set of core theses and analytical tools—including attractor dynamics, phase space modeling, and feedback loop analysis—that allow the framework to be applied beyond political analysis. It offers explanatory and diagnostic value for economics, institutional design, organizational systems, and other complex adaptive structures. The theory provides a foundation for comparative analysis, predictive modeling, and the development of counter-models aimed at disrupting malignant stability and enabling genuine transformation. This paper presents the core articulation of Malignant Patterning Theory.The theory is formally registered on the Open Science Framework (OSF) to establish conceptual priority.A permanent open-access archival version is available via Zenodo.

This work presents the core formulation of Malignant Patterning Theory, a cross-disciplinary theoretical framework explaining persistent regression and failed transformation in complex systems, with particular emphasis on political and institutional systems. The theory conceptualizes regression as a predictable systemic outcome produced by closed structural attractors operating within constrained phase spaces. It shifts analysis from events and actors to system dynamics, feedback loops, and structural closure. This publication represents the archival version of the theory. The formal theoretical registration is hosted on the Open Science Framework (OSF). A working paper version is available via SSRN.