2026/03/06 by Bernd von Mallinckrodt · 1 voice
paper · doi:10.5281/zenodo.18886829
openalex publication_date 2026/03/06 · openalex created_date 2026/03/07 · openalex updated_date 2026/07/01
This paper introduces the Mallinckrodt Axiom, a conceptual inversion of a dominant assumption in systems theory, governance, and management science. While conventional models typically associate systemic stability with increasing levels of order, optimization, and control, this work proposes the opposite mechanism: complex systems frequently collapse not because of chaos or insufficient order, but because of structural over-order. When optimization compresses internal variance and suppresses environmental feedback, systems may appear increasingly efficient while simultaneously becoming structurally fragile. This dynamic is conceptualized within the Meta-Model of Singularization, which describes the progressive loss of adaptive capacity in complex adaptive systems. To operationalize this principle, the paper introduces the Compression–Resonance–Tension Index (CRTI) as a conceptual diagnostic indicator of systemic fragility. The framework is illustrated through cross-disciplinary analogies drawn from materials science (brittle fracture), ecology (monocultures), cybernetics, and control theory (over-optimized feedback systems). By reframing systemic collapse as a consequence of excessive structural order rather than disorder, the Mallinckrodt Axiom provides a new analytical perspective for understanding fragility, resilience, and adaptive stability in complex socio-technical, ecological, and organizational systems. Keywords (20 für Scholar & Semantic Scholar) Complex Systems Systems Theory Complex Adaptive Systems Structural Fragility Systemic Collapse Resilience Theory Cybernetics Nonlinear Dynamics Structural Compression Environmental Resonance Mallinckrodt Axiom Compression Resonance Tension Index CRTI Meta Model of Singularization System Optimization Feedback Dynamics Organizational Collapse Ecological Stability Socio-technical Systems Adaptive Systems