Causal Mechanisms of Stability, Phase Transitions, and Collapse in Complex Self-Regulating Systems: Distinguishability and Uncertainty as the Basis of Dynamics

This paper proposes an architectural approach to the analysis of complex self-regulating systems aimed at resolving the gap between the registration of empirical effects and the understanding of causal mechanisms underlying system stability, transitions, and breakdown. The model introduces distinguishability as a fundamental structural condition of system existence and dynamics. System regimes, phase transitions, phase windows, adaptive stability, tension-based fixation, and disorganization are interpreted as different modes of preserving, processing, or losing distinguishability over time. The proposed framework operates at an architectural level, allowing the integration of observable regimes into a unified causal structure without reducing system complexity or relying on discipline-specific parameters. This enables consistent differentiation between adaptive and disorganized dynamics, reversible and irreversible transitions, and stable regimes and phase windows. The model is applicable across physical, biological, cognitive, social, and artificial systems, including machine learning and neural network dynamics. Architectural relations identified in the paper provide a foundation for subsequent formalization within specific scientific domains while preserving causal rigor and conceptual consistency. The work is presented as a preprint and constitutes a conceptual and methodological basis for further theoretical development, empirical testing, and interdisciplinary application.