Information-Dynamic Theory of Stable Actualization Regimes

Abstract This work presents an information-dynamic theory of stable actualization regimes, introducing a non-probabilistic structural framework for describing realizable states of physical and conceptual systems. The theory is based on the notion of admissible variability and formalizes the requirement that stable actualization is possible only when more than one admissible configuration remains available. A structural configuration is characterized by a domain of admissible variations, together with a weighting function that quantifies the structural admissibility of each variation under a given configuration. A global admissibility measure is defined as the aggregate structural weight over the entire admissible domain and serves as an indicator of whether a configuration can be realized as a stable regime. The logarithmic transform of this measure defines a structural potential that increases as the admissible domain contracts. A central result of the theory is the prohibition of zero admissibility: configurations in which the admissible domain collapses to a single allowed variation are shown to be unrealizable as stable actualizations. This prohibition is not interpreted as a limitation of measurement or knowledge, but as a structural constraint on realizability itself. The proposed framework does not modify existing physical laws and does not introduce new forces, constants, or dynamical equations. Instead, it functions as a meta-theoretical layer that constrains the realizability and stability of regimes compatible with established physical theories. Within this framework, quantities such as mass, inertia, temporal irreversibility, and the absence of exact singular states admit consistent structural interpretations while remaining fully compatible with relativistic symmetry and conservation principles. The work is presented as a theoretical preprint intended to establish conceptual and mathematical foundations rather than to propose new empirical laws. Potential applications include stability analysis, system diagnostics, and interpretative frameworks for physical theories, without claims of experimental violation or modification of known physics.