A Formalized Dynamic Model of Self-Regulating Systems Under Conditions of Uncertainty / A Theoretical and Methodological Study with a Formalized Dynamic Model and Applied Interpretation

This work is important because it proposes a new way of talking about the behavior of complex systems under conditions of uncertainty—without reducing everything to psychology, biology, or physics taken separately. Instead of describing states, symptoms, or external loads, the author shows how systems, in principle, deal with situations for which they have no ready-made solutions, and why some systems maintain stability while others become stuck, break down, or disorganize. The key value of the work lies in shifting the focus from describing what is happening to analyzing why what is happening produces a reaction at all, what types of reactions are fundamentally possible, why these reactions differ across systems, and how they unfold over time. This makes it possible to describe, in a single language, processes that previously had to be split across disciplines: the psyche in isolation, physiology in isolation, and technical or artificial systems in isolation. The proposed architecture offers a shared structural principle without reducing one domain to another. The work is especially relevant to contemporary science because it: addresses the long-standing reduction problem between physics and the psyche; makes it possible to analyze system stability and breakdowns without normative labels; provides a formal but non-reductionist language for describing complex, poorly formalizable processes. The text is published as a preprint and is deliberately released for discussion at an active stage of development. The author explicitly invites critique, testing, and further extension of the model. Key sections for understanding the work are: the “Interpretation” section, which explains the meaning of the model and how to read its formal elements without reducing them to specific disciplines; Appendix 10, which demonstrates how the same principle can be applied to artificial intelligence architectures, extending the work beyond a purely psychological context.