A General Dynamic Theory of Competitive Signal Diversion

Abstract

Competitive interaction among autonomous sub-agents in a complex adaptive system generates a structural dilemma: unconstrained rivalry accelerates innovation while simultaneously producing destructive entropy, whereas forced equilibrium removes destructive friction at the price of evolutionary stagnation. The present work develops a formal, mathematically explicit treatment of the Law of Systemic Signal Diversion, previously stated in qualitative and cybernetic terms, by embedding it inside three complementary and well-established mathematical frameworks: (i) a strategic (game-theoretic) model of agent-level channel allocation under an institutionally imposed noise-impedance function; (ii) a population-level replicator-dynamics model describing the evolutionary stability of signal-routing versus noise-routing strategies; and (iii) an information-theoretic and reliability-theoretic model linking accumulated systemic noise to a hazard-rate survival function and a capital-accumulation-type adaptation capacity. The resulting apparatus yields testable, falsifiable predictions, is applied through explicit structural (non-literal) analogies to ecological competition, oligopolistic and antitrust economics, multi-agent reinforcement learning, and macro-historical geopolitics, and is illustrated with four extended case studies, including the 1951 European Coal and Steel Community as a documented historical instance of institutionalized signal diversion. The paper closes with an explicit statement of the theory's limitations, its falsifiability criteria, its distinguishing contribution relative to existing cybernetic and game-theoretic literature, and a concrete computational simulation architecture. Throughout, claims are calibrated to the epistemic status actually supportable by the mathematics presented: the formal apparatus establishes internal logical and mathematical consistency of the Signal Diversion framework and generates testable hypotheses; it does not, and cannot, constitute empirical proof in the absence of calibration against measured data, and this distinction is treated as a first-class methodological requirement rather than a rhetorical caveat.