Flow Fusion: Toward a Science of Algorithmic Governability A Structural Model for the Stability of Distributed Decision Systems

Artificial intelligence systems increasingly participate in the production, amplification, and propagation of decisions across public institutions, financial markets, digital platforms, health systems, and critical infrastructures. While existing research has focused primarily on model performance, fairness, explainability, robustness, and safety, comparatively less attention has been devoted to the governability of decision systems at scale. This paper introduces Flow Fusion, a conceptual, falsifiable, and control-oriented structural model for analyzing the stability of distributed decision systems. The model centers on the relationship between decision pressure and effective governance capacity, and proposes a governability condition under which such systems remain observable, reversible, and correctable under real-world conditions. The model is organized around several variables: decision frequency, impact, propagation scale, propagation velocity, observability, override capacity, reversibility, accountability, correction latency, and governance debt. From these variables, the paper derives a preliminary Governability Index and a Governance Buffer, both intended as first-order analytical approximations rather than empirically validated universal metrics. Drawing on systems theory, cybernetics, network science, institutional theory, and algorithmic governance research, the paper argues that the defining challenge of the algorithmic age is not only the development of increasingly capable decision systems, but ensuring that such systems remain governable once their decisions propagate across complex socio-technical environments. Flow Fusion is presented not as a validated universal law, but as a bounded structural model and research program designed to support future empirical testing, simulation, classification, and institutional analysis of distributed decision systems.