C_ Unified Governability Framework for Human and Artificial Collective Systems (ECA)

This document presents a unified formal framework for the analysis of governability in complex collective systems, with a specific focus on endogenously-constrained artificial and human systems. The approach is grounded in the principles of Crowd-Based Dynamics (CBD) and extends them toward a structurally closed model of governability consistent with the Endogenously-Constrained AI (ECA) paradigm. Within this framework, governability is not imposed through external control but emerges from internal structural constraints that regulate system evolution. Collective systems are modeled as dynamically evolving structures driven by accumulation, memory effects, and interaction dynamics, where stability and instability arise from endogenous processes rather than external intervention. The model formalizes the conditions under which a system remains governable, transitions between regimes, or collapses due to internal saturation, informational tipping, and structural limitations. It introduces a non-predictive, structurally deterministic approach based on attractor dynamics, memory weighting, and constrained accessibility of system states. Key mechanisms include: endogenous constraint of system trajectories, memory-driven asymmetry and attractor dominance, internal regulation of accessible configurations, structural limits to reversibility and control. The framework defines governability as a bounded regime emerging from the system’s own internal organization, where intervention capacity is intrinsically limited by structural saturation and informational constraints. This approach is particularly relevant for artificial intelligence systems, where stability, auditability, and control must be ensured without reliance on external corrective mechanisms. It provides a formal basis for designing systems that remain stable and governable through their internal architecture. The framework is also applicable to financial systems and broader complex adaptive environments, offering a unified perspective on systemic risk, regime transitions, and endogenous collapse. The contribution of this work lies in establishing a mathematically grounded, non-speculative model of governability in which control is not externally enforced but structurally embedded within the system itself.