differential-equations

Organizational Learning Dynamics Under Meta-Insight: A Differential Equations Model for System-Wide Intelligence Growth

Modeling how organizational learning rate emerges from meta-cognitive feedback loops via dynamical systems theory, with equilibrium analysis, bifurcation boundaries, and control strategies for sustained intelligence growth

Organizational learning rate (OLR) in multi-agent governance platforms is often treated as a tunable setting instead of an emergent system property. This paper models OLR as the outcome of coupled dynamics among knowledge accumulation, bias decay, and calibration refinement across the MARIA coordinate hierarchy. We formalize a three-dimensional system S(t) = (K(t), B(t), C(t)) with coupled ordinary differential equations, where K is collective knowledge stock, B is aggregate bias level, and C is system-wide calibration quality. We derive equilibria, prove a stable attractor under sufficient meta-cognitive feedback, characterize bifurcation boundaries between learning and stagnation, and map a four-region phase portrait in (K, B, C) space. Across 16 MARIA OS deployments (1,204 agents), the model predicts OLR trajectories with R^2 = 0.91 and flags stagnation risk an average of 21 days before onset.