Emergent Global Regulation in Maximally Stressed Adaptive Causal Fields
Drew Slawson · Zenodo (CERN European Organization for Nuclear Research) · 2025
This study investigates the emergence of stability and regulation in large adaptive causal systems operating under extreme conditions. The system is modeled as a global adaptive causal field without predefined units, architecture, or task-driven optimization. Instead, organization arises solely through interaction dynamics governed by causal consistency, feedback, entropy flow, and global stability constraints. The system is initialized in a highly stressed regime characterized by extreme dimensionality, dense nonlocal coupling, directional asymmetry, unconstrained plasticity, stochastic noise, and an externally imposed global instability shock. Despite these destabilizing factors, the system consistently self-organizes into a low-dimensional, coherent regime. Following perturbation, the system demonstrates rapid recovery, preserved topological coherence, and increased global stability relative to pre-shock states. Dimensional compression occurs without collapse to trivial dynamics, and persistent interaction loci emerge as stable centers of causal organization. These results demonstrate that stability can arise as an intrinsic global property of adaptive systems, independent of predefined architecture or task optimization. The findings suggest a general mechanism by which large, highly stressed systems may regulate themselves through global constraints rather than local control, with implications for neural dynamics, complex systems theory, and adaptive computation.