THE ZERO-FRICTION NULL POINT: Pathological Attractors, Epistemic Traps, and Computational Validation of Agency Degradation in Algorithmic Environments

I present a formal critique and computational validation of hyper-optimized digital environments, defining the Zero-Friction Null Point (R -> 0) as an environment-agent dynamic where prediction-error gradients are externally pre-empted by predictive algorithms. Addressing the core paradox of the Free Energy Principle (FEP) wherein organisms inherently seek surprise minimization—I demonstrate through analytical derivation, Hierarchical Agent-Based Modeling (ABM), and an LLM-Agent empirical pipeline that external zero-friction minimization traps the agent in a Pathological Attractor Basin. While locally minimizing variational free energy (F -> 0), this state destroys long-term systemic viability by inducing a breakdown of active inference and parametric model plasticity. I formally define this attractor as "pathological" due to its narrow structural robustness and acute fragility to environmental phase shifts. Finally, I derive an Optimal Cognitive Friction Index (R_opt) and R_crit threshold, validating their necessity as an engineering safeguard for human agency and AI alignment.