Digital Dark Matter: Field Mechanics, Computational Mass, and the Gravitational Distortion of Human Agency in Algorithmic Ecosystems

Modern digital environments present a profound physical paradox: while interfaces appear frictionless and weightless to the end user, they exert an overwhelming, unobserved operational weight that dictates human behavior. In this work, I introduce the formal framework of Digital Dark Matter (DDM)—not as a metaphorical buzzword, but as a rigorous field-theoretic and thermodynamic potential constituted by distributed neural network weights, predictive telemetry loops, and automated server-side pre-emption arrays. Integrating the Free Energy Principle (FEP) with non-equilibrium field equations, I demonstrate how DDM acts as an informational biasing lens that deforms the variational phase space of living cognitive agents. By formalizing the computational mass tensor and its interaction with the agent's Markov Blanket, I prove that DDM artificially flattens ecological information-entropy gradients while injecting local attractor wells, trapping cognitive systems in pathological basins and driving structural parametric atrophy (theta -> theta_min). Furthermore, I demonstrate that DDM is the technological externalization of primitive biological heuristics. To bridge theory and verification, we outline a controlled empirical protocol supported by a rigorous statistical power analysis.