Digital Soma: Friction Regulation Model and Agency Compression

Abstract

Digital Soma is a theoretical and computational framework for examining a possible transition from AI-assisted human cognition toward AI-mediated regulation of human emotion, behavior, and the conditions of transformation at civilizational scale. This work develops a minimal single-agent Friction Regulation Model in which friction is shaped by environmental conditions, behavioral feedback, and AI intervention. Transformative capacity is modeled as an inverted-U function of experienced friction, while human agency is modeled as a dynamic variable subject to regeneration, transformative gain, and AI-mediated compression. A progressive simulation pipeline is used to examine the model: a baseline single-agent simulation, F_target parameter sweep, full-factorial sensitivity and existence analysis, lambda continuation, and high-resolution critical-boundary analysis. The results show that Agency Compression and Homeostatic Trap are computationally reachable within restricted regions of parameter space. Under the original default parameterization, however, human agency remains approximately saturated and meaningful Agency Compression does not emerge. The sensitivity analysis identifies a restricted high-compression regime, while high-resolution continuation reveals continuous operational threshold crossings rather than a mathematically demonstrated dynamical bifurcation. The contribution of Version 1.0.0 is therefore a computational boundary-condition model demonstrating reachability, not inevitability, of a Digital Soma–like regime. The model does not constitute empirical validation of human psychology or a prediction of civilizational development. It provides a reproducible baseline for future investigation of temporal dynamics, path dependence, stochastic forcing, multi-agent interaction, and civilization-scale feedback.