A mathematical sketch of governance invariants built on dimensionless normalization and fractal (renormalization group) stability. Outlines entropy-free invariants, control laws, and universal scaling patterns for safe automation.
An entropy-first formulation of dimensionless, fractal governance. Uses normalized entropy, transfer entropy, multiscale entropy, and entropy production as invariants to detect cascades and shape safe, self-similar automation.
Abstract This document outlines a foundational perspective for a possible future discipline of runtime epistemology — the study of how infrastructure systems can quantify their own state of divergence from intended behavior. It proposes that Shannon entropy offers a mathematically principled basis for measuring runtime drift in live systems, forming the core of a design pattern suitable for both operational reliability and machine-driven reasoning. Introduction Contemporary infrastructure systems are increasingly dynamic, distributed, and subject to change. While observability tools have improved, systems still rely on humans to reconcile what is happening with what was supposed to happen. This epistemic gap — the difference between actual and intended behavior — remains largely qualitative, ad hoc, and unmeasured. ...