Entropy

A practical blueprint for the first entropy-capable version of Karma — using simple statistical measures and ClickHouse queries to detect surprise.

How Karma uses information-theoretic entropy to detect operational drift, learn expectations, and close the loop toward self-healing systems.

Karma now ingests, normalizes, and routes events from any CDC-like source into a shared ledger, optional graph, and an action loop — setting the stage for learned expectations and autonomous intervention.

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. ...