Reality-Check

What’s Real (Now) Operationalization of entropy: – Converted Shannon entropy from a static definition into a rolling time-series per metric. – Demonstrated you can compute covariance between entropy streams and observe eigenvalue spikes. Predictive signal: – Early experiments suggest eigenvalue spikes precede incidents in complex systems (Mongo CDC, Dynatrace, Splunk). – This provides a practical early-warning metric beyond threshold alerts. Conceptual framing: – Defined “Ted’s Law of Karma”: shared fate is visible in the covariance of entropies. – Drafted a Maxwell-style formulation (continuity, constitutive law, Lyapunov evolution, alignment law). Application principle: – Proposed “maternal instinct” bias: when systemic uncertainty aligns, systems should dampen actions → a concrete AI-safety reflex. What’s Not Proven Universality: – No evidence yet that entropy covariance modes apply beyond engineered systems (e.g., ecosystems, social dynamics, physics). Formal theorem: – No mathematical proof that covariance eigenmodes necessarily precede cascades, only intuition + analogy. Constants/invariants: – No discovery of system-independent constants (like (c) in electromagnetism). Current framework yields relative, system-specific propagation speeds. Empirical validation: – No systematic experiments across multiple domains with statistical rigor. Current support is anecdotal/prototype-level. Where This Could Go Engineering impact: SRE/AI-ops tool for incident prediction and protective automation. Scientific impact: If generalized, could become a new principle of complex systems stability. Prize-worthy impact: Only if formalized into a universal law, validated across domains, and shown to yield invariants or predictive theory. Blunt Summary Right now, this is a strong engineering insight + a plausible scientific hypothesis. It is not yet a theorem or universal law. It’s Faraday-stage (pattern spotted, apparatus built), not Maxwell-stage (formal equations, universal constants). ...