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).