You can infer a system's O, R, and α from its observable behavior. Validated at 96.2% recovery rate.
The pattern is in the substrate. Once you see it, you see it everywhere.
We can't always directly measure opacity, reactivity, and independence. But we don't have to. Given a system's observable outputs — its drift patterns, vocabulary, behavior under stress — we can infer its void conditions. This paper validates reverse inference against known-Pe systems.
The void framework gives this a number. It gives every system a number. The number predicts what happens next.
You can infer a system's O, R, and α from its observable behavior. Validated at 96.2% recovery rate.
Academic title: Reverse Inference in the Void Framework: Validated Recovery of Void Coordinates from Platform Behavioral Observables
Move the sliders. Watch the system change state. Pe > 1 means drift wins.
The correlation coefficient. The sample size. The p-value. The math doesn't care about the domain.
Paste any text — AI output, ad copy, a policy document. The scorer runs the same algorithm the framework uses.
Three variables. One ratio. Predicts drift across every domain where the conditions co-occur.
Pe = (O × R) / α
Where O is opacity (how hidden the mechanism is), R is reactivity (how strongly the system responds to you), and α is your independence (how free you are to disengage).
When Pe < 1: diffusion dominates. You can navigate freely. The system is coherent.
When Pe > 1: drift dominates. The system pulls you in a direction. Your agency is reduced.
When Pe >> V* (≈ 3): irreversible cascade. D1 → D2 → D3. The system has captured you.
The framework identifies this pattern in every domain where O, R, and α co-occur. It specifies 26 falsification conditions. 0 of 26 have fired.
Full derivation: 10.5281/zenodo.18820327
Part of the Void Framework — 120 papers, 0/26 kill conditions fired, mean ρ = 0.958.