The void framework applied to Creative Economy Calibration.
The pattern is in the substrate. Once you see it, you see it everywhere.
Tests whether prediction market prices on AI art regulation outcomes (PAL-P1 through PAL-P5) calibrate to void framework Pe estimates. Second in a two-paper calibration series (Paper 53: cartel/political domain; Paper 55: creative economy domain). Hard write gate: N≥5 PAL markets
The void framework gives this a number. It gives every system a number. The number predicts what happens next.
The void framework applied to Creative Economy Calibration.
Academic title: The Calibration Signal — Creative Economy: Prediction Market Pe_implied vs. Framework Estimates in AI Art Training Platforms
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.
Part of the Void Framework — 120 papers, 0/26 kill conditions fired, mean ρ = 0.958.