The temporal distinction operator. Measures how distinctions evolve over time for predictive computation.
The temporal distinction operator — measures how distinctions evolve over time using the DT Equation of Motion: dD/dt = ∇(Distinction Density) + Constraint Forces - Conservation Drag. Enables predictive causal computation without training data — it needs initial conditions and the 7 laws, not statistical correlations. Applications in finance (market phase transition detection), security (attack prediction), and AI (temporal causal reasoning).
Every project at Distinction Labs traces back to the Seven Axioms. This project is no exception.
DeltaT (ΔT) is built on the mathematical foundation of Distinction Dynamics — the same axioms that govern the Distinction Engine, IntelliPhoton, and every other system in the lab.