Equilibrium-Driven
Intelligence Systems
E_F = f(ΔC − ΔΩ) ≥ 0
Mathematical coordination for decentralised systems. Where formulas perform the heavy lifting.
Get in Touch
Mathematical coordination for decentralised systems. Where formulas perform the heavy lifting.
Get in TouchA deterministic equilibrium regulator — not a predictive text engine.
Proportional Harm Model quantifies harm-load and applies proportionality calculus. Converts systemic failures into quantified harm values.
Enforces non-dominant strategies and prevents coercive imbalance. Stability occurs only when coherence exceeds ownership-load.
Models drift states, suppression transitions, chronology collapse, and forced equilibrium reset. State transitions governed by mathematical certainty.
Equilibrium mathematics applied across critical systems.
Market coordination, extraction economy reform, surplus equilibrium
Predictive drift detection, diabetes intervention, dementia monitoring
Wireless energy grids, RF-exposure governance, network allocation
Proportional harm modeling, breach cascade analysis, redress systems
NashMark AI applies the Nash-Markov equilibrium framework to real-world systems. Unlike conventional AI that optimises for prediction, we model for stability.
The NMAI Core is open-source (AGPL-3.0). The Sansana/PHM calibration layer provides domain-specific harm quantification under restricted license.
From the Monkey Mind Theory to Nash Inevitability — the mathematical foundations are published under the Truthfarian framework.
C = Coherence (system stability)
Ω = Ownership-load (pressure, burden)
Contact us for licensing, partnerships, and consultation.
contact@nashmarkai.com