DecisionDNA exists for a single purpose:
To quantify how coordination drives performance — before the outcome arrives.
We study systems under pressure. Teams, markets, militaries, biological collectives, banks. Anywhere decisions must be made faster than complete information allows.
Our work sits where strategy, biology, and systems physics intersect.
How performance emerges from the interaction of metabolic rate and decision accuracy. Why coordinated systems converge toward accuracy² scaling. When systems exceed coordination capacity and decay becomes irreversible.
How breakdown begins years before financial symptoms. What patterns precede failure across industries. Why collapse appears sudden but is always measurable.
Football clubs. Sovereign banks. Wartime coalitions. Bio-metabolic machinery. AI infrastructure ecosystems.
When coordination falters, the curve bends. Every time.
Across domains we observe the same pattern:
High metabolism + High accuracy
Compounding advantage
High metabolism + Low accuracy
Noisy churn
Low metabolism + High accuracy
Paralysis and stagnation
Low metabolism + Low accuracy
Failure
English Premier League — R² > 0.90. Performance predicted by metabolic balance.
Six-Day War — Advantage accurately predicted ex-ante.
Nuremberg vs Tokyo tribunals — 84× throughput difference → 2-year outcome gap.
Banking crisis 2019-2023 — DBS/JPM compounding vs SVB/Credit Suisse collapse.
We do not model outcomes. We measure the physics that produce them.
Traditional management research examines outcomes. We measure pre-outcome metabolism.
Traditional strategy explains success in hindsight. We detect failure in advance.
Traditional data abundance leads to noise. Our method extracts coherence from overload.
We move from story to signal. From judgement to measurement. From analysis to physics.
We publish where scientific scrutiny is highest and where complexity forces clarity.
Work currently under review:
Organization Science
Strategic Management Journal
Additional manuscripts in development across biological coordination scaling, organizational failure prediction, and decision metabolism under AI acceleration.
Academic rigor is not marketing. It is the backbone of the instrument.
We are expanding the DecisionDNA evidence base across:
Multi-sector organizational casework
AI-amplified decision systems
Cross-scale metabolic mapping
Predictive failure windows
Interventions to increase decision vitality
Real-time metabolic monitoring
Our goal is not to describe the world. It is to instrument it.
A world where:
Organizations know when they are running too fast before they burn.
Investors see fragility before the market reprices it.
Leaders course-correct before momentum reverses.
Performance stops being hindsight and becomes foresight.
DecisionDNA research makes the future measurable.