About Us
The Challenge of Measuring Consciousness
Consciousness has long been considered unmeasurable - a subjective phenomenon that defies quantification. Traditional approaches struggle because consciousness cannot be directly observed; we can only infer it from external markers like behavior, brain activity, or system responses.
This creates a fundamental problem: How do you measure something you cannot see?
What We Do
In biological systems: EEG patterns during sleep states and sustained attention reveal varying levels of conscious awareness
In organisations: Institutional decision-making patterns predict resilience or collapse
By applying the same theoretical framework across domains with appropriate mechanism-matched operationalisation, we test CGT through rigorous pre-registered studies and prospective predictions.
Our Approach
We measure two fundamental properties that underlie consciousness across complex adaptive systems where integration and regulation are separable: how information is integrated into coherent wholes, and how systems maintain differentiated responses to different contexts.
Neither property alone is sufficient. A system that integrates everything into sameness lacks awareness. A system with many states but no coherence lacks unity. Consciousness emerges from the balance.
Our proprietary methodology translates these principles into quantifiable metrics through:
Domain-specific operationalization — the same theoretical framework, adapted to each substrate
Rigorous validation — effect sizes, classification accuracy, and statistical significance
Prospective testing — predictions made public before outcomes are known
The formulas stay protected. The results speak for themselves.
Why It Matters
Making consciousness measurable transforms it from philosophy into science. Our research enables:
✓ Objective sleep state analysis for neuroscience and clinical applications
✓ Predictive institutional analysis identifying organizations at risk of collapse
Where others see an impossible measurement problem, we see patterns waiting to be decoded.
Our Work
Institutional Collapse Predictor
Track Record: 12/12 (100%)
Retrospective Cases:
- FTX (November 2022)
- Enron (December 2001)
- WeWork (September 2019)
- Theranos (2018)
- Bear Stearns (March 2008)
- Lehman Brothers (2008)
- WorldCom (2002)
- Washington Mutual (2008)
- Countrywide Financial (2008)
- AIG (2008)
Prospective Predictions:
- NVIDIA (September 2025): Predicted stable — validated November 2025 ✓
- Saks Global (December 2025): Predicted collapse — Chapter 11 filed January 2026 ✓
Current Predictions: We have 4 featured and ~20 active predictions being validated through 2027. Some predict collapse despite market optimism. Some predict survival despite widespread bankruptcy concerns. The goal is to demonstrate the model can distinguish between temporary trouble and actual collapse, not just be pessimistic about everything.
Results will validate or disprove the approach.
Sleep Neuroscience Research
EEG Consciousness Measurement Framework
Analysed 622 sleep transitions (12 subjects, Sleep-EDF database)
Published: "Dual Architecture of Consciousness Transitions" (bioRxiv, 2025)
Key Discovery: Consciousness employs TWO distinct mechanisms:
- 75% gradual integration (IIT-style)
- 25% threshold gating via spindles (GWT-style)
Results: Cohen's d = 2.814 (p<0.001) — first empirical reconciliation of competing consciousness theories.
Additional Validation:
- 160+ subjects across 3 independent datasets
- 143,000+ epochs analyzed
- Effect sizes d > 1.3 for wake/sleep discrimination
- 100% directional accuracy
Atmospheric Domain Exploration
The Bias-Coherence Index (BCI) applies CGT's formula to atmospheric ensemble forecasting, detecting high-error weather events with AUC = 0.880 across 14 UK storm systems (1,218 forecasts, 2021–2024).
This represents a boundary exploration of CGT in atmospheric systems - demonstrating domain transfer of the CGI formula while identifying operationalisation constraints.
Results available on Zenodo.