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 of integration and complexity dynamics reveal separable dimensions of conscious experience. Recent work has shown a clean double dissociation in inhaled DMT: one dynamic measure (mac_I) predicts the intensity of experience, while EEG microstate configuration predicts visual content type (complex vs elementary imagery). Similar patterns are observed across sleep–wake transitions, propofol sedation, and ketamine.
In organisations: Institutional decision-making and adaptive patterns predict resilience or collapse. Our proprietary ICP tool and CGI-based codebook have demonstrated strong prospective and retrospective validation. By applying the same theoretical framework (CGI = √(φ × ρ)) across domains with mechanism-matched operationalisation, we test CGT through rigorous pre-registered studies and continuous prospective validation.
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 in complex adaptive systems: integration (how information is bound into coherent wholes) and differentiation (how systems maintain distinct, context-sensitive responses).
Neither property alone is sufficient. A system that integrates everything into sameness lacks awareness. A system with many differentiated states but no coherence lacks unity. Consciousness emerges from the dynamic balance between the two.
Our proprietary methodology translates these principles into quantifiable metrics through:
Domain-specific operationalization — the same theoretical framework, (CGI = √(φ × ρ)), adapted to each substrate
Rigorous validation — effect sizes, surrogate testing, and pre-specified statistical criteria
Prospective testing — predictions made public before outcomes are known
The core formulas remain protected where proprietary. The empirical results are fully open. 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.