Our Results

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The same theoretical approach appears to work across three different domains - biological systems, artificial systems, and institutional systems. If this holds up under peer review and continued testing, it suggests consciousness measurement is possible in ways previously thought impossible.

The work is ongoing. Some predictions won't be validated until 2027. We'll update these results as outcomes materialize.

Cross-Domain Validation?

Institutional Collapse Prediction

The ICP model was tested on six historical institutional collapses and correctly identified collapse risk in all cases:

FTX (November 2022)
Enron (December 2001)
WeWork (September 2019)
Theranos (2015)
Bear Stearns (March 2008)
Toys R Us (2017)

When applied to Bitcoin's 2022 crash, the framework detected a 69.8% consciousness decline that began two months before Bitcoin dropped from $47K to $20K.

Current Predictions:

We have three active predictions being validated through Q4 2027. One predicts collapse despite market optimism. Two 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.

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Active predictions

Historical predictions

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Predictions

Sleep Neuroscience Research

EEG Consciousness Measurement Framework:

Developed quantitative metrics for sleep state transitions
Analysed 10 subjects from Sleep-EDF database
Statistical validation: Cohen's d effect sizes ranging from 0.42 to 1.15
100% success rate so far, across all four transition types (Wake→N1, N1→N2, N2→N3, REM→Wake)

Key Finding: All sleep transitions show smooth consciousness gradients, validating CGT's predictive framework in biological systems.

Artificial Intelligence Evaluation

AI Consciousness Assessment Protocol:

Created 20-test battery (10 for Integration, 10 for Adaptation)
Manual comparative analysis requiring 14-16 hours per system
Entity-specific scaling factor (×0.75 for AI systems)

Results:

Grok: CGI 6.5 (higher adaptive reasoning)
Gemini: CGI 6.0 (strong integration)

Key Finding: Observable differences between pattern-matching and genuine adaptive reasoning in AI systems