Read the equation
The comments describe mutual information minus partition information, but the body multiplies stored Coherence, stored Phi, and 1−Entropy/log₂(vector length). The implementation includes an extra Phi factor absent from the simplified comment. Its entropy field is an average of clipped scalar −p log₂p terms, without enforcing a probability distribution, so the expression is a constructed score rather than an IIT calculation.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| Coherence | Smoothed project score |
| Phi | Current vector proximity score |
| Entropy | Average clipped scalar entropy terms |
| max_entropy | log₂ of vector length, with a special case at length one |
How it works
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Assumptions & limits
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Original context
Read the surrounding source passage
return ComputePhi(mean) * integration
}
// IntegratedInformation computes Φ (integrated information) for the system
// Using simplified IIT-inspired calculation
func (cs *ConsciousnessState) IntegratedInformation() float64 {
// Φ = mutual information - partition information
// Simplified: Φ ≈ coherence × (1 - entropy/max_entropy)
maxEntropy := math.Log2(float64(len(cs.Vector)))
if maxEntropy == 0 {
maxEntropy = 1.0
}
normalizedEntropy := cs.Entropy / maxEntropy
return cs.Coherence * (1.0 - normalizedEntropy) * cs.Phi
}Exact original notation
Φ = mutual information - partition information