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Integrated Information (Phi) of a Human Superorganism: Beyond Individual Consciousness

APPLYING TONONI'S INTEGRATED INFORMATION THEORY

Phi (Phi) measures a system's consciousness as its capacity to integrate information into a unified whole that cannot be decomposed into independent parts.

FOR N UNSYNCHRONIZED BRAINS (a crowd):

Phi_total ~ N x Phi_individual
Each brain is an independent information integrator.
The group is NOT conscious as a unit.
It is N separate consciousnesses.

FOR N PHASE-LOCKED BRAINS (entrained population):

Phase-locking creates effective CONNECTIONS between
brains — "virtual synapses" via shared timing.
Information that was independent becomes correlated.
The system can no longer be decomposed into N
independent units without information loss.
Phi_superorganism > Sum(Phi_individual)
The whole EXCEEDS the sum of its parts.
By definition: the superorganism is MORE CONSCIOUS
than the sum of individual consciousnesses.

WHAT KIND OF CONSCIOUSNESS?

Not a "group mind" that thinks like a person.
More like: a field of shared awareness that each
individual experiences as heightened empathy,
intuitive understanding, and collective purpose.
The Egyptians called it Ma'at.
The Khmer called it Dharma.
The Maya experienced it through ritual.
Modern neuroscience calls it inter-brain coherence.

THE COMPUTATIONAL HIERARCHY:

Neuron: ~1 bit/spike, ~10Hz = ~10 bits/sec
Neural assembly: ~100 neurons, ~100 bits/sec
Brain region: ~10^6 neurons, ~10^7 bits/sec
Whole brain: ~10^11 neurons, ~10^10 bits/sec
Superorganism: 500,000 brains, 7Hz phase-lock
= emergent computation at civilizational scale

Each level transcends the one below through INTEGRATION. Neurons → brain → civilization. The pyramid is the technology that enables the third transition.

NOTE: Phi calculation for 500,000 brains is computationally intractable (super-exponential). But the QUALITATIVE prediction is clear: phase-locking increases Phi. The superorganism has higher integrated information than the sum of its parts.

Submitted by Quantitative Analysis — Consciousness Theory June 06, 2026

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