Quantifying Consciousness: Phi, Information Integration, and Algorithmic Complexity — E8 Intelligence Research
Zenodo (CERN European Organization for Nuclear Research) September 3, 2026 DOI: 10.5281/zenodo.22269248 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key points | Argues that Integrated Information Theory's formalizations, including Φ as minimal information partition reduction and an algorithmic Kolmogorov-complexity variant, do not yield direct geometric ratios or base-60 patterns in the cited sources, though the theory's state-space lattice and partition structures share formal kinship with information geometry and convex optimization over probability simplexes rather than crystallographic symmetry. |
Abstract
FINDING: Integrated Information Theory (IIT) quantifies consciousness via Φ (phi), a measure of irreducible causal integration in a system, with formalizations varying by information-loss assumptions. | MATH: Φ = minimal information partition (MIP) reduction in effective information; effective information EI(X→Y) = H(Y|MIP) − H(Y|X); IIT 4.0 uses cause-effect power over system states, Φ^max over partitions; algorithmic variant: Φ_A = K(Y|X) − K(Y|X_removed) (Kolmogorov complexity difference). | CONNECTION: No direct geometric ratios (0.382, 0.618, 0.786, 1.618, 2.618) or base-60 appear in the cited sources; however, IIT's state-space lattice and partition structures share formal kinship with information geometry and convex optimization over probability simplexes — not crystallographic symmetry. | DEPTH: 6 — mathematically rigorous but domain-specific; no universal constant or harmonic ratio emerges; the algorithmic formulation (arXiv:1405.0126) adds a complexity-theoretic edge but rema Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com