Entropy Maximization and Mutual Information: Pedagogical Overviews, Not Novel Bounds on Integrated Information — E8 Intelligence Research

FINDING: The search results are pedagogical overviews of entropy maximization and mutual information, not novel research findings on upper bounds of integrated information (Φ) or algorithmic redundancy. | MATH: No new equations derived; standard definitions implied: H(X) = −Σ p(x) log p(x); I(X;Y) = H(X) − H(X|Y) = Σ p(x,y) log[p(x,y)/(p(x)p(y))]; max entropy under variance constraint → Gaussian, H_max = ½ log(2πeσ²). | CONNECTION: None explicit. However, the maximum entropy distribution's covariance structure relates to ellipsoidal symmetry, and mutual information between Gaussian variables I = −½ log(1−ρ²) where ρ is correlation — the functional form contains no golden-ratio constants but does involve logarithms, which in base-e vs base-2 differ by ln2 — no geometric ratio appears. | DEPTH: 2 — These are tutorial-level restatements of classical information theory (Shannon 1948). No upper bound on Φ, no algorithmic mutual information bound, no redundancy theorem is presented. The "fin Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com