Verifying Good Regulator Conditions for Hypergraph Observers: Natural Gradient Learning from Causal Invariance via Established Theorems
Max Zhuravlev

TL;DR
This paper demonstrates that hypergraph observers satisfying causally invariant conditions also meet Good Regulator criteria, linking information geometry, natural gradient learning, and theoretical physics to derive specific regime parameters.
Contribution
It formalizes hypergraph observers as Good Regulators, applies information geometry to derive natural gradient learning, and connects Wolfram and Vanchurin frameworks through established theorems.
Findings
Hypergraph observers satisfy Good Regulator conditions.
Derived a closed-form formula for the regime parameter alpha.
Identified model-dependent predictions and observer regime variability.
Abstract
We verify that persistent observers in causally invariant hypergraph substrates satisfy the conditions of the Conant-Ashby Good Regulator Theorem. Building on Wolfram's hypergraph physics and Vanchurin's neural network cosmology, we formalize persistent observers as entities that minimize prediction error at their boundary with the environment. Applying a modern reformulation of the Conant-Ashby theorem, we demonstrate that hypergraph observers satisfy Good Regulator conditions, requiring them to maintain internal models. Once an internal model with loss function exists, the emergence of a Fisher information metric follows from standard information geometry. Invoking Amari's uniqueness theorem for reparameterization-invariant gradients, we show that natural gradient descent is the unique admissible learning rule. Under the ansatz M=F^2 for exponential family observers and one specific…
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Taxonomy
TopicsQuantum many-body systems · Quantum Computing Algorithms and Architecture · Quantum Mechanics and Applications
