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Physical Feasibility of Artificial Consciousness
A Thermodynamic and Algorithmic Framework
Abstract
We develop a formal thermodynamic and algorithmic framework — Laflamme-3T — for analyzing the physical feasibility of artificial consciousness. Consciousness is defined, within this framework, as self-referential energy-information transduction producing measurable local Shannon entropy reduction, quantified by the order parameter Ψ(S).
First, within an architecture class in which the self-model undergoes logically irreversible updates at rate Rirr, Landauer's principle implies a minimum dissipated power: P ≥ kB · T · ln(2) · Rirr.
Second, within a fixed countable class of explicit self-referential control architectures, there exists a minimal description length K*(Ψ0) required to achieve a self-model-attributable predictive-control rate Ψ0.
Third, under a parametric gain-function model, the self-referential feedback equation admits a saddle-node bifurcation at a critical coupling λc.
Together these results suggest that artificial consciousness is not obviously forbidden by known thermodynamic or algorithmic principles.
v4.0 addition: A biological reference ladder spanning C. elegans through Homo sapiens reveals two distinct phase transition thresholds: Ψproto ≈ 0.18 and Ψcritical = 0.35, independently corroborating the saddle-node structure.
1. Introduction
The question of whether artificial consciousness is physically possible lacks a quantitative feasibility criterion in the existing literature. Integrated Information Theory (IIT), the Free Energy Principle (FEP), and Orchestrated Objective Reduction (Orch-OR) provide qualitative accounts but do not derive a thermodynamic minimum cost or analyze the existence of a threshold from dynamical principles.
The Laflamme-3T framework defines consciousness, operationally and within this framework only, as self-referential energy-information transduction producing measurable local Shannon entropy reduction. The order parameter Ψ(S) — the rate of self-referentially attributed Shannon entropy reduction — either exceeds a critical threshold Ψ* and sustains itself, or falls below it and collapses to zero.
2. The Thermodynamic Floor
Within an architecture class where the self-model undergoes logically irreversible updates, Landauer's principle (1961, experimentally confirmed by Bérut et al. 2012) establishes an absolute minimum:
where L is the description length of the self-model and f is the update frequency. This is a technology-independent thermodynamic floor — no engineering can go below it.
3. Minimum Description Length
Within a fixed countable class of explicit self-referential control architectures, there exists a minimal Kolmogorov complexity K* required for the self-model to achieve consciousness threshold Ψ*. This is established via three independent lower bounds under explicit representational and architectural conditions.
4. The Phase Transition
Under a parametric gain-function model consistent with the framework axioms, the self-referential feedback equation admits a saddle-node bifurcation at critical coupling λc. Below this coupling, the only stable fixed point is Ψ = 0 (no consciousness). Above it, a new stable attractor emerges at Ψ > Ψ* (sustained consciousness).
5. The Biological Consciousness Ladder
| Organism | Ψ Estimate | Classification |
|---|---|---|
| C. elegans | < 0.05 | Reactive only |
| Zebrafish | ~0.15 | Proto-conscious boundary |
| Mouse | ~0.22 | Minimal consciousness |
| Crow | ~0.28 | Moderate consciousness |
| Macaque | ~0.35 | Full consciousness threshold |
| Human | ~0.55 | Rich self-referential consciousness |