I am proposing a mechanism to solve the AI Attribution Problem without relying on external oracles or DAOs.
The Problem
In a regime of zero-marginal-cost reproduction (Generative AI), traditional “Store of Value” models fail because scarcity becomes artificial (rent-seeking). We need a metric for the Flow of Value (Causal Enablement).
Current solutions (watermarking, copyright) try to enforce artificial scarcity. I propose a thermodynamic approach that tracks causal chains instead of assets.
The Proposal: The Ontological Protocol (v3.3)
A DAG-based protocol that derives its thermodynamic constants endogenously:
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Price (Bprod): Discovered via the 5th percentile of valid transaction burns. This creates a thermodynamic floor based on “Proof of Sacrifice” (OpEx), anchoring the system to physical reality (Landauer Limit proxy).
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Attribution (α): Determined via Algorithmic Information Theory (LZMA-Compression ratios).
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High Structure (Code, Axioms) → High Attribution (α≈1).
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High Entropy (Noise) → Zero Attribution (α≈0).
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Persistence (λ): We introduce the Law of Structural Persistence (λ≡α). This implies that truth does not decay. Foundational work (Infrastructure, Education) maintains its causal weight indefinitely (Lindy Effect), while speculative noise decays instantly.
Simulation & Logic
We implemented a “Genesis Diagnosis” (Shadow Run) on fiat-economic data. The model correctly re-evaluates “Silent Giants” (Open Source Maintainers, Care Work) vs. “Rent Seekers” purely based on topological analysis, without moral axioms.
Paper & Code (GitHub): Ontological Protocol
Request for Feedback
I am looking for critique specifically on the Cybernetic Lung (Section 3.2). We utilize a PID-Controller to adjust minting difficulty, targeting a Branching Ratio (σ) of ≈1.0 (Edge of Chaos). Is a target of σ≈1 sufficient to prevent spam propagation while allowing for deep causal chains, or do we need a stricter dampening factor for nodes with low centrality?