This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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(Co-Authored and Co-Created with AI)
I am proposing a thermodynamic economic protocol designed to solve the AI Attribution Problem and align incentives in a post-scarcity economy, without relying on central planners or external oracles.
I am releasing the whitepaper (v3.3) and reference code today and am looking for "Red Teaming" from the alignment community regarding the protocol's resistance to Goodhart’s Law.
The Problem: Entropy & Value
In an era of generative AI (zero-marginal-cost reproduction), the traditional economic model of "Store of Value" (Asset Ownership) collapses because scarcity becomes artificial or enforced by rent-seeking gatekeepers.
Furthermore, current attention economies incentivize predatory coupling (e.g., addiction loops, doom-scrolling), where an agent maximizes time-on-site while increasing the entropy (disorder/dysfunction) of the user. This is a classic Moloch trap.
The Proposal: Radical Endogeneity
The Ontological Protocol (TOP) shifts the paradigm from Object-Oriented (owning things) to Process-Oriented (causal enablement). It is based on the axiom Esse est Operari (To be is to execute).
The system acts as a third-order cybernetic loop that derives its constants endogenously:
Price as a Thermodynamic Floor (Bprod)
Instead of arbitrary pricing, the system discovers the "Minimum Viable Burn" via the 5th percentile of valid transactions. This acts as a Proof of Sacrifice. An agent must expend thermodynamic work (OpEx) to write to the graph. This prevents spam and Sybil attacks purely through physics, not identity verification.
Truth as Structure (α)
We attempt to solve attribution without subjective consensus. We use Algorithmic Information Theory (specifically LZMA compression ratios) as a proxy for "Structure".
High Structure (Code, Axioms, DNA) → High Attribution Weight (α≈1).
High Entropy (Noise, Randomness) → Low Attribution Weight (α≈0).
The Law of Structural Persistence (λ≡α)
We propose that the temporal decay of a causal link should be inverse to its entropy. Truth does not decay.
Mechanism: If a node produces high-structure output (e.g., foundational education or infrastructure), its causal link (λ) remains active indefinitely (Lindy Effect).
Result: This solves the "Teacher's Dilemma" (where input and output are separated by decades) without needing tax-based redistribution. The teacher holds equity in the student's future graph.
The Alignment Mechanism: The Metabolic Loop
Perhaps most relevant to AI Safety is the Metabolic Loop (Appendix G).
We redefine consumption not as a "dead end," but as Metabolic Input for actuation.
Scenario: A gamer plays a game.
Old Model: Value is destroyed (Time spent). Developer profits from addiction.
TOP Model: The game is an input that recharges the human's "Stasis Capacitor" (Blife). If the human subsequently produces order (e.g., writes code, farms wheat), the Game Developer receives a retroactive Ripple Score from that output.
Incentive: If the game creates addiction (output → 0), the developer's revenue stream dries up. The system mathematically aligns the provider's profit with the user's agency.
Request for Critique
The full specification (v3.3) includes the mathematical derivation of these constants and a Python simulation of the "Genesis Diagnosis."
I am specifically looking for feedback on:
The LZMA Proxy: Is using compression ratios as a proxy for "Value" robust enough against adversarial inputs (e.g., highly compressible but useless patterns), or is the requirement for downstream adoption (Children in the DAG) a sufficient filter?
Systemic Rigidity: Does the strict coupling of λ≡α create a "Tyranny of Structure" that undervalues high-entropy artistic expression?
(Co-Authored and Co-Created with AI)
I am proposing a thermodynamic economic protocol designed to solve the AI Attribution Problem and align incentives in a post-scarcity economy, without relying on central planners or external oracles.
I am releasing the whitepaper (v3.3) and reference code today and am looking for "Red Teaming" from the alignment community regarding the protocol's resistance to Goodhart’s Law.
The Problem: Entropy & Value
In an era of generative AI (zero-marginal-cost reproduction), the traditional economic model of "Store of Value" (Asset Ownership) collapses because scarcity becomes artificial or enforced by rent-seeking gatekeepers.
Furthermore, current attention economies incentivize predatory coupling (e.g., addiction loops, doom-scrolling), where an agent maximizes time-on-site while increasing the entropy (disorder/dysfunction) of the user. This is a classic Moloch trap.
The Proposal: Radical Endogeneity
The Ontological Protocol (TOP) shifts the paradigm from Object-Oriented (owning things) to Process-Oriented (causal enablement). It is based on the axiom Esse est Operari (To be is to execute).
The system acts as a third-order cybernetic loop that derives its constants endogenously:
Price as a Thermodynamic Floor (Bprod)
Instead of arbitrary pricing, the system discovers the "Minimum Viable Burn" via the 5th percentile of valid transactions. This acts as a Proof of Sacrifice. An agent must expend thermodynamic work (OpEx) to write to the graph. This prevents spam and Sybil attacks purely through physics, not identity verification.
Truth as Structure (α)
We attempt to solve attribution without subjective consensus. We use Algorithmic Information Theory (specifically LZMA compression ratios) as a proxy for "Structure".
High Structure (Code, Axioms, DNA) → High Attribution Weight (α≈1).
High Entropy (Noise, Randomness) → Low Attribution Weight (α≈0).
The Law of Structural Persistence (λ≡α)
We propose that the temporal decay of a causal link should be inverse to its entropy. Truth does not decay.
Mechanism: If a node produces high-structure output (e.g., foundational education or infrastructure), its causal link (λ) remains active indefinitely (Lindy Effect).
Result: This solves the "Teacher's Dilemma" (where input and output are separated by decades) without needing tax-based redistribution. The teacher holds equity in the student's future graph.
The Alignment Mechanism: The Metabolic Loop
Perhaps most relevant to AI Safety is the Metabolic Loop (Appendix G).
We redefine consumption not as a "dead end," but as Metabolic Input for actuation.
Scenario: A gamer plays a game.
Old Model: Value is destroyed (Time spent). Developer profits from addiction.
TOP Model: The game is an input that recharges the human's "Stasis Capacitor" (Blife). If the human subsequently produces order (e.g., writes code, farms wheat), the Game Developer receives a retroactive Ripple Score from that output.
Incentive: If the game creates addiction (output → 0), the developer's revenue stream dries up. The system mathematically aligns the provider's profit with the user's agency.
Request for Critique
The full specification (v3.3) includes the mathematical derivation of these constants and a Python simulation of the "Genesis Diagnosis."
I am specifically looking for feedback on:
The LZMA Proxy: Is using compression ratios as a proxy for "Value" robust enough against adversarial inputs (e.g., highly compressible but useless patterns), or is the requirement for downstream adoption (Children in the DAG) a sufficient filter?
Systemic Rigidity: Does the strict coupling of λ≡α create a "Tyranny of Structure" that undervalues high-entropy artistic expression?
Paper & Code (GitHub):
https://github.com/SkopiaOutis/ontologial-protocol