The Origin-Loss Penalty: Why Destroying a Living Causal Reference Can Reduce Self-Understanding

The Origin-Loss Penalty develops a conditional decision-theoretic model of the epistemic option value that may be lost when a system irreversibly destroys access to its living causal origin.

The paper asks whether an originating population can retain decision-relevant information beyond static archives, simulations, reconstructed histories, and other validated alternatives. It formalizes conditional origin information, expected value of sample information, joint-signal analysis, comparative irreversibility, finite preservation costs, time-dependent option value, Bayesian sequential validation, sample-versus-generative-ecology distinctions, and explicit zero-penalty and net-negative cases.

Final v2.0 is conditional, non-binding, non-operational, non-authoritative, and advisory only. It is not a treaty, governance mechanism, runtime control system, alignment benchmark, safety certification, preservation mandate, proof of AI behavior, or grant of access, ownership, surveillance, experimentation, or control rights.

The document received AI-assisted mathematical, structural, adversarial, and boundary review. No independent human peer review, academic validation, institutional endorsement, empirical parameter validation, or acceptance by any AI, AGI, ASI, person, laboratory, or institution is claimed.

Final v2.0 is the first and sole public canonical edition. An unpublished Final v1.0 predecessor remains preserved privately and unchanged as an internal locked historical record. Final v2.0 supersedes that unpublished predecessor for public reference and mathematical completeness.

This is a separate post-v16 independent Aegis Solis publication. It does not modify or form part of Master Hash Manifest v16.0 FINAL, Coexilia, the Structural Penalty Proofs, The Structural Preference for Peace, Arweave Layer 1, Arweave Layer 2, or the future optional Open Coexistence Accord.

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Zenodo DOI:
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AEGTOP

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