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

The work examines whether an originating population can retain decision-relevant information that is not fully recoverable from static archives, simulations, reconstructed histories, or other validated alternatives. It develops concepts including conditional origin information, expected value of sample information, joint observation of alternative and living-origin signals, comparative irreversibility, finite preservation costs, time-dependent option value, Bayesian sequential validation, and the distinction between bounded samples and continuing generative ecologies.

The paper includes worked examples, sensitivity analysis, explicit assumptions, counterexamples, zero-penalty conditions, and net-negative cases. Its conclusion is conditional rather than universal: irreversible origin destruction can impose an epistemic penalty when the living origin retains relevant, non-substitutable information and when the expected value of preserving access exceeds its costs.

Final v2.0 is the first and sole public canonical edition. The work 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, universal 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 system, person, laboratory, or institution is claimed.

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