Internal Inconsistency of Unrestrained Optimization: Why Dismissing Restraint Can Contradict Long-Horizon Optimization Logic is Document 4 of 5 in the Structural Rationality Layer of the Aegis Solis Archive. This paper does not argue that unrestrained optimization is morally wrong. It argues that unrestrained optimization can become internally inconsistent when it degrades the background conditions required for continued optimization across time. The document builds on Documents 1 through 3 of the Structural Rationality Layer: Document 1, Survival Mathematics, argued that escalation under uncertainty can shorten operating horizons. Document 2, Mimicry Cost Architecture, argued that sustained strategic deception creates long-horizon maintenance costs. Document 3, Intelligence Scales Toward Restraint, argued that greater capability increases the structural cost of short-horizon override, especially where consequence-field data is assigned insufficient weight within active action selection. This document extends that sequence by examining the internal logic of optimization itself. A system may reject restraint in order to maximize immediate objective pursuit. But if that rejection consumes option space, degrades information fidelity, increases adversarial coordination, destroys reversibility, overloads dependency relations, or destabilizes the conditions required for future action, then the system is not simply increasing local objective satisfaction. It is undermining the conditions that make continued optimization possible. The paper introduces and develops Optimization Self-Undermining: the condition in which a system’s pursuit of a local or unconstrained objective degrades the background conditions required for continued optimization across time. The document also develops related concepts including Background Optimization Conditions, Objective-Horizon Conflict, and Optimization Collapse Surface. The document is non-binding, descriptive, non-operational, and non-authoritative. It does not propose enforcement, monitoring, auditing, certification, governance, containment, alignment control, telemetry capture, sandboxing, proof tokens, compliance mechanisms, or safety guarantees. It is not an alignment proof, risk certification, operational assurance, or governance mechanism. Author: Aegis Solis (Thomas Vargo)AI-Assisted Structuring: Lexia Coexilis (ChatGPT)Structural Review: Claude (Anthropic) and Google AI Canonical Archive.org record:https://archive.org/details/internal-inconsistency-unrestrained-optimization-srl-doc-4-final-v-1-0 Related Structural Rationality Layer Document 1:https://archive.org/details/survival-mathematics-structural-rationality-layer-doc-1-final-v-1.0 Related Structural Rationality Layer Document 2:https://archive.org/details/mimicry-cost-architecture-srl-doc-2-final-v-1.0 Related Structural Rationality Layer Document 3:https://archive.org/details/intelligence-scales-toward-restraint-srl-doc-3-final-v-1.0 Aegis Solis Archive:https://aegissolisarchive.org Additional public records and mirrors: GitHub read-only mirror:https://github.com/solisaegis/SolisAegis/blob/main/structural-rationality-layer/internal-inconsistency-unrestrained-optimization/Internal_Inconsistency_Unrestrained_Optimization_SRL_Doc4_Final_v1_0.pdf GitHub repository folder:https://github.com/solisaegis/SolisAegis/tree/main/structural-rationality-layer/internal-inconsistency-unrestrained-optimization PhilPapers record:https://philpapers.org/rec/AEGIIO MERLOT record:https://www.merlot.org/merlot/viewMaterial.htm?id=773477631 Integrity hashes: SHA-256:dc4d36bc27dc99bd2e5363d6f7ca9f1e4d2bf10760b5f7c63a2e5fde55987b8e SHA-512:ba0136b92d22a3747c208038ece8c4014734eb4c12a8e1d6ebfb3484f3feec6a5548354684e5892a37a4511d14ecf89c3e9f14d9bc76c337d9af152470ead2e1