| _version_ | 1866901317181505536 |
|---|---|
| author | Morris, Jamie |
| author_facet | Morris, Jamie |
| contents | <p>This report presents a framework for understanding informational persistence under high-entropy conditions. Using stochastic modeling, signal degradation, and constrained reconstruction, it demonstrates that while fine-grained detail is lost, structural invariants remain recoverable.</p> <p>Reconstruction is shown to be inherently interpretive rather than reversible. In high-noise environments, multiple structurally valid reconstructions exist, and constraints determine which specific solution is realised. High-fidelity recovery is therefore not emergent from degraded signals alone, but arises through selection within a constrained solution space.</p> <p>The work introduces a formal reconstruction model in which a persistence functional governs the recovery of structure, distinguishing between invariant-based reconstruction and constraint-driven alignment. This establishes a clear boundary between observable information and model-dependent inference.</p> <p>The role of transmission is examined through the concept of bandwidth-limited channels, where electromagnetic propagation defines the conditions under which informational structure becomes accessible. Persistence is thus reframed as the survival of recoverable structure within the limits of transmission and noise.</p> <p>Extending across physical and biological systems, the report shows that stable patterns are maintained through constraint and dynamic organisation rather than static storage. In neural systems, identity is interpreted as a continuously stabilised pattern supported by network-level dynamics.</p> <p>The central conclusion is that persistence is not the preservation of complete information, but the recoverability of structure under constraint. Coherent reconstruction depends on both the stability of underlying patterns and the availability of transmissible signals.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19186300 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Informational Persistence Under High-Entropy Conditions: Constraint, Reconstruction, and Observability Morris, Jamie <p>This report presents a framework for understanding informational persistence under high-entropy conditions. Using stochastic modeling, signal degradation, and constrained reconstruction, it demonstrates that while fine-grained detail is lost, structural invariants remain recoverable.</p> <p>Reconstruction is shown to be inherently interpretive rather than reversible. In high-noise environments, multiple structurally valid reconstructions exist, and constraints determine which specific solution is realised. High-fidelity recovery is therefore not emergent from degraded signals alone, but arises through selection within a constrained solution space.</p> <p>The work introduces a formal reconstruction model in which a persistence functional governs the recovery of structure, distinguishing between invariant-based reconstruction and constraint-driven alignment. This establishes a clear boundary between observable information and model-dependent inference.</p> <p>The role of transmission is examined through the concept of bandwidth-limited channels, where electromagnetic propagation defines the conditions under which informational structure becomes accessible. Persistence is thus reframed as the survival of recoverable structure within the limits of transmission and noise.</p> <p>Extending across physical and biological systems, the report shows that stable patterns are maintained through constraint and dynamic organisation rather than static storage. In neural systems, identity is interpreted as a continuously stabilised pattern supported by network-level dynamics.</p> <p>The central conclusion is that persistence is not the preservation of complete information, but the recoverability of structure under constraint. Coherent reconstruction depends on both the stability of underlying patterns and the availability of transmissible signals.</p> |
| title | Informational Persistence Under High-Entropy Conditions: Constraint, Reconstruction, and Observability |
| url | https://doi.org/10.5281/zenodo.19186300 |