Modern scientific explanations across physics, biology, and cognitive science rely on a shared set of idealized assumptions, including unbounded integration, perfect global accessibility, fully transparent self-modeling, disembodied information processing, and memory without persistent physical storage. In this work, we conduct a large-scale, constraint-driven computational investigation into the joint admissibility of these assumptions under irreducible physical and informational constraints. Without introducing new entities, forces, or optimization objectives, we explore whether any self-consistent realization exists in which all such assumptions can coexist. Across an ensemble of 8,192 independent trajectories, no jointly admissible configuration was found. Instead, all systems entered a structurally defined no-go region characterized by instability, causal overload, and collapse of self-consistent explanatory state. A systematic assumption-removal analysis reveals that admissibility is restored upon removal of any single assumption from a restricted subset, yielding a uniquely constrained and stable explanatory structure. These results establish a forced assumption sacrifice theorem: reality admits no explanatory framework in which all standard explanatory ideals are simultaneously valid. Explanation itself is shown to be a constrained dynamical regime, imposing principled limits on transparency, integration, and abstraction across scientific domains.
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Drew Slawson (Sun,) studied this question.
www.synapsesocial.com/papers/6978551eccb046adae5174f6 — DOI: https://doi.org/10.5281/zenodo.18365637
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