We study how macroscopic observational constraints restrict admissible microscopic explanatory structures when no intrinsic order or dynamics is assumed a priori. Starting from an unordered collection of measurement outcomes, we formulate inference as a constrained large deviation problem, selecting probability assignments that minimize relative entropy with respect to a reference measure determined solely by the measurement setup. We show that among all microscopic structures compatible with a given macroscopic constraint, those rendering the observation statistically most typical are selected. As an explicit illustration, we demonstrate how ordered microscopic structures can emerge purely from inference under constraint, even when the reference measure is fully permutation symmetric. Order is thus not assumed but inferred, serving here only as an illustrative example of a broader class of relational explanatory hypotheses constrained by observation.

翻译：本研究探讨在未预先假设任何内在序或动力学的情况下，宏观观测约束如何限制可容许的微观解释结构。我们从无序的测量结果集合出发，将推断问题表述为一个约束性大偏差问题，选择相对于仅由测量装置确定的参考测度具有最小相对熵的概率分配。我们证明，在所有与给定宏观约束相容的微观结构中，那些使观测在统计上最典型的结构会被选中。作为一个具体示例，我们展示了即使参考测度完全具有置换对称性，有序微观结构如何能纯粹在约束下通过推断而涌现。因此，序在此并非假设而是推断的结果，仅作为受观测约束的更广泛关系性解释假设类别的一个说明性示例。