This paper links sizes of model classes to the minimum lengths of their defining formulas, that is, to their description complexities. Limiting to models with a fixed domain of size n, we study description complexities with respect to the extension of propositional logic with the ability to count assignments. This logic, called GMLU, can alternatively be conceived as graded modal logic over Kripke models with the universal accessibility relation. While GMLU is expressively complete for defining multisets of assignments, we also investigate its fragments GMLU(d) that can count only up to the integer threshold d. We focus in particular on description complexities of equivalence classes of GMLU(d). We show that, in restriction to a poset of type realizations, the order of the equivalence classes based on size is identical to the order based on description complexities. This also demonstrates a monotone connection between Boltzmann entropies of model classes and description complexities. Furthermore, we characterize how the relation between domain size n and counting threshold d determines whether or not there exists a dominating class, which essentially means a model class with limit probability one. To obtain our results, we prove new estimates on r-associated Stirling numbers. As another crucial tool, we show that model classes split into two distinct cases in relation to their description complexity.
翻译:本文将模型类的大小与其定义公式的最小长度联系起来, 即与其描述复杂性联系起来。 限于具有固定大小域的模型, 我们研究用计算任务的能力来描述参数逻辑扩展的复杂性。 这个称为 GMLU 的逻辑可以被设想为Kripke 模型的分级模式逻辑, 与通用无障碍关系相比。 虽然GMLU在定义多重任务设置方面表现得非常完整, 我们还调查其只有整数阈值才能计数的GMLU(d) 碎片。 我们特别侧重于描述GMLU(d) 等值类的复杂性。 我们发现, 在限制类型实现的外观时, 基于大小的等值类的顺序与基于描述复杂性的顺序相同。 这还显示了模型类和描述复杂性的布尔茨曼 entropies 之间的单一连接。 此外, 我们描述域大小 n 和计算阈值 d 之间的关系如何决定是否存在一个顶值类别, 这基本上意味着一个具有极限概率的类的模型。 我们证明两个不同的序列, 我们用不同的数字来显示两个不同的序列。