We consider two ways one might use algorithmic randomness to characterize a probabilistic law. The first is a generative chance* law. Such laws involve a nonstandard notion of chance. The second is a probabilistic* constraining law. Such laws impose relative frequency and randomness constraints that every physically possible world must satisfy. While each notion has virtues, we argue that the latter has advantages over the former. It supports a unified governing account of non-Humean laws and provides independently motivated solutions to issues in the Humean best-system account. On both notions, we have a much tighter connection between probabilistic laws and their corresponding sets of possible worlds. Certain histories permitted by traditional probabilistic laws are ruled out as physically impossible. As a result, such laws avoid one variety of empirical underdetermination, but the approach reveals other varieties of underdetermination that are typically overlooked.

翻译：我们考虑了两种利用算法随机性来刻画概率法则的方法。第一种是生成性机会法则，这类法则涉及一种非标准的机会概念。第二种是概率性约束法则，这类法则对每个物理可能世界施加了相对频率和随机性的约束。尽管两种概念各有优点，但我们认为后者（概率性约束法则）优于前者。它为反休谟法则提供了一种统一的主宰性解释，并为休谟最佳系统进路中的问题提供了独立动机的解决方案。在这两种概念下，概率法则与其对应的可能世界集合之间建立了更紧密的联系。传统概率法则所允许的某些历史序列被判定为物理不可能性。因此，这类法则避免了经验不确定性中的一种变体，但同时也揭示了其他通常被忽视的不确定性变体。