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:我们考虑两种方法,一种是算法随机性来描述概率法。第一种是基因概率法。这种法律涉及非标准的机会概念。第二种是概率法。这种法律规定了每个实际可能的世界都必须满足的相对频率和随机性限制。虽然每个概念都有优点,但我们认为后者比前者有利。它支持非荷美法律的统一治理说明,并为Humein最佳系统账户中的问题提供了独立自主的解决方案。在这两种概念上,我们在概率法和相应的可能的世界之间有着更紧密的联系。传统概率法允许的某些历史在实际上不可能被排除。因此,这种法律避免了一种在判断之下的经验,但这种方法揭示了通常被忽视的其他自决类型。</s>