Kearns and Star have previously recommended that we measure the degree to which a reason supports a conclusion, either about how to act or what to believe, as the conditional probability of the conclusion given the reason. I show how to properly formulate this recommendation to allow for dependencies and conditional dependencies among the considerations being aggregated. This formulation allows us to account for how considerations, which do not themselves favour a specific conclusion, can modify the strength of a reason for that conclusion, and thus to explain the intensifiers and attenuators described by Dancy. The formulation also accounts for the workings of partial undercutters in epistemology. I then show how my account avoids the counterexamples that Brunero levied against probability-based theories of the strengths of reasons. My account supports the theory, suggested by Kearns and Star, that the strengths of reasons are measured by conditional probabilities. If my account is successful, then it will count in favour of the idea that the strengths of reasons are measured on the same scale as are conditional probabilities.

Kearns 和 Star 之前曾建议我们衡量一个理由支持结论的程度，无论是关于如何行动或相信什么，作为给定理由的结论的条件概率。我展示了如何正确地制定此建议，以允许汇总的考虑因素之间存在依赖关系和条件依赖关系。这种表述使我们能够解释本身不支持特定结论的考虑因素如何修改得出该结论的理由的强度，从而解释丹西描述的增强器和衰减器。该公式还说明了认识论中部分削弱的工作原理。然后我展示了我的帐户如何避免布鲁内罗针对基于概率的理由强度理论提出的反例。我的叙述支持 Kearns 和 Star 提出的理论，即理由的强度是通过条件概率来衡量的。如果我的帐户是成功的，那么它将支持以下观点：理由的强度是在与条件概率相同的尺度上衡量的。