Conditionals with conditional constituents pose challenges for the Thesis, the idea that the probability of a conditional is the corresponding conditional probability. This note is concerned with two proposals for overcoming those challenges, both inspired by early work of van Fraassen: the Bernoulli Semantics associated with Stalnaker and Jeffrey, and augmented with a mechanism for obtaining “local probabilities” by Kaufmann; and a proposal by Bacon which I dub Ordinal Semantics. Despite differences in mathematical details and emphasis of presentation, both proposals lend themselves for use as a basis for a modal-theoretic interpretation of embedded conditionals.

The goal of this note is to compare the two frameworks by implementing a model for the interpretation of conditionals in each, based on the same underlying probability model for non-conditional sentences. I show that in the Ordinal model, certain sentences are assigned probabilities that do not accord with intuitions. This problem is familiar from the literature on Bernoulli models and can be addressed by introducing Kaufmann-style local probabilities into Ordinal models. I then show that Bernoulli Semantics has other limitations, in that it assigns probabilities in violation of the Thesis to certain very complex formulas. The upshot is that a fusion of the theories may be our best shot at getting the predictions right.

具有条件成分的条件对论文提出了挑战，即条件的概率是相应的条件概率。本说明涉及克服这些挑战的两个建议，它们都受到 van Fraassen 早期工作的启发：与 Stalnaker 和 Jeffrey 相关的Bernoulli Semantics，并增加了 Kaufmann 获得“局部概率”的机制；还有培根的一个提议，我称之为Ordinal Semantics。尽管在数学细节和表达的重点方面存在差异，但这两个建议都可以用作嵌入式条件的模态理论解释的基础。

本说明的目的是通过实现一个模型来比较这两个框架，以解释每个框架中的条件句，该模型基于非条件句的相同潜在概率模型。我展示了在序数模型中，某些句子被分配了不符合直觉的概率。这个问题在有关伯努利模型的文献中很熟悉，可以通过将考夫曼式局部概率引入 Ordinal 模型来解决。然后我表明伯努利语义学还有其他局限性，因为它将违反论文的概率分配给某些非常复杂的公式。结果是，理论的融合可能是我们做出正确预测的最佳方法。