Ben Schwan
Case Western Reserve University
Reuben Stern
Kansas State University
There are cases of ineffable learning — i. e., cases where an agent learns something, but becomes certain of nothing that she can express — where it is rational to update by Jeffrey conditionalization. But there are likewise cases of ineffable learning where updating by Jeffrey conditionalization is irrational. In this paper, we first characterize a novel class of cases where it is irrational to update by Jeffrey conditionalization. Then we use the d-separation criterion to develop a causal understanding of when and when not to Jeffrey conditionalize that bars updating by Jeffrey conditionalization in these cases. Finally, we reflect on how the possibility of so-called “unfaithful” causal systems bears on the normative force of the causal updating norm that we advocate.
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Inference to the Best Explanation in Uncertain Evidential Situations.Borut Trpin & Max Pellert - 2019 - British Journal for the Philosophy of Science 70 (4):977-1001.
Jeffrey conditionalization: proceed with caution.Borut Trpin - 2020 - Philosophical Studies 177 (10):2985-3012.

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