 It would seem that this analysis accurately predicts our intuitions in (45) about \(\mathsf{B&gt;L}\). Let \(P_0\) be an agent’s credence before learning that Switch B is down. (45a) requires that \(P_0(\mathsf{L}\mid \mathsf{A\land B})\) is (or is close to) 1, (45b) requires that \(P_0(\mathsf{\neg L}\mid \mathsf{\neg A\lor \neg B})\) is (or is close to) 1. The agent also learns that Switch A is up, so \(P_0(\mathsf{A})\) is (or is close to) 1. All of this together seems to guarantee that \(P_0(\mathsf{B\mid L})\) is also very high. However, this is due to an inessential artifact of the example: the agent learned that Switch B was down after learning that Switch A is up. This detail does not matter to the intuition. As was seen with example (43), we often hold fixed facts that happen after the antecedent turns out false. Indeed, Adams’ Prior Probability Analysis makes the incorrect prediction that (43) is unassertible in its natural context.