There is some apparent incompatibility between Bayesian diachronic norms for rationally updating one’s credences and defeasibility. Most epistemologists think that doxastic attitudes formed based on experience are vulnerable to having some positive epistemic status, such as their justification, weakened by learning of additional information, namely defeaters. One kind of defeater is undercutting defeaters which defeat, roughly, by calling into question whether one’s reason for believing somethin…
Read moreThere is some apparent incompatibility between Bayesian diachronic norms for rationally updating one’s credences and defeasibility. Most epistemologists think that doxastic attitudes formed based on experience are vulnerable to having some positive epistemic status, such as their justification, weakened by learning of additional information, namely defeaters. One kind of defeater is undercutting defeaters which defeat, roughly, by calling into question whether one’s reason for believing something really is sufficient for rationally believing it. Some epistemologists, such as Weisberg (2009; 2015), argue that Jeffrey conditionalization fail to represent undercutting defeat in virtue of being rigid. Borrowing from Comesaña (2020), I call this Weisberg’s puzzle. The classic norm for updating one’s credences, strict conditionalization, appear to fail in representing undercutting defeat for other reasons as well. In this essay, I describe these norms and argue that while strict conditionalization is ill-suited for representing cases of undercutting defeat, Jeffrey conditionalization can do it when applied to an appropriate evidential partition, namely one that takes the agent’s higher-order beliefs into account. In particular, I favor the solution proposed by Lange (2024) of applying Jeffrey conditionalization to a partition including the agent’s higher-order beliefs. I also discuss some surrounding problems, such as the inputs problem with respect to Jeffrey conditionalization, as well as how to interpret epistemic defeat in Bayesian terms.