The thesis that agents should calibrate their beliefs in the face of higher-order evidence—i.e., should adjust their first-order beliefs in response to evidence suggesting that the reasoning underlying those beliefs is faulty—is sometimes thought to be in tension with Bayesian approaches to belief update: in order to obey Bayesian norms, it's claimed, agents must remain steadfast in the face of higher-order evidence. But I argue that this claim is incorrect. In particular, I motivate a minimal constraint on a reasonable treatment (...) of the evolution of self-locating beliefs over time and show that calibrationism is compatible with any generalized Bayesian approach that respects this constraint. I then use this result to argue that remaining steadfast isn't the response to higher-order evidence that maximizes expected accuracy. (shrink)

Recent years have seen the rise of fittingness-first views, which take fittingness to be the most basic normative feature, in terms of which other normative features can be explained. This paper poses a serious difficulty for the fittingness-first approach by showing that existing fittingness-first accounts cannot plausibly accommodate an important class of reasons: reasons not to believe a proposition. There are two kinds of reasons not to believe a proposition: considerations that are counterevidence; and considerations that count against believing the (...) proposition without indicating that it is false. I will argue that the fittingness- first accounts have trouble accommodating reasons of the latter kind. (*published with open access). (shrink)