It seems like we care about at least two features of our credence function: gradational-accuracy and verisimilitude. Accuracy-first epistemology requires that we care about one feature of our credence function: gradational-accuracy. So if you want to be a verisimilitude-valuing accuracy-firster, you must be able to think of the value of verisimilitude as somehow built into the value of gradational-accuracy. Can this be done? In a recent article, Oddie has argued that it cannot, at least if we want the accuracy measure (...) to be proper. I argue that it can. 1Introduction2Some Nuts and Bolts3First Attempts4Oddie’s Constraint5The Good5.1Proximity over the disagreement metric 5.2Proximity over the magnitude metric 6The Bad and the Ugly 7Some More Good: The Role of Evenness of Distribution 8Some More Bad: Which Propositions to Privilege? 9Concluding Thoughts: Accuracy and Practical Value. (shrink)

Thirders sometimes feel compelled to give the same answer – a credence of 1/3 – to the original and the duplicating Sleeping Beauty problem, which leads to some unwanted consequences. I will argue that they do not have to feel compelled to give the same answer, because the original and the duplicating version of the Sleeping Beauty problem are different types of decision problems. If one accepts that it is rationally permissible to give different answers to different types of decision (...) problems, both versions do not require the same solution. (shrink)

How should we measure knowledge? According to the Counting Approach, we can measure knowledge by counting pieces of knowledge. Versions of the Counting Approach that try to measure knowledge by counting true beliefs with suitable support or by counting propositions known run into problems, stemming from infinite numbers of propositions and beliefs, difficulties in individuating propositions and beliefs, and cases in which knowing the same number of propositions contributes differently to knowledge. In this paper I develop a novel question-relative and (...) contextualist version of the counting approach, which measures an agent's knowledge by counting the number of complete answers of a contextually salient issue they can rule out. The question-relative and contextualist version of the Counting Approach avoids the issues for the proposition and belief-based systems, and offers a general, systematic, and explanatory system for measuring knowledge. (shrink)

There is an ongoing debate about which rule we ought to use for scoring probability estimates. Much of this debate has been premised on scoring-rule monism, according to which there is exactly one best scoring rule. In previous work, I have argued against this position. The argument given there was based on purely a priori considerations, notably the intuition that scoring rules should be sensitive to truthlikeness relations if, and only if, such relations are present among whichever hypotheses are at (...) issue. The present paper offers a new, quasi-empirical argument against scoring-rule monism. This argument uses computational simulations to show that different scoring rules can have different economical consequences, depending on the context of use. (shrink)