This paper looks at whether it is possible to unify the requirements of rationality with the demands of normative reasons. It might seem impossible to do because one depends upon the agent’s perspective and the other upon features of the situation. Enter Reasons Perspectivism. Reasons perspectivists think they can show that rationality does consist in responding correctly to reasons by placing epistemic constraints on these reasons. They think that if normative reasons are subject to the right epistemic constraints, rational requirements (...) will correspond to the demands generated by normative reasons. While this proposal is prima facie plausible, it cannot ultimately unify reasons and rationality. There is no epistemic constraint that can do what reasons perspectivists would need it to do. Some constraints are too strict. The rest are too slack. This points to a general problem with the reasons-first program. Once we recognize that the agent’s epistemic position helps determine what she should do, we have to reject the idea that the features of the agent’s situation can help determine what we should do. Either rationality crowds out reasons and their demands or the reasons will make unreasonable demands. (shrink)

According to a prominent thought, in one’s practical reasoning one should rely only on what one knows. Yet for many choices, the relevant information is uncertain. This has led Schiffer to the following objection: oftentimes, we are fully rational in reasoning from uncertain premises which we do not know. For example, we may decide to take an umbrella based on a 0.4 credence that it will rain. There are various ways proponents of a knowledge norm for practical reasoning can respond. (...) One option is to say that the right way of dealing with uncertain information requires knowledge of probabilities. Another option is to say that credences can be knowledge because they really are beliefs with an unusual kind of content that consists of a set of probability spaces. Mixed accounts are possible as well. On neither of the accounts in the literature, however, can reasoning from uncertain premises be taken as reasoning based on a graded attitude, a credence, toward an ordinary proposition. To make room for this possibility, I argue that reliance comes in degrees. The knowledge norm is only plausible when taken to be concerned with full reliance.Partial reliance, on the other hand, goes hand in hand with credence. (shrink)

The aim of this paper is to examine whether it would be advantageous to introduce knowledge norms instead of the currently assumed rational credence norms into the debate about decision making under normative uncertainty. There is reason to think that this could help us better accommodate cases in which agents are rationally highly confident in false moral views. I show how Moss’ view of probabilistic knowledge can be fruitfully employed to develop a decision theory that delivers plausible verdicts in these (...) cases. I also argue that, for this new view to be better than existing alternatives, it must adopt a particular solution to the new evil demon problem, which asks whether agents and their BIV-counterparts are equally justified. In order to get an attractive decision theory for cases of moral uncertainty, we must reject the claim that agents and their BIV-counterparts are equally justified. Moreover, the resulting view must be supplemented with a moral epistemology that explains how it is possible to be rationally morally uncertain. This is especially challenging if we assume that moral truths are knowable a priori. (shrink)

This paper explores the consequences of applying two natural ideas from epistemology to decision theory: (1) that knowledge should guide our actions, and (2) that we know a lot of non-trivial things. In particular, we explore the consequences of these ideas as they are applied to standard decision theoretic puzzles such as the St. Petersburg Paradox. In doing so, we develop a “knowledge-first” decision theory and we will see how it can help us avoid fanaticism with regard to the St. (...) Petersburg puzzle and related puzzles. The result will be a decision theory that gives a novel, but well-motivated, reason for discounting small probabilities when making decisions. We examine the merits and demerits of such a decision theory. (shrink)

I will argue for a novel variant of the knowledge norm for practical reasoning. In Sect. 2, I will look at current variations of a knowledge norm for practical reasoning and I will provide reasons to doubt these proposals. In Sects. 3 and 4, I develop my own proposal according to which knowledge is the norm of apt practical reasoning. Section 5 considers objections. Finally, Sect. 6 concerns the normativity of my proposed knowledge norm and its significance.

I discuss the epistemic status of algorithmic predictions in the legal realm. My main claim is that algorithmic predictions do not give us knowledge, not even probabilistic knowledge. The situation, however, is relevantly different from the one in which we find ourselves at the time of assessing statistical evidence in general, and it is rather related to the fact that algorithmic fairness in legal contexts is essentially undetermined. In the light of this, we have to settle for justified beliefs and (...) justified credences. I end by drawing some morals for the Knowledge Norm of action. (shrink)

Sarah Moss’ thesis that we have probabilistic knowledge is from some perspectives unsurprising and from other perspectives hard to make sense of. The thesis is potentially transformative, but not yet elaborated in sufficient detail for epistemologists. This paper interprets Mossean probabilistic knowledge in a suitably-modified Kripke framework, thus filling in key details. It argues that probabilistic knowledge looks natural and plausible when so interpreted, and shows how the most pressing challenges to the thesis can be overcome. Most importantly, probabilistic knowledge (...) can satisfy factivity in the framework, though we are not forced to accept a specific account of probabilistic “facts”. The framework also reflects Moss’ claim that old-fashioned propositional knowledge is just a limiting case of probabilistic knowledge, and all knowledge is fundamentally probabilistic. Finally, Moss endorses a failure of contraposition: for example, p implies probably p, but not probably p does not imply not p. The framework makes clear the sense in which the valid inferences regarding probably p are as Moss claims. (shrink)