Epistemology

While moral encroachment—the view that the moral features of an agent’s belief can impact its epistemic status—has received a significant amount of attention, core problems with the view remain unaddressed. In this paper, I aim to accomplish three tasks: first, I argue that if moral encroachment simply applies to cases in which agents engage in statistical reasoning, then the thesis is epistemically redundant. Second, I critically examine the possibility of moral encroachment applying to cases in which agents do not engage in statistical reasoning. Lastly, I demonstrate that even if the thesis of moral encroachment is stripped of its epistemic implications, it still can teach us important moral lessons about when our beliefs wrong others.

While the claim that moral ignorance exculpates is quite controversial, the parallel claim with respect to non-moral ignorance seems to be universally accepted. As a starting point, we can state this claim as follows:

Non-moral Ignorance Exculpates: If an agent did everything that could be reasonably expected of her to inquire into some empirical issue as to whether P, the seeming truth of P played the appropriate role in the agent’s motivation to Φ, and the agent would not have merited blame for Φ-ing if P had been the case, then the agent does not merit blame for Φ-ing.

In this paper, I aim to accomplish two tasks. First, I argue that NMIE is false in certain cases in which, by Φ-ing, the agent violates a course-grained, reasonable community norm without knowing that doing so is in everyone’s best interests. Second, I argue that, while moral ignorance, like non-moral ignorance, does not exculpate when community norms are violated in this manner, it does exculpate when they are not. With these two tasks accomplished, we will see the striking parallels in the manner in which both moral and non-moral ignorance exculpate.

In our deliberations about what to do, ought we take for granted the propositions that we know? According to one prominent view, which I’ll call the Fallibilist View, it is rational to be very confident in such propositions when deliberating about what to do, but almost never maximally confident in them. The primary appeal of the Fallibilist View is that it appears to address serious shortcomings of the natural alternative, which I’ll call the Certainty View, according to which it is practically rational to be maximally confident in all known propositions. In this paper, I aim to accomplish two tasks. First, I’ll demonstrate some unappealing features of the Fallibilist View. To follow, I’ll present a third view, which I call the Split View. According to the Split View, there are distinct notions of primary and dispositional rationality, and while it is not dispositionally rational to take known propositions for granted in our practical deliberations, it is primarily rational to do so.

In this paper, I present a puzzle about the connection between an agent’s knowledge and her rationality and a way to solve it. The puzzle is that, intuitively, many of us want to accept both that it is rational for an agent to act on what she knows and that it is irrational for an agent to take what she knows for granting in her practical reasoning. These two claims about rationality present us with a puzzle because, holding fixed our interpretation of rationality, we cannot accept them both. According to my view, the most compelling way of solving this puzzle is to distinguish between our primary and dispositional evaluations of actions. By making this distinction, we not only gain a unique perspective on the relationship between knowledge and rationality, we also see how doing what we know is best might still manifest an undesirable habit.

In this paper, I present a puzzle about how courts react to purely statistical evidence and my own tentative approach to solving it. The basic puzzle is that while there are a number of contexts in which statistical evidence is rejected as a foundation for liability, there are others such as toxic torts in which such evidence is thought to be sufficient. While a number of attempts have been made to explain why statistical evidence is unacceptable in a variety of contexts, significantly fewer have brought to light cases in which such evidence is commonplace. Through an examination of toxic torts, I show that it is untenable to claim that as a general matter, courts will not ground findings of liability in statistical evidence. I then put forward a more nuanced view, according to which findings of liability can be justified when grounded in direct—but not indirect—statistical evidence.

Is it bad practice to use statistical evidence as the basis for a finding of guilt or liability? And if it is bad practice, why is it bad practice? This paper focuses on the incentivizing aspects of candidate legal systems in civil cases. Incentives clearly matter––a legal system which encouraged bad behavior could easily be problematic even if it reliably penalized those who ran afoul of the law and did not penalize those who did not. And the use of statistical evidence does have implications for incentives. One lesson to be drawn from our discussion is that philosophers should not be coming up with stories as to why it is generally bad to use statistical evidence as one’s primary basis for conviction.