This paper discusses a novel problem at the intersection of ethics and epistemology: there can be cases in which moral considerations seem to "encroach'' upon belief from multiple directions at once, and possibly to varying degrees, thereby leaving their overall effect on belief unclear. We introduce these cases -- cases of moral encroachment under moral uncertainty -- and show that they pose a problem for all predominant accounts of moral encroachment. We then address the problem, by developing a modular Bayesian (...) framework that, we argue, is sufficiently flexible and scaleable to accommodate the multifaceted uncertainty we describe while still generating clear recommendations for an agent's beliefs. Our framework has several practical upshots, and we close by articulating them: we derive insights about the relationship between moral character and doxastic behavior and make suggestions for how to encourage people to revise their doxastic states in morally laudable ways, without deviating from core Bayesian norms. (shrink)

The threshold view says that a person forms an outright belief P if and only if her credence for P reaches a certain threshold. Using computer simulations, I compare different versions of the threshold view to understand how they perform under time pressure in decision problems. The results illuminate the strengths and weaknesses of the various cognitive strategies in different decision contexts. A threshold view that performs well across diverse contexts is likely to be a cognitively flexible and context-dependent fusion (...) of several of the existing theories. The results of the simulations also cast doubts on the possibility of a threshold view that is both simple enough to streamline our reasoning while also allowing us to form good action-guiding beliefs. (shrink)

Many philosophers have argued that statistical evidence regarding group characteristics (particularly stereotypical ones) can create normative conflicts between the requirements of epistemic rationality and our moral obligations to each other. In a recent article, Johnson-King and Babic argue that such conflicts can usually be avoided: what ordinary morality requires, they argue, epistemic rationality permits. In this article, we show that as data get large, Johnson-King and Babic’s approach becomes less plausible. More constructively, we build on their project and develop a (...) generalized model of reasoning about stereotypes under which one can indeed avoid normative conflicts, even in a big data world, when data contain some noise. In doing so, we also articulate a general approach to rational belief updating for noisy data. (shrink)

We often need to have beliefs about things on which we are not experts. Luckily, we often have access to expert judgements on such topics. But how should we form our beliefs on the basis of expert opinion when experts conflict in their judgments? This is the core of the novice/2-expert problem in social epistemology. A closely related question is important in the context of policy making: how should a policy maker use expert judgments when making policy in domains in (...) which she is not herself an expert? This question is more complex, given the messy and strategic nature of politics. In this paper we argue that the prediction with expert advice framework from machine learning provides helpful tools for addressing these problems. We outline conditions under which we should expert PWEA to be helpful and those under which we should not expect these methods to perform well. (shrink)

Approximate coherentism suggests that imperfectly rational agents should hold approximately coherent credences. This norm is intended as a generalization of ordinary coherence. I argue that it may be unable to play this role by considering its application under learning experiences. While it is unclear how imperfect agents should revise their beliefs, I suggest a plausible route is through Bayesian updating. However, Bayesian updating can take an incoherent agent from relatively more coherent credences to relatively less coherent credences, depending on the (...) data observed. Thus, comparative rationality judgments among incoherent agents are unduly sensitive to luck. (shrink)

In this paper, I provide an account of epistemic anxiety as an emotional response to epistemic risk: the risk of believing in error. The motivation for this account is threefold. First, it makes epistemic anxiety a species of anxiety, thus rendering psychologically respectable a notion that has heretofore been taken seriously only by epistemologists. Second, it illuminates the relationship between anxiety and risk. It is standard in psychology to conceive of anxiety as a response to risk, but psychologists – very (...) reasonably – have little to say about risk itself, as opposed to risk judgement. In this paper, I specify what risk must be like to be the kind of thing to which anxiety can be a response. Third, my account improves on extant accounts of epistemic anxiety in the literature. It is more fleshed out than Jennifer Nagel’s, which is largely agnostic about the nature of epistemic anxiety, focusing instead on what work it does in our epistemic lives. In offering an account of epistemic anxiety as an emotion, my account explains how it is able to do the epistemological work to which Nagel puts it. My account is also more plausible than Juliette Vazard’s, on which epistemic anxiety is an emotional response to potential threat to one’s practical interests. Vazard’s account cannot distinguish epistemic anxiety from anxiety in general, and also fails to capture all instances of what we want to call epistemic anxiety. My account does better on both counts. (shrink)

We are witnessing a certain tendency in epistemology to account for the anti-luck intuition in terms of risk. I.e., instead of the traditional anti-luck diagnosis of Gettier cases and fake barn cases, a new anti-risk diagnosis seems to be preferable by many. My goal in this paper is twofold: first, I contribute to motivate that drift; and second, I defend that we ought to partially resist it. An anti-risk diagnosis is valid and preferable for fake barn cases, but we still (...) need an anti-luck diagnosis for classic Gettier cases. The paper thus defends the Solomon-like result that we need both concepts—epistemic luck and epistemic risk—to deal with all the cases where knowledge is undermined. (shrink)

Bayesian epistemologists support the norms of probabilism and conditionalization using Dutch book and accuracy arguments. These arguments assume that rationality requires agents to maximize practical or epistemic value in every doxastic state, which is evaluated from a subjective point of view (e.g., the agent’s expectancy of value). The accuracy arguments also presuppose that agents are opinionated. The goal of this paper is to discuss the assumptions of these arguments, including the measure of epistemic value. I have designed AI agents based (...) on the Bayesian model and a nonmonotonic framework and tested how they achieve practical and epistemic value in conditions in which an alternative set of assumptions holds. In one of the tested conditions, the nonmonotonic agent, which is not opinionated and fulfills neither probabilism nor conditionalization, outperforms the Bayesian in the measure of epistemic value that I argue for in the paper (α-value). I discuss the consequences of these results for the epistemology of rationality. (shrink)

In this paper the truth is studied in the frame of autonomous systems theory. The method of the truth verification is worked out in its functional aspect. The verification is based on comparison of the predicted inner state of the autonomous agent, that is the cognitive subject, to the achieved inner state of the agent. The state is achieved as the result of performing the action in the real world—the agent’s environment. The action design is created on the basis of (...) the agent’s model of the world. The truth is defined as the adequacy of the model of the real world in the context of the goal that is assumed to be reached as the effect of the performed action. The concepts of the cognitive subject, the truth bearings and the knowledge are redefined. The classical problems of aletheiology and epistemology are discussed in the light of the proposed approach. The cybernetic construct of an autonomous agent allows the researcher to consider a wide class of cognitive entities, which, in the previous approaches, have been limited only to human beings as cognitive subjects. (shrink)

Policymakers who seek to make scientifically informed decisions are constantly confronted by scientific uncertainty and expert disagreement. This thesis asks: how can policymakers rationally respond to expert disagreement and scientific uncertainty? This is a work of non-ideal theory, which applies formal philosophical tools developed by ideal theorists to more realistic cases of policymaking under scientific uncertainty. I start with Bayesian approaches to expert testimony and the problem of expert disagreement, arguing that two popular approaches— supra-Bayesianism and the standard model of (...) expert deference—are insufficient. I develop a novel model of expert deference and show how it can deal with many of these problems raised for them. I then turn to opinion pooling, a popular method for dealing with disagreement. I show that various theoretical motivations for pooling functions are irrelevant to realistic policymaking cases. This leads to a cautious recommendation of linear pooling. However, I then show that any pooling method relies on value judgements, that are hidden in the selection of the scoring rule. My focus then narrows to a more specific case of scientific uncertainty: multiple models of the same system. I introduce a particular case study involving hurricane models developed to support insurance decision-making. I recapitulate my analysis of opinion pooling in the context of model ensembles, confirming that my hesitations apply. This motivates a shift of perspective, to viewing the problem as a decision theoretic one. I rework a recently developed ambiguity theory, called the confidence approach, to take input from model ensembles. I show how it facilitates the resolution of the policymaker’s problem in a way that avoids the issues encountered in previous chapters. This concludes my main study of the problem of expert disagreement. In the final chapter, I turn to methodological reflection. I argue that philosophers who employ the mathematical methods of the prior chapters are modelling. Employing results from the philosophy of scientific models, I develop the theory of normative modelling. I argue that it has important methodological conclusions for the practice of formal epistemology, ruling out popular moves such as searching for counterexamples. (shrink)