Zeev Goldschmidt

Several philosophers have proposed Knowledge-Based Decision Theories (KDTs)—theories that require agents to maximize expected utility as yielded by utility and probability functions that depend on the agent’s knowledge. Proponents of KDTs argue that such theories are motivated by Knowledge-Reasons norms that require agents to act only on reasons that they know. However, no formal derivation of KDTs from Knowledge-Reasons norms has been suggested, and it is not clear how such norms justify the particular ways in which KDTs relate knowledge and rational action. In this paper, I suggest a new axiomatic method for justifying KDTs and providing them with stronger normative foundations. I argue that such theories may be derived from constraints on the relation between knowledge and preference, and that these constraints may be evaluated relative to intuitions regarding practical reasoning. To demonstrate this, I offer a representation theorem for a KDT proposed by Hawthorne and Stanley (2008) and briefly evaluate it through its underlying axioms.

What action-guiding judgments should we rely on in cases of moral uncertainty, when we divide our credence among competing moral views and assign credences between 0 and 1 to propositions such as “action x is better than action y”? We show that the problem of “moral uncertainty resolution” can be viewed as a “belief binarization” problem: how to arrive at all-out (“accept/reject”) judgments on some propositions based on our credences in them. Looking at moral uncertainty through this lens yields a diagnostically useful impossibility result, which identifies a tradeoff between four initially plausible conditions on a moral uncertainty resolution rule.

This thesis is composed of five essays tackling a range of issues in different areas of decision theory: moral uncertainty, the theory of reasons, and the semantics of taste predicates. In the first two chapters concerning moral uncertainty, I demonstrate how various versions of this problem are instances of broader phenomena addressed by general theories of rationality. Situating the problem of moral uncertainty in these broader contexts enables novel and illuminating applications of the general theories to it. In particular, the first two chapters demonstrate how the theories of belief binarization, and choice under indeterminacy – both theories devised independently of moral uncertainty – may be fruitfully applied to various versions of the morally uncertain agent. The second two chapters explore two different aspects of the theory of reasons – their weight and the notion of acting for the right reasons. In chapter 3, I propose a method for measuring the weight of reasons that draws on the Theory of Measurement (Krantz et al. 1971). I derive a numerical representation of the weight of reasons from comparative judgments of outweighing relations, thus providing a rich structure rooted in intuitive judgments about the notion. In chapter 4, I apply the concept of acting for the right reasons to the case of assertion to argue that the norm of assertion is not epistemic. In doing so I demonstrate how concepts from the theory of reasons may have bearings on first-order issues like the contents of norms. Finally, chapter 5 is about the semantics of taste predicates. I apply the theory of “Clouds of Contexts” developed with other purposes in mind (von Fintel and Gillies 2011) to allow the contextualist to account for linguistic intuitions arising from dialogues about taste. Such dialogues, on the proposed theory, function as explorations of individual and group taste-related attitudes.

Several philosophers have proposed Knowledge-Based Decision Theories (KDTs)—theories that require agents to maximize expected utility as yielded by utility and probability functions that depend on the agent’s knowledge. Proponents of KDTs argue that such theories are motivated by Knowledge-Reasons norms that require agents to act only on reasons that they know. However, no formal derivation of KDTs from Knowledge-Reasons norms has been suggested, and it is not clear how such norms justify the particular ways in which KDTs relate knowledge and rational action. In this paper, I suggest a new axiomatic method for justifying KDTs and providing them with stronger normative foundations. I argue that such theories may be derived from constraints on the relation between knowledge and preference, and that these constraints may be evaluated relative to intuitions regarding practical reasoning. To demonstrate this, I offer a representation theorem for a KDT proposed by Hawthorne and Stanley (2008) and briefly evaluate it through its underlying axioms.

We study the representation of attitudes to risk in Jeffrey’s decision-theoretic framework suggested by Stefánsson and Bradley :602–625, 2015; Br J Philos Sci 70:77–102, 2017) and Bradley :231–248, 2016; Decisions theory with a human face, Cambridge University Press, Cambridge, 2017). We show that on this representation, the value of any prospect may be expressed as a sum of two components, the prospect’s instrumental value and the prospect’s intrinsic value. Both components have an expectational form. We also make a distinction between a prospect’s overall intrinsic value and a prospect’s conditional intrinsic value given each one of its possible outcomes and argue that this distinction has great explanatory power. We explore the relation between these two types of intrinsic values and show that they are determined at the level of preferences. Finally, we explore the relation between the intrinsic values of different prospects and point to a strong restriction on this relation that is implicit in Jeffrey’s axioms. We suggest a natural interpretation to this restriction.