In some way or other, all of my work is about how the formal and the normative make contact. Much of my recent work has focused on how best to understand Bayesian diachronic coherence. Here are abstracts (and some drafts) for some published and unpublished work.
Commutativity, Normativity and Holism: Lange Revisited, Canadian Journal of Philosophy (forthcoming)
Lange (2000) famously argues that although Jeffrey Conditionalization is non-commutative over evidence, it's not defective in virtue of this feature. Since reversing the order of the evidence in a sequence of updates that don't commute does not reverse the order of the experiences that underwrite these revisions, the conditions required to generate commutativity failure at the level of experience will fail to hold in cases where we get commutativity failure at the level of evidence. If our interest in commutativity is, fundamentally, an interest in the order-invariance of information, an updating sequence that does not violate such a principle at the more fundamental level of experiential information should not be deemed defective. This paper claims that Lange's argument fails as a general defense of the Jeffrey framework. Lange's argument entails that the inputs to the Jeffrey framework differ from those of classical Bayesian Conditionalization, in a way that makes them defective. Therefore, either the Jeffrey framework is defective in virtue of not commuting its inputs, or else it is defective in virtue of commuting the wrong kinds of ones.
Higher-Order Beliefs and the Undermining Problem for Bayesianism, Acta Analytica (2019)
Jonathan Weisberg has argued that Bayesianism's rigid updating rules make Bayesian updating incompatible with undermining defeat. In this paper, I argue that when we attend to the higher-order beliefs we must ascribe to agents in the kinds of cases Weisberg considers, the problem he raises disappears. Once we acknowledge the importance of higher-order beliefs to the undermining story, we are led to a different understanding of how these cases arise. And on this different understanding of things, the rigid nature of Bayesianism's updating rules is no obstacle to its accommodating undermining defeat.
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This paper argues for a new account of Bayesian updating by taking what I will call an "Evidence-First" approach to diachronic coherence. This approach says that an agent is diachronically coherent when the information that she updates on satisfies whatever conditions we would want our evidence to satisfy. This approach opposes a common way of thinking about the Bayesian framework, according to which it treats evidence as a black box. The aim of this paper is to provide a different interpretation of Bayesianism's main updating constraint by filling in this black box with a Bayesian account of evidence.
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This paper considers a problem for Bayesian epistemology and goes on to propose a solution to it. On the traditional Bayesian framework, an agent updates her beliefs by Bayesian conditioning, a rule that tells her how to revise her beliefs whenever she gets evidence that she holds with certainty. In order to extend the framework to a wider range of cases, Richard Jeffrey (1965) proposed a more liberal version of this rule that has Bayesian conditioning as a special case. Jeffrey conditioning is a rule that tells the agent how to revise her beliefs whenever she gets evidence that she holds with any degree of confidence. The problem? While Bayesian conditioning has a foundationalist structure, this foundationalism disappears once we move to Jeffrey conditioning. If Bayesian conditioning is a special case of Jeffrey conditioning then they should have the same normative structure. The solution? To reinterpret Bayesian updating as a form of diachronic coherentism.
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Recently some have challenged the idea that there are genuine norms of diachronic rationality. Part of this challenge has involved offering replacements for diachronic principles. Skeptics about diachronic rationality believe that we can provide an error theory for it by appealing to synchronic updating rules that, over time, mimic the behavior of diachronic norms. In this paper, I argue that the most promising attempts to develop this position within the Bayesian framework are unsuccessful. I show that the synchronic updating rules that Meacham (2010) and Hedden (2015a, 2015b) propose as surrogates for the diachronic norm of Conditionalization each fail for different reasons. I conclude by proposing a new synchronic surrogate of Conditionalization that draws upon some of the features of each of these earlier attempts.