My research sits at the intersection of two research programmes: imprecise probability and epistemic utility theory. Imprecise probability theory (IP) generalizes traditional Bayesianism. IP is really a family of frameworks for modeling uncertainty and choice: lower probabilities, lower previsions, sets of desirable gambles, sets of desirable gamble sets, choice functions, probability filters, and more. These frameworks all yield the precise probabilities of traditional Bayesianism as a special limiting case, but make fewer structural assumptions about rational belief and choice.
Epistemic utility theory, on the other hand, aims to provide rigorous, decision-theoretic justifications for norms governing rational belief. It does this in two stages. Firstly, it pins down what is that makes one's beliefs epistemically valuable at a world, and proposes epistemic utility functions that measure this sort of value. Secondly, it uses the machinery of decision theory to show that certain epistemic norms are a better means to the end of epistemic value than others.
Historically, epistemic utility theory focused only on precise probability models. I am currently leading a research project funded by the European Research Council which extends epistemic utility theory to norms governing imprecise probability models.
Before moving to Bristol, I was a lecturer at the University of Kent and an Assistant Professor at Kansas State University. I was also a postdoc on Richard Pettigrew’s ERC project. I received my PhD from the University of Michigan where I worked with Jim Joyce.
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