Manuscripts (subject to revision)
Full Belief Updating: a Lockean Approach (2025)
Abstract: -omitted, currently under review, name of paper changed, click pdf button for manuscript.
The Strong Pessimistic Meta-Induction Argument...wrong!, An Application of Formal Learning Theory (2022)
Abstract: Formal Learning Theory is a theory of inductive inference. In slogan form, it says that one ought to do the best they can do in the relevant empirical problem. The accomplishments of Formal Learning Theory are hard to understate: it solves Hume's Problem of Induction, Goodman's New Riddle of Induction, Hempel's Raven Paradox, justifies enumerative induction, recovers the Standard Model of particle physics, justifies Occam's Razor, and supports common assumptions found in causal discovery (which, as aptly recognized by Hume so long ago, is just another inductive problem). This paper adds to Formal Learning Theory's accomplishments by demonstrating how it refutes a version of the Pessimistic Meta-Induction Argument for Strong Anti-Realism, the view that one is rationally required to disbelieve current scientific theories because past theories have been refuted by newly acquired evidence.
Atomism, not Necessarily, But Atoms: Yes, An Application of Formal Learning Theory (2023)
Abstract: Formal Learning Theory is a theory of inductive inference. In slogan form, it says that one ought to do the best they can do in the relevant empirical problem. The accomplishments of Formal Learning Theory are hard to understate: it solves Hume's Problem of Induction, Goodman's New Riddle of Induction, Hempel's Raven Paradox, justifies enumerative induction, recovers the Standard Model of particle physics, justifies Occam's Razor, and supports common assumptions found in causal discovery (which, as aptly recognized by Hume so long ago, is just another inductive problem). This paper adds to Formal Learning Theory's accomplishments by demonstrating how it accounts for declarations of atomhood as found in modern physics despite, and perhaps unfortunately, failing to favor a requirement of us to believe that our world is gunky or a requirement of us to believe that our world is atomy.
Bayesianism, Inference to the Best Explanation, Formal Learning Theory, and its Hegemony: A Justification of IBE (2024)
Abstract: Formal Learning Theory (FLT) and Inference to the Best Explanation (IBE) are both theories of induction. They are about patterns of reasoning that draw conclusions not entailed by the relevant premises. Here, we will understand the drawing of such conclusions as being about what is rationally required to believe given one’s evidence and not about claiming that the inferred conclusion is true or likely to be true. It is worth noting that this makes inductive reasoning unlike deductive reasoning, which admits of a plausible metaphysical understanding: it is the study of truth-preserving arguments. The thesis of this paper is a bold one. We argue for the hegemony of FLT with respect to inductive matters. Specifically, we claim that any adequate detailing of Inference to the Best Explanation is completely covered by the details of Formal Learning Theory with the rest of IBE consisting of heuristic, practical, or stage-setting matters. Furthermore, we detail how the FLT Hegemony Thesis bares many desirable fruits. Now, before getting to our upshots or arguments for FLT Hegemony, we begin by briefly reviewing FLT and IBE and then locating our Hegemony Thesis in the current context of discussion.
All About Actual Accuracy-Dominance (2024)
Abstract: We have lots of good arguments for a variety of epistemic norms on how you should plan to change your credences or beliefs upon coming to possess new evidence. We don't have many good arguments for how you should actually change your credences or beliefs in response to receiving new evidence. Sure, we do have some arguments for actual epistemic norms, but none of them are accuracy-dominance arguments. Here we fill this gap. Doing so requires some conceptual development about good and bad ways to evaluate your epistemic performance. In short: your evidence, while not directly placing constraints on your rational attitudes, places a constraint on how you should evaluate your epistemic performance. If you possess evidence E, it seems, from your point of view, bad to take non-E worlds as relevant to the assessment of your epistemic performance. Using this idea, we develop accuracy-dominance arguments for: Actual Conditionalization, Actual Reverse Conditionalization, Actual Joint Almost Lockean Completeness, Actual Jeffery Conditionalization, and Actual Gallow Conditionalization.
Some Leit Commentary (2025)
Abstract: Leitgeb (2017) proposed that rational all-or-nothing beliefs are stable. In this note, we investigate to what extent we can develop dutchbook and accuracy-dominance arguments for rational belief's stability in a context with just all-or-nothing beliefs simpliciter. We ultimately find such arguments unconvincing.
Elimination Counterexamples: the Qualitative Case (2025)
Abstract: We prove the existence and, at least for additive belief accuracy-measures, inevitability of elimination counterexamples for actual jointly Almost Lockean Complete belief-pairs. After considering and rejecting a variety of interpretations of an "avoid elimination counterexamples condition", we propose an understanding of this condition that is both plausible and preserves accuracy-first epistemology and its accuracy-dominance arguments. Analogous implications for the credal case are suggested.
Deference Done for All-or-Nothing Beliefs (2025)
Abstract: Much admirable work has been done on developing a theory of (epistemic) deference for credences. In contrast, not much work has been done on developing a theory of qualitative deference for all-or-nothing beliefs simpliciter. If you learn that the weatherlady believes that it’s gonna rain tomorrow and you think her an expert worthy of deference, what constraints does this impose on your own beliefs? In this short note, we develop and propose a theory of qualitative deference answering just these kinds of questions. Let’s get to it.