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151Finding missing proofs with automated reasoningStudia Logica 68 (3): 329-356. 2001.This article features long-sought proofs with intriguing properties (such as the absence of double negation and the avoidance of lemmas that appeared to be indispensable), and it features the automated methods for finding them. The theorems of concern are taken from various areas of logic that include two-valued sentential (or propositional) calculus and infinite-valued sentential calculus. Many of the proofs (in effect) answer questions that had remained open for decades, questions focusing on …Read more
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59We’ll adopt a simple framework today. Our assumptions: A model (M) is a family of hypotheses. A hypothesis (H) is a curve plus an associated error term . For simplicity, we’ll assume a common N (0, 1) Gaussian
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112A Bayesian Account of Independent Evidence with ApplicationsPhilosophy of Science 68 (S3). 2001.A Bayesian account of independent evidential support is outlined. This account is partly inspired by the work of C. S. Peirce. I show that a large class of quantitative Bayesian measures of confirmation satisfy some basic desiderata suggested by Peirce for adequate accounts of independent evidence. I argue that, by considering further natural constraints on a probabilistic account of independent evidence, all but a very small class of Bayesian measures of confirmation can be ruled out. In closin…Read more
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142Introduction to the Special Issue: Probability, Confirmation and FallaciesSynthese 184 (1): 1-1. 2012.
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173Let Ln be a sentential language with n atomic sentences {A1, . . . , An}. Let Sn = {s1, . . . , s2n} be the set of 2n state descriptions of Ln, in the following, canonical lexicographical truth-table order: State Description A1 A2 · · · An−1 An T T T T T s1 = A1 & A2 & · · · &An−1 & An T T T T F s1 = A1 & A2 & · · · &An−1 & ¬An T T T F T s3 = A1 & A2 & · · · & ¬An−1 & An T T T F F s4 = A1 & A2 & · · · & ¬An−1 & ¬An..
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72Remarks on "Random Sequences"Australasian Journal of Logic 12 (1). 2015.We show that standard statistical tests for randomness of finite sequences are language-dependent in an inductively pernicious way.
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177Contrastive BayesianismIn Martijn Blaauw (ed.), Contrastivism in philosophy, Routledge/taylor & Francis Group. 2013.Bayesianism provides a rich theoretical framework, which lends itself rather naturally to the explication of various “contrastive” and “non-contrastive” concepts. In this (brief) discussion, I will focus on issues involving “contrastivism”, as they arise in some of the recent philosophy of science, epistemology, and cognitive science literature surrounding Bayesian confirmation theory
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327Probabilistic measures of causal strengthIn Phyllis McKay Illari Federica Russo (ed.), Causality in the Sciences, Oxford University Press. pp. 600--627. 2011.
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616What is the “Equal Weight View'?Episteme 6 (3): 280-293. 2009.In this paper, we investigate various possible (Bayesian) precisifications of the (somewhat vague) statements of “the equal weight view” (EWV) that have appeared in the recent literature on disagreement. We will show that the renditions of (EWV) that immediately suggest themselves are untenable from a Bayesian point of view. In the end, we will propose some tenable (but not necessarily desirable) interpretations of (EWV). Our aim here will not be to defend any particular Bayesian precisification…Read more
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63The principle that every truth is possibly necessary can now be shown to entail that every truth is necessary by a chain of elementary inferences in a perspicuous notation unavailable to Hegel. —Williamson [5, p.
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91Suppose we have two false hypotheses H1 and H2. Sometimes, we would like to be able to say that H1 is closer to the truth than H2 (e.g., Newton’s hypothesis vs. Ptolemy’s).
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49E confirmsi H1 more strongly than E confirmsi H2 iff c(H1, E) > c(H2, E). [where c is some relevance measure]
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Areas of Specialization
| Metaphysics and Epistemology |
| Science, Logic, and Mathematics |
| Formal Epistemology |