Works in progress
- Coherence and Ceteris Paribus Conditions. [Abstract]
- Experimental Explication. [Abstract]
- Competing Explanations and Explaining-Away Arguments. [Abstract]
- Is there a Coherent Concept of Coherence? (with Ulrike Hahn & Adam Harris).
- Simulating IBE (with Ryan Muldoon).
In (Schupbach, 2008), I put forward a “possibility result” for Bayesian Coherentism, showing that there exist intuitively plausible sets of ceteris paribus conditions that imply that coherence is, in an important sense, truth-conducive. Recently, Schubert (forthcoming) has argued against this result, claiming that the ceteris paribus conditions that I put forward are “jointly inconsistent”. In this article, I argue that this criticism is misguided for at least two important reasons. First, Schubert misinterprets the ceteris paribus conditions that I consider; thus, his argument fails to apply to my previous work. Second, Schubert’s attempted proof is fallacious; thus, his argument does not pose a threat even granting his misinterpretation of the conditions in question. I conclude that my earlier possibility result still stands.
Carnap introduces explication as a method for transforming inexact concepts into corresponding exact concepts. To ensure that the latter concept does correspond in an interesting way to the former, Carnap requires that the two be "sufficiently similar." I argue that this requirement on explication provides a natural place for experimental work to inform philosophy, and I give some examples of this philosophical use of experimentation. Then, I show that such experimental explication should not only appeal to the Carnapian but also to the experimental philosopher given that it easily sidesteps some common objections to experimental philosophy.
"Explaining-away arguments" aim to undermine the justification for some hypothesis by appealing to the explanatory power of an alternative hypothesis. In order for such arguments to have their negative force, they must satisfy several conditions. After clarifying these conditions, I focus in on one in particular: the two hypotheses in question offer potential explanations that compete in some sense. To clarify this component of any successful explaining-away argument, I construct a formal (probabilistic) explication of what it is for potential explanations to compete. Although this account neatly clarifies what it typically takes for hypotheses to compete with one another in explaining-away arguments, there are some exceptions to the rule. In light of this work on explanatory competition, I argue that explaining-away arguments are often misapplied today. This is due to the fact that philosophers often fail to appreciate the subtle line dividing competing from non-competing explanations.
Articles
- Is the Conjunction Fallacy tied to Probabilistic Confirmation? [Abstract]
- Comparing Probabilistic Measures of Explanatory Power. [Abstract]
- New Hope for Shogenji's Coherence Measure. [Abstract]
- The Logic of Explanatory Power (with Jan Sprenger). [Abstract]
- On the Alleged Impossibility of Bayesian Coherentism. [Abstract]
- Must the Scientific Realist Be a Rationalist? [Abstract]
- On a Bayesian Analysis of the Virtue of Unification. [Abstract]
- Paley's Inductive Inference to Design. [Abstract]
Crupi et al. (2008) offer a confirmation-theoretic, Bayesian account of the conjunction fallacy —- an error in reasoning that occurs when subjects judge that Pr(h1 & h2|e) > Pr(h1|e). They introduce three formal conditions that are satisfied by classical conjunction fallacy cases, and they show that these same conditions imply that h1 & h2 is confirmed by e to a greater extent than is h1 alone. Consequently, they suggest that people are tracking this confirmation relation when they commit conjunction fallacies. I offer three experiments testing the merits of Crupi et al.’s account specifically and confirmation-theoretic accounts of the conjunction fallacy more generally. The results of Experiment 1 show that, although Crupi et al.’s conditions do seem to be causally linked to the conjunction fallacy, they are not necessary for it; there exist cases that do not meet their three conditions in which subjects still tend to commit the fallacy. The results of Experiments 2 and 3 show that Crupi et al.’s conditions, and those offered by other confirmation-theoretic accounts of the fallacy, are not sufficient for the fallacy either; there exist cases that meet all three of CFT’s conditions in which subjects do not tend to commit the fallacy. Additionally, these latter experiments show that such confirmation-theoretic conditions are at best only weakly causally relevant to the presence of the conjunction fallacy. Given these findings, CFT’s account specifically, and any general confirmation-theoretic account more broadly, falls short of offering a satisfying explanation of the presence of the conjunction fallacy. (Synthese (2012) 184(1): 13-27. Published as part of a special issue on "Probability, Confirmation, and Reasoning Fallacies").
Note: Katya Tentori and Vincenzo Crupi's response to my research on confirmation-theoretic accounts of the conjunction fallacy may be found in the same issue of Synthese.
Note: Katya Tentori and Vincenzo Crupi's response to my research on confirmation-theoretic accounts of the conjunction fallacy may be found in the same issue of Synthese.
Recently, in attempting to account for explanatory reasoning in probabilistic terms, Bayesians have proposed several measures of the degree to which a hypothesis explains a given set of facts. These candidate measures of "explanatory power" are shown to have interesting normative interpretations and consequences. What has not yet been investigated, however, is whether any of these measures are also descriptive of people’s actual explanatory judgments. Here, I present my own experimental work investigating this question. I argue that one measure in particular is an accurate descriptor of explanatory judgments. Then, I discuss some interesting implications of this result for both the epistemology and the psychology of explanatory reasoning. (Philosophy of Science (December 2011) 78(5): 813-829).
I show that the two most devastating objections to Shogenji's formal account of coherence necessarily involve information sets of cardinality n>2. Given this, I surmise that the problem with Shogenji's measure has more to do with his means of generalizing the measure than with the measure itself. I defend this claim by offering an alternative generalization of Shogenji's measure. This alternative retains the intuitive merits of the original measure while avoiding both of the relevant problems that befall it. In the light of all of this, I suggest that there is new hope for Shogenji's analysis: Shogenji's early and influential attempt at measuring coherence, when generalized in a subset-sensitive way, is able to clear its most troubling objections. (British Journal for the Philosophy of Science (2011) 62(1): 125-142).
This article introduces and defends a probabilistic measure of the explanatory power that a particular explanans has over its explanandum. To this end, we propose several intuitive, formal conditions of adequacy for an account of explanatory power. Then, we show that these conditions are uniquely satisfied by one particular probabilistic function. We proceed to strengthen the case for this measure of explanatory power by proving several theorems, all of which show that this measure neatly corresponds to our explanatory intuitions. Finally, we briefly describe some promising future projects inspired by our account. (Philosophy of Science (2011) 78(1): 105-127).
The success of Bovens and Hartmann’s recent "impossibility result" against Bayesian Coherentism relies upon the adoption of a specific set of ceteris paribus conditions. In this paper, I argue that these conditions are not clearly appropriate; certain proposed coherence measures motivate different such conditions and also call for the rejection of at least one of Bovens and Hartmann's conditions. I show that there exist sets of intuitively plausible ceteris paribus conditions that allow one to sidestep the impossibility result. This shifts the debate from the merits of the impossibility result itself to the underlying choice of ceteris paribus conditions. (Philosophical Studies (2008) 141(3): 323-331).
Marc Alspector-Kelly claims that Bas van Fraassen's primary challenge to the scientific realist is for the realist to find a way to justify the use of some mode of inference that takes him from the world of observables to knowledge of the world of unobservables without thereby abandoning empiricism. It is argued that any effort to justify such an "inferential wand" must appeal either to synthetic a priori or synthetic a posteriori knowledge. This disjunction turns into a dilemma for the empirically-minded realist as either disjunct leads to unwanted consequences. In this paper, I split the horns of this dilemma by arguing that the realist can justify one particular such mode of inference -- abduction -- without committing himself to rationalism. The realist may justify this mode of inference by appealing to the analytic a priori axioms of the probability calculus. I show that Peter Lipton's tripartite defense of abduction constitutes such a method of justification. (Synthese (2007) 154(2): 329-334).
In three recent papers, Wayne Myrvold (1996, 2003) and Timothy McGrew (2003) have developed Bayesian accounts of the virtue of unification. In his account, McGrew demonstrates that, ceteris paribus, a hypothesis that unifies its evidence will have a higher posterior probability than a hypothesis that does not. Myrvold, on the other hand, offers a specific measure of unification that can be applied to individual hypotheses. He argues that one must account for this measure in order to calculate correctly the degree of confirmation that a hypothesis receives from its evidence. Using the probability calculus, I prove that the two accounts of unification require the same underlying inequality; thus, McGrew and Myrvold have accounted for unification in fundamentally identical probabilistic terms. I then evaluate five putative counterexamples to this account and show that these examples, far from disqualifying it, serve to clarify our notion of unification by disentangling it from a host of other concepts. (Philosophy of Science (2005) 72(4): 594-607).
In a recent article, Graham Oppy offers a lucid and intriguing examination of William Paley's design argument. Oppy sets two goals for his article. First, he sets out to challenge the "almost universal assumption" that Paley's argument is inductive by revealing it actually to be a deductive argument. Second, he attempts to expose Paley's argument as manifestly poor when interpreted in this way. I will argue that Oppy is unsuccessful in accomplishing his first goal, leaving his second goal quite irrelevant. Contrary to Oppy's interpretation, Paley's argument is best interpreted as an inference to the best explanation. (Philosophia Christi (2005) 7(2): 491-502).
Dissertation
- Studies in the Logic of Explanatory Power. [Abstract]
Human reasoning often involves explanation. In everyday affairs, people reason to hypotheses based on the explanatory power these hypotheses afford; I might, for example, surmise that my toddler has been playing in my office because I judge that this hypothesis delivers a good explanation of the disarranged state of the books on my shelves. But such explanatory reasoning also has relevance far beyond the commonplace. Indeed, explanatory reasoning plays an important role in such varied fields as the sciences, philosophy, theology, medicine, forensics, and law.
This dissertation provides an extended study into the logic of explanatory reasoning via two general questions. First, I approach the question of what exactly we have in mind when we make judgments pertaining to the explanatory power that a hypothesis has over some evidence. This question is important to this study because these are the sorts of judgments that we constantly rely on when we use explanations to reason about the world. Ultimately, I introduce and defend an explication of the concept of explanatory power in the form of a probabilistic measure. This formal explication allows us to articulate precisely some of the various ways in which we might reason explanatorily.
The second question this dissertation examines is whether explanatory reasoning constitutes an epistemically respectable means of gaining knowledge. I defend the following ideas: The probability theory can be used to describe the logic of explanatory reasoning, the normative standard to which such reasoning attains. Explanatory judgments, on the other hand, constitute heuristics that allow us to approximate reasoning in accordance with this logical standard while staying within our human bounds. The most well known model of explanatory reasoning, Inference to the Best Explanation, describes a cogent, nondeductive inference form. And reasoning by Inference to the Best Explanation approximates reasoning directly via the probability theory in the real world. Finally, I respond to some possible objections to my work, and then to some more general, classic criticisms of Inference to the Best Explanation. In the end, this dissertation puts forward a clearer articulation and novel defense of explanatory reasoning. (Defended on June 14, 2011).
This dissertation provides an extended study into the logic of explanatory reasoning via two general questions. First, I approach the question of what exactly we have in mind when we make judgments pertaining to the explanatory power that a hypothesis has over some evidence. This question is important to this study because these are the sorts of judgments that we constantly rely on when we use explanations to reason about the world. Ultimately, I introduce and defend an explication of the concept of explanatory power in the form of a probabilistic measure. This formal explication allows us to articulate precisely some of the various ways in which we might reason explanatorily.
The second question this dissertation examines is whether explanatory reasoning constitutes an epistemically respectable means of gaining knowledge. I defend the following ideas: The probability theory can be used to describe the logic of explanatory reasoning, the normative standard to which such reasoning attains. Explanatory judgments, on the other hand, constitute heuristics that allow us to approximate reasoning in accordance with this logical standard while staying within our human bounds. The most well known model of explanatory reasoning, Inference to the Best Explanation, describes a cogent, nondeductive inference form. And reasoning by Inference to the Best Explanation approximates reasoning directly via the probability theory in the real world. Finally, I respond to some possible objections to my work, and then to some more general, classic criticisms of Inference to the Best Explanation. In the end, this dissertation puts forward a clearer articulation and novel defense of explanatory reasoning. (Defended on June 14, 2011).
Book reviews
- Reflective Knowledge by Ernest Sosa
- Depth by Michael Strevens (reviewed with Stephan Hartmann)
Presentations
- Competing Explanations and Explaining-Away Arguments. [Venues]
- Experimental Explication. [Venues]
- Comparing Probabilistic Measures of Explanatory Power. [Venues]
- The Logic of Explanatory Power (with Jan Sprenger). [Venues]
- Coherence: Philosophical Issues, Psychological Implications (with Adam Harris & Ulrike Hahn). [Venues]
- Confirming the Disconfirmed and Conjunction Fallacies. [Venues]
- How to Be (and How not to Be) a Bayesian Explanationist. [Venues]
- Is the Conjunction Fallacy Tied to Probabilistic Confirmation? [Venues]
- On the Alleged Impossibility of Bayesian Coherentism. [Venues]
7th Annual Philosophy of Religion Conference; Baylor University; Waco, Texas; January 27-28, 2012 (Invited).
1st Pittsburgh-Tilburg Workshop: Formal Epistemology Meets Experimental Philosophy; Tilburg University; Tilburg, the Netherlands; September 29-30, 2011.
Biennial Meeting of the Philosophy of Science Association; Montreal, Quebec, Canada; November 4-6, 2010.
3rd Sydney-Tilburg Conference: The Future of Philosophy of Science; Tilburg University; Tilburg, the Netherlands; April 14-16, 2010.
3rd Sydney-Tilburg Conference: The Future of Philosophy of Science; Tilburg University; Tilburg, the Netherlands; April 14-16, 2010.
The Seventh Annual Formal Epistemology Workshop; Universität Konstanz; Konstanz, Germany; September 2-4, 2010 (schedule).
Progic 2009: 4th Workshop on Combining Probability and Logic; University of Groningen; Groningen, The Netherlands; September 17-19, 2009 (presented by Jan Sprenger).
Progic 2009: 4th Workshop on Combining Probability and Logic; University of Groningen; Groningen, The Netherlands; September 17-19, 2009 (presented by Jan Sprenger).
The Fourth London Reasoning Workshop; Birkbeck, University of London; London, England; July 27-28, 2009 (presented by Adam Harris).
Vereniging voor Analytische Filosofie 2009 (Conference for the Dutch-Flemish Society for Analytic Philosophy); Tilburg University; Tilburg, the Netherlands; January 23, 2009.
Presented to the Seminar on Probability, Decision, and Uncertainty; Institut d'Histoire et de Philosophie des Sciences et des Techniques; Paris, France; January 16, 2009.
The Workshop on the Psychology of Argumentation and Inductive Reasoning; Tilburg University; Tilburg, the Netherlands; November 17-21, 2008.
Presented to the Seminar on Probability, Decision, and Uncertainty; Institut d'Histoire et de Philosophie des Sciences et des Techniques; Paris, France; January 16, 2009.
The Workshop on the Psychology of Argumentation and Inductive Reasoning; Tilburg University; Tilburg, the Netherlands; November 17-21, 2008.
Presented to the Formal Philosophy Seminar; Katholieke Universiteit; Leuven, Belgium; November 28, 2008 (Pictures).
The Second London-Paris-Tilburg Workshop in Logic and Philosophy of Science; Tilburg University; Tilburg, The Netherlands; October 24, 2008 (Invited).
The Second London-Paris-Tilburg Workshop in Logic and Philosophy of Science; Tilburg University; Tilburg, The Netherlands; October 24, 2008 (Invited).
The Workshop on Probability, Confirmation and Fallacies; Katholieke Universiteit; Leuven, Belgium; April 3 - April 7, 2008 (Invited; schedule).
The First Western Michigan University Graduate Philosophy Conference; Kalamazoo, Michigan; November 30 - December 2, 2007 (Invited, Distinguished Alumnus Talk).
The First Western Michigan University Graduate Philosophy Conference; Kalamazoo, Michigan; November 30 - December 2, 2007 (Invited, Distinguished Alumnus Talk).
The Fourth Annual Formal Epistemology Workshop; Carnegie Mellon University; Pittsburgh, Pennsylvania; May 31 - June 3, 2007 (schedule).
Foundations of the Formal Sciences VI: Reasoning about Probabilities and Probabilistic Reasoning; Universiteit van Amsterdam; Amsterdam, The Netherlands; May 2-5, 2007.
Foundations of the Formal Sciences VI: Reasoning about Probabilities and Probabilistic Reasoning; Universiteit van Amsterdam; Amsterdam, The Netherlands; May 2-5, 2007.

I am an Assistant Professor of Philosophy at the University of Utah. To date, I haven't encountered a philosophical topic that I didn't find fascinating. That said, my teaching and research interests have to do with questions of human reasoning. More specifically, I have ongoing research projects in the philosophy of science (particularly on issues pertaining to explanation, confirmation, and scientific method), mainstream epistemology (especially regarding the internalism / externalism debate, nature of understanding, and induction), formal epistemology (particularly inductive logics, explication, and formal accounts of epistemic coherence), and the psychology of human reasoning (including heuristics and biases, fallacies, rational analysis). Lately, I've been thinking a bit about philosophical method too. Click here to see a Wordle word cloud constructed from the abstracts of some of my recent publications.
Teaching
- (* = graduate course)
- S13: Symbolic Logic: Inductive Logics*
- F12: Foundations of Probability and Statistics
- F12: Reasoning and Rational Decision Making
- S12: Symbolic Logic: Deductive Logics*
- S12: Reasoning and Rational Decision Making
- F11: History and Philosophy of Science
- F11: Reasoning and Rational Decision Making
- S10: Myth and Science (Pitt HPS)
Site Design
- Inspired by JC Beall's site design,
- Which was based on Ted Sider's
