Forthcoming. “Sharpening the Tools of Imagination.” Synthese.
This paper argues that in some contexts, thought experiments, models, diagrams, analogies, and metaphors can be understood as tools of imagination. While usually treated separately, a unified epistemological account is possible according to which a given tool of imagination is epistemically good or bad insofar as it improves scientific imaginings. More specifically, this paper argues that acts of imagination in science are epistemologically good, at least according to scientists, when they have epistemologically good consequences. A distinction is drawn between tools being good in retrospect, at the time, and in general. That is, a tool may be evaluated as good retrospectively (given its good consequences), even if it would not have been prescribed as good at the time. A tool can also be good at the cutting edge, in the sense that there is reason to believe it might have good consequences. Finally, a tool may be good in the sense that it improves a scientist’s epistemic virtues, which is good insofar as that has good epistemic consequences.
Forthcoming. “Scientists are Epistemic Consequentialists about Imagination.” Philosophy of Science. DOI: 10.1017/psa.2022.31
Scientists imagine for epistemic reasons, and these imaginings can be better or worse. But what does it mean for an imagining to be epistemically better or worse? There are at least three metaepistemological frameworks that present different answers to this question: epistemological consequentialism, deontic epistemology, and virtue epistemology. This paper presents empirical evidence that scientists adopt each of these different epistemic frameworks with respect to imagination, but argues that the way they do this is best explained if scientists are fundamentally epistemic consequentialists about imagination.
2022. “Understanding Metaphorical Understanding (Literally).” European Journal for Philosophy of Science. DOI: 10.1007/s13194-022-00479-5 (with D. Wilkenfeld).
Metaphors are found all throughout science: in published papers, working hypotheses, policy documents, lecture slides, grant proposals, and press releases. They serve different functions, but perhaps most striking is the way they enable understanding, of a theory, phenomenon, or idea. In this paper, we leverage recent advances on the nature of metaphor and the nature of understanding to explore how they accomplish this feat. We attempt to shift the focus away from the epistemic value of the content of metaphors, to the epistemic value of the metaphor’s consequences. Many famous scientific metaphors are epistemically good, not primarily because of what they say about the world, but because of how they cause us to think. Specifically, metaphors increase understanding either by improving our sets of representations (by making them more minimal or more accurate), or by making it easier for us to encode and process data about complex subjects by changing how we are disposed to conceptualize those subjects. This view hints towards new positions concerning testimonial understanding, factivity, abilities, discovery via metaphor, and the relation between metaphors and models.
2021. “Telling Stories in Science: Feyerabend and Thought Experiments.” HOPOS: The Journal of the International Society for the History of Philosophy of Science 10(2). DOI: 10.1086/712946.
The history of the philosophy of thought experiments has touched on the work of Kuhn, Popper, Duhem, Mach, Lakatos, and other big names of the 20th century, but so far, almost nothing has been written about Paul Feyerabend. His most influential work was Against Method, 8 chapters of which concern a case study of Galileo with a specific focus on Galileo’s thought experiments. In addition, the later Feyerabend was very interested in what might be called the epistemology of drama, including stories and myths. This paper brings these different aspects of Feyerabend’s work together in an attempt to present what might have been his considered views on scientific thought experiments. I conclude by contrasting Feyerabend’s ideas with two modern currents in the debate surrounding thought experiments: 1) the claim that the epistemology of thought experiments is just the epistemology of deductive or inductive arguments, and 2) the claim that the specifically narrative quality of thought experiments must be taken into account if we want a complete epistemology of thought experiments.
2020. “The Productive Anarchy of Scientific Imagination.” Philosophy of Science 87: 968–978. DOI: 10.1086/710629.
Imagination is important for many things in science: solving problems, interpreting data, designing studies, etc. Philosophers of imagination typically account for the productive role played by imagination in science by focusing on how imagination is constrained, e.g., by using self-imposed rules to infer logically, or model events accurately. But the constraints offered by these philosophers either constrain too much, or not enough, and they can never account for uses of imagination that are needed to break today’s constraints in order to make progress tomorrow. Thus, epistemology of imagination needs to make room for an element of epistemological anarchy.
2020. “The Material Theory of Induction and the Epistemology of Thought Experiments.” Studies in History and Philosophy of Science Part A 83: 17–27. DOI: 10.1016/j.shpsa.2020.03.005.
John D. Norton is responsible for a number of influential views in contemporary philosophy of science. This paper will discuss two of them. The material theory of induction claims that inductive arguments are ultimately justified by their material features, not their formal features. Thus, while a deductive argument can be valid irrespective of the content of the propositions that make up the argument, an inductive argument about, say, apples, will be justified (or not) depending on facts about apples. The argument view of thought experiments claims that thought experiments are arguments, and that they function epistemically however arguments do. These two views have generated a great deal of discussion, although there hasn’t been much written about their combination. I argue that despite some interesting harmonies, there is a serious tension between them. I consider several options for easing this tension, before suggesting a set of changes to the argument view that I take to be consistent with Norton’s fundamental philosophical commitments, and which retain what seems intuitively correct about the argument view. These changes require that we move away from a unitary epistemology of thought experiments and towards a more pluralist position.
2019. “Towards a Dual Process Epistemology of Imagination.” Synthese 198:1329–1350. DOI: 10.1007/s11229-019-02116-w.
Sometimes we learn through the use of imagination. The epistemology of imagination asks how this is possible. One barrier to progress on this question has been a lack of agreement on how to characterize imagination; for example, is imagination a mental state, ability, character trait, or cognitive process? This paper argues that we should characterize imagination as a cognitive ability, exercises of which are cognitive processes. Following dual process theories of cognition developed in cognitive science, the set of imaginative processes is then divided into two kinds: one that is unconscious, uncontrolled, and effortless, and another that is conscious, controlled, and effortful. This paper outlines the different epistemological strengths and weaknesses of the two kinds of imaginative process, and argues that a dual process model of imagination helpfully resolves or clarifies issues in the epistemology of imagination and the closely related epistemology of thought experiments.
2019. “Everyday Scientific Imagination: A Qualitative Study of the Uses, Norms, and Pedagogy of Imagination in Science.” Science & Education 28(6), 711-730. DOI: 10.1007/s11191-019-00067-9.
Imagination is necessary for scientific practice, yet there are no in vivo sociological studies on the ways that imagination is taught, thought of, or evaluated by scientists. This article begins to remedy this by presenting the results of a qualitative study performed on two systems biology laboratories. I found that the more advanced a participant was in their scientific career, the more they valued imagination. Further, positive attitudes toward imagination were primarily due to the perceived role of imagination in problem-solving. But not all problem-solving episodes involved clear appeals to imagination, only maximally specific problems did. This pattern is explained by the presence of an implicit norm governing imagination use in the two labs: only use imagination on maximally specific problems, and only when all other available methods have failed. This norm was confirmed by the participants, and I argue that it has epistemological reasons in its favour. I also found that its strength varies inversely with career stage, such that more advanced scientists do (and should) occasionally bring their imaginations to bear on more general problems. A story about scientific pedagogy explains the trend away from (and back to) imagination over the course of a scientific career. Finally, some positive recommendations are given for a more imagination-friendly scientific pedagogy.
2019. “The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.” Pp. 49-66 in M. Addis et al. (eds.) Scientific Discovery in the Social Sciences. Springer: Heidelberg. DOI: 10.1007/978-3-030-23769-1_4.
When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms.
2018. “The Content-Dependence of Imaginative Resistance.” Pp. 143-166 in F. Cova and S. Rénhault (eds.), Advances in Experimental Philosophy of Aesthetics. London: Bloomsbury. (With H. Kim and M. Kneer).
An observation of Hume’s has received a lot of attention over the last decade and a half: Although we can standardly imagine the most implausible scenarios, we encounter resistance when imagining propositions at odds with established moral (or perhaps more generally evaluative) convictions. The literature is ripe with ‘solutions’ to this so-called ‘Puzzle of Imaginative Resistance’. Few, however, question the plausibility of the empirical assumption at the heart of the puzzle. In this paper, we explore empirically whether the difficulty we witness in imagining certain propositions is indeed due to claim type (evaluative v. non-evaluative) or whether it is much rather driven by mundane features of content. Our findings suggest that claim type plays but a marginal role, and that there might hence not be much of a ‘puzzle’ to be solved.
2018. “How Thought Experiments Increase Understanding.” Pp. 526-44 in M. Stuart et al. (eds.), The Routledge Companion to Thought Experiments. London: Routledge.
We might think that thought experiments are at their most powerful or most interesting when they produce new knowledge. This would be a mistake; thought experiments that seek understanding are just as powerful and interesting, and perhaps even more so. A growing number of epistemologists are emphasizing the importance of understanding for epistemology, arguing that it should supplant knowledge as the central notion. In this chapter, I bring the literature on understanding in epistemology to bear on explicating the different ways that thought experiments increase three important kinds of understanding: explanatory, objectual and practical.
2017. “Imagination: A Sine Qua Non of Science.” Croatian Journal of Philosophy Vol. XVII, No. 49: 9-32.
What role does the imagination play in scientific progress? After examining several studies in cognitive science, I argue that one thing the imagination does is help to increase scientific understanding, which is itself indispensable for scientific progress. Then, I sketch a transcendental justification of the role of imagination in this process.
2016. “Norton and the Logic of Thought Experiments.” Axiomathes 26: 451–466. DOI: 10.1007/s10516-016-9306-2.
John D. Norton defends an empiricist epistemology of thought experiments , the central thesis of which is that thought experiments are nothing more than arguments. Philosophers have attempted to provide counterexamples to this claim, but they haven’t convinced Norton. I will point out a more fundamental reason for reformulation that criticizes Norton’s claim that a thought experiment is a good one when its underlying logical form possesses certain desirable properties. I argue that by Norton’s empiricist standards, no thought experiment is ever justified in any deep sense due to the properties of its logical form. Instead, empiricists should consider again the merits of evaluating thought experiments more like laboratory experiments , and less like arguments.
2016. “Taming Theory with Thought Experiments: Understanding and Scientific Progress.” Studies in History and Philosophy of Science Part A 58: 24-33. (This paper was highlighted in Nature Physics here). DOI: 10.1016/j.shpsa.2016.04.002 0039-3681.
I claim that one way thought experiments contribute to scientific progress is by increasing scientific understanding. Understanding does not have a currently accepted characterization in the philosophical literature, but I argue that we already have ways to test for it. For instance, current pedagogical practice often requires that students demonstrate being in either or both of the following two states: 1) Having grasped the meaning of some relevant theory, concept, law or model, 2) Being able to apply that theory, concept, law or model fruitfully to new instances. Three thought experiments are presented which have been important historically in helping us pass these tests, and two others that cause us to fail. Then I use this operationalization of understanding to clarify the relationships between scientific thought experiments , the understanding they produce, and the progress they enable. I conclude that while no specific instance of understanding (thus conceived) is necessary for scientific progress, understanding in general is.
2015. “Philosophical Conceptual Analysis as an Experimental Method.” Pp. 267-292 in Gamerschlag et al. (eds.). Meaning, Frames and Conceptual Representation. Düsseldorf: Düsseldorf University Press.
Philosophical conceptual analysis is an experimental method. Focusing on this helps to justify it from the skepticism of experimental philosophers who follow Weinberg, Nichols & Stich (2001). To explore the experimental aspect of philosophical conceptual analysis, I consider a simpler instance of the same activity: everyday linguistic interpretation. I argue that this, too, is experimental in nature. And in both conceptual analysis and linguistic interpretation, the intuitions considered problematic by experimental philosophers are necessary but epistemically irrelevant. They are like variables introduced into mathematical proofs which drop out before the solution. Or better, they are like the hypotheses that drive science, which do not themselves need to be true. In other words, it does not matter whether or not intuitions are accurate as descriptions of the natural kinds that undergird philosophical concepts; the aims of conceptual analysis can still be met.
2014. “Cognitive Science and Thought Experiments: A Refutation of Paul Thagard’s Skepticism.” Perspectives on Science 22: 98-121. DOI: 10.1162/POSC_a_00130.
Paul Thagard has recently argued that thought experiments are dangerous and misleading when we try to use them as evidence for claims. This paper refutes his skepticism. Building on Thagard’s own work in cognitive science, I suggest that Thagard has much that is positive to say about how thought experiments work. My last section presents some new directions for research on the intersection between thought experiments and cognitive science.
2014. “On the Origins of the Philosophy of Thought Experiments: The Forerun.” Perspectives on Science 22: 13-54 (with Y. Fehige). DOI: 10.1162/POSC_a_00127.
The history of thought experiments is now gaining a great deal of attention, and this is due to the renewed interest of philosophers on the subject. This paper inquires into the history of the philosophy of thought experiments. We name the period to be examined in this paper the “forerun.” Its main stakeholders are Georg C. Lichtenberg, Novalis, and Immanuel Kant. We will present and discuss the work of each of them in order to characterize the period, and then reveal parallels and lessons that apply to more recently proposed accounts of thought experiments.