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Michael T. Stuart [14]Michael Stuart [2]
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Michael T. Stuart
University of York
  1. How Thought Experiments Increase Understanding.Michael T. Stuart - 2018 - In Michael T. Stuart, Yiftach Fehige & James Robert Brown (eds.), The Routledge Companion to Thought Experiments. London: Routledge. pp. 526-544.
    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 (...)
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  2. Towards a dual process epistemology of imagination.Michael T. Stuart - 2019 - Synthese (2):1-22.
    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 (...)
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  3. The Content-Dependence of Imaginative Resistance.Hanna Kim, Markus Kneer & Michael T. Stuart - 2018 - In Réhault Sébastien & Cova Florian (eds.), Advances in Experimental Philosophy of Aesthetics. Bloomsbury. pp. 143-166.
    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 (...)
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  4. Imagination: A Sine Qua Non of Science.Michael T. Stuart - 2017 - Croatian Journal of Philosophy (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.
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  5. Scientists are Epistemic Consequentialists about Imagination.Michael T. Stuart - forthcoming - Philosophy of Science:1-22.
    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 (...)
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  6. Everyday Scientific Imagination: A Qualitative Study of the Uses, Norms, and Pedagogy of Imagination in Science.Michael Stuart - 2019 - Science & Education 28 (6-7):711-730.
    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. (...)
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  7. Playing the Blame Game with Robots.Markus Kneer & Michael T. Stuart - 2021 - In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (HRI’21 Companion). New York, NY, USA:
    Recent research shows – somewhat astonishingly – that people are willing to ascribe moral blame to AI-driven systems when they cause harm [1]–[4]. In this paper, we explore the moral- psychological underpinnings of these findings. Our hypothesis was that the reason why people ascribe moral blame to AI systems is that they consider them capable of entertaining inculpating mental states (what is called mens rea in the law). To explore this hypothesis, we created a scenario in which an AI system (...)
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  8. Thought Experiments: State of the Art.Michael T. Stuart, Yiftach Fehige & James Robert Brown - 2018 - In Michael T. Stuart, Yiftach Fehige & James Robert Brown (eds.), The Routledge Companion to Thought Experiments. London: Routledge. pp. 1-28.
    This is the introduction to the Routledge Companion to Thought Experiments.
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  9. Peeking Inside the Black Box: A New Kind of Scientific Visualization.Michael T. Stuart & Nancy J. Nersessian - 2018 - Minds and Machines 29 (1):87-107.
    Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...)
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  10. The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.Michael Stuart - 2019 - In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer Verlag.
    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 (...)
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  11. Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent Agents.Michael T. Stuart & Markus Kneer - 2021 - Proceedings of the ACM on Human-Computer Interaction 5 (CSCW2).
    While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for (...)
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  12. The material theory of induction and the epistemology of thought experiments.Michael T. Stuart - 2020 - Studies in History and Philosophy of Science Part A 83 (C):17-27.
    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 (...)
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  13. Philosophical Conceptual Analysis as an Experimental Method.Michael T. Stuart - 2015 - In Thomas Gamerschlag, Doris Gerland, Rainer Osswald & Wiebke Petersen (eds.), Meaning, Frames, and Conceptual Representation. Düsseldorf University Press. pp. 267-292.
    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. 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 (...)
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  14. The future won’t be pretty: The nature and value of ugly, AI-designed experiments.Michael T. Stuart - 2023 - In Milena Ivanova & Alice Murphy (eds.), The Aesthetics of Scientific Experiments. New York, NY: Routledge.
    Can an ugly experiment be a good experiment? Philosophers have identified many beautiful experiments and explored ways in which their beauty might be connected to their epistemic value. In contrast, the present chapter seeks out (and celebrates) ugly experiments. Among the ugliest are those being designed by AI algorithms. Interestingly, in the contexts where such experiments tend to be deployed, low aesthetic value correlates with high epistemic value. In other words, ugly experiments can be good. Given this, we should conclude (...)
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  15. How to tame your Feyerabend. [REVIEW]Michael T. Stuart - 2023 - Metascience 32 (2):173-176.
    This is a book review of Karim Bschir and Jamie Shaw (eds.); Interpreting Feyerabend: critical essays. Cambridge: Cambridge University Press, 2021, 290 pp, $99.99 HB.
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  16. REVIEW: James R. Brown, Laboratory of the Mind. [REVIEW]Michael T. Stuart - 2012 - Spontaneous Generations 6 (1):237-241.
    Originally published in 1991, The Laboratory of the Mind: Thought Experiments in the Natural Sciences, is the first monograph to identify and address some of the many interesting questions that pertain to thought experiments. While the putative aim of the book is to explore the nature of thought experimental evidence, it has another important purpose which concerns the crucial role thought experiments play in Brown’s Platonic master argument.In that argument, Brown argues against naturalism and empiricism (Brown 2012), for mathematical Platonism (...)
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