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Ignorance-Preserving Mental Models Thought Experiments as Abductive Metaphors

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Who can satisfy themselves with mere cognition through experience in all the cosmological questions, of the duration and size of the world, of freedom or natural necessity, since, wherever we may begin, any answer given according to principles of experience always begets a new question which also requires an answer, and for that reason clearly proves the insufficiency of all physical modes of explanation for the satisfaction of reason?

Immanuel Kant. Prolegomena to Any Future Metaphysics. Cambridge University Press, 1997/2004 (p. 103).

Abstract

In this paper, we aim at explaining the relevance of thought experiments (TEs) in philosophy and the history of science by describing them as particular instances of two categories of creative thinking: metaphorical reasoning and abductive cognition. As a result of this definition, we will claim that TEs hold an ignorance-preserving trait that is evidenced in both TEs inferential structure and in the process of scenario creation they presuppose. Elaborating this thesis will allow us to explain the wonder that philosophers of science have consistently shown for TEs, as well as the high functionality of TEs in the creative aspects of scientific and philosophical praxis.

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Notes

  1. The theory of conceptual metaphors, initially presented by Lakoff and Johnson, has been later supported by many authors, including Lakoff (2004) ,Kovecses (2002), Feldman and Narayanan (2003), Casadio (2009), D’Angelosante et al. (2015), Madsen (2016) and Gola and Ervas (2016).

  2. On the history of metaphors, see cf. Ortony (1993).

  3. Initially, philosophers of science began investigating the importance of models, analogies, and metaphors in scientific works in order to grasp the structure and dynamics of scientific reasoning, cf. Black (1955, 1962), Achinstein (1964) and Cartwright (1983).

  4. Of course, the terms that canalize the metaphorical reasoning are not random, but the authors of the metaphors choose them because they structurally fit in the domains connection. The functional adequacy of the metaphorical terms will be further discussed in the next subsection where we will analyze the construction of TEs scenario as a metaphorical construction.

  5. Structure-mapping mechanisms are present in the so-called analogical metaphors, that share the relational and inferential structure of analogical reasoning. As already reported by Gentner (1982), analogical relationships do not support all metaphorical reasonings: metaphors can range from mere relational to attributional comparisons and even elude the definition of domains relationship in terms of alignment. In this article we focus on conceptual analogical metaphors because we claim that this particular type of metaphorical reasoning is at the core of scenario creation of TEs and can shed some lights on their specific functionality.

  6. See Schrödinger (1983) and Wittgenstein (1958, p. 100).

  7. Also Brown (1991) highlighted the reframing power of TEs.

  8. To be clear on this point, we refer to the form of abduction as fallacious just because we discuss its value within a classical deductive framework. That, of course, goes without saying that deduction does not exhaust the range of legitimate inferences.

  9. \(K^*\) is an accessible successor of K to the degree that an agent has the know-how to construct it in a timely way; i.e., in ways that are of service in the attainment of targets linked to K. For example, I want to know how to spell ‘accommodate’, and have forgotten, then my target can’t be hit on the basis of K, what I now know. But I might go to my study and consult the dictionary. This is \(K^*\). It solves a problem originally linked to K.

  10. The description of TEs as normally more comprehensible than the theories they explain or describe still makes a compelling case when considering the “illusion of depth of understanding or IDU” discussed by Ylikosky (2009), which strongly differentiates the sense of understanding from the actual state of understanding. TEs can actually improve the state of understanding in the readers of the theories they refer to because, even if TEs are created through an abductive reasoning, they explain or describe their argument adopting deductive schemas (Arfini 2016). With this overturning, TEs become efficient means to explain more clearly the theories or phenomena they are based upon, with respect to linear argumentations of the theories from which they emerge. As argued by Ylikosky (2009, p. 16) “Explanations are not deductive arguments, but they can be reconstructed as such. The idea is that an (even partial) attempt at deductive reconstruction leads to improvements in the process of articulating explanations by forcing one to explicate both many of the background assumptions and the intended explanandum.” Thus, the point of the publication and discussion of TEs alongside of the relative theories is not only to focus on the particular framework the authors aim at presenting, but also to ease the comprehension for the outsiders of this point of view.

  11. Representing TEs as models that can help us face our ignorance without actually resolve it, does not merely mean that we help ourselves to thought experiments when we do not, for lack of complete knowledge, know better. TEs are conceptual tools that let the agents access levels of understanding that are not available through linear argumentation (that are “tacit”, we could say, borrowing Polanyi’s terminology) and need a visual/narrative scenario to become apparent. The abductive reasoning that forms the inferential pattern of this knowledge-attainment plays an active role in determining both the explanatory force of the designated solution and the ignorance that is preserved in the inferential performance.

References

  • Achinstein, P. (1964). Models, analogies, and theories. Philosophy of Science, 31(4), 328–350.

    Article  Google Scholar 

  • Aliseda, A. (2005). The logic of abduction in the light of Peirce’s pragmatism. Semiotica, 1/4(153):363–374.

    Google Scholar 

  • Arfini, S. (2016). Thought experiments as model-based abductions. In L. Magnani & C. Casadio (Eds.), Model-based reasoning in science and technology. Logical, epistemological, and cognitive issues. Berlin: Springer.

    Google Scholar 

  • Bishop, M. (1999). Why thought experiments are not arguments. Philosophy of Science, 66(4), 534–541.

    Article  Google Scholar 

  • Black, M. (1955). Metaphor. Proceedings of the Aristotelian Society, 55, 273–294.

    Article  Google Scholar 

  • Black, M. (1962). Models and metaphors. Studies in language and philosophy. Ithaca: Cornell University Press.

    Google Scholar 

  • Boyd, R. (1979). Metaphors and theory change. What is “metaphor” a metaphor for? In A. Ortony (Ed.), Metaphors and thought. Cambridge: Cambridge University Press.

    Google Scholar 

  • Brown, J. R. (1991). The laboratory of the mind: Thought experiments in the natural sciences. London: Routledge.

    Google Scholar 

  • Brown, T. (2003). Making truth metaphors in science. Urbana: The Board of Trustees of the University of Illinois.

    Google Scholar 

  • Cartwright, N. (1983). How the laws of physics lie. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Casadio, C. (2009). Effetto “framing”: come inquadriamo il mondo con le metafore. Paradigmi, 1, 55–68.

    Google Scholar 

  • Craik, K. (1943). The nature of explanations. Cambridge: Cambridge University Press.

    Google Scholar 

  • D’Angelosante, V., Tommasi, M., Casadio, C., & Verrotti, A. (2015). Seizure metaphors in children with epilepsy: A study based on a multiple-choice self-report questionnaire. Epilepsy and Behavior, 46, 167–172.

    Article  Google Scholar 

  • Davidson, D. (1978). What metaphors mean. Critical Inquiry, 5(1), 31–47.

    Article  Google Scholar 

  • Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41, 1–63.

    Article  Google Scholar 

  • Feldman, J., & Narayanan, S. (2003). Embodied meaning in a neural theory of language. Brain and Language, 89, 385–392.

    Article  Google Scholar 

  • Firestein, S. (2012). Ignorance: How it drives science. Oxford: Oxford University Press.

    Google Scholar 

  • Gabbay, D. M., & Woods, J. (2005). The reach of abduction: Insight and trial, Volume 1 of A practical logic of cognitive systems. Amsterdam: Elsevier.

    Google Scholar 

  • Gendler, T. S. (1998). Galileo and the indispensability of scientific thought experiment. British Journal for the Philosophy of Science, 49(3), 397–424.

    Article  Google Scholar 

  • Gendler, T. S. (2000). Thought Experiment: On the powers and limits of imaginary cases. New York: Garland Press.

    Google Scholar 

  • Gentner, D. (1982). Are scientific analogies metaphors? In D. S. Miall (Ed.), Metaphor: Problems and perspectives (pp. 106–132). Brighton: Harvester.

    Google Scholar 

  • Gentner, D., & Bowdle, B. (2008). Metaphor as structure-mapping. In R. W. Gibbs (Ed.), The Cambridge handbook of metaphor and thought (pp. 109–128). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52, 45–56.

    Article  Google Scholar 

  • Gola, E. & Ervas, F. (2016). Metaphors we live twice: A communicative approach beyond the conceptual view? In E. G & F. E (Ed.), Metaphor and communication. Amsterdam: John Benjamins

  • Kovecses, A. (2002). Metaphor: A practical Introduction. Oxford: Oxford University Press.

    Google Scholar 

  • Kuhn, T. (1979). Metaphors in science. In A. Ortony (Ed.), Metaphors and Thought. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Lakoff, G. (2004). Don’t think of an elephant! Know your values and frame the debate. Chelsa: Green Publishing.

    Google Scholar 

  • Lakoff, G., & Johnson, M. (1981). Metaphors we live by. Chicago: University Chicago Press.

    Google Scholar 

  • Madsen, M. W. (2016). Cognitive metaphor theory and the metaphysics of immediacy. Cognitive Science, 40, 881–908.

    Article  Google Scholar 

  • Magnani, L. (2009). Abductive Cognition. The epistemological and eco-cognitive dimensions of hypothetical reasoning. Berlin: Springer.

    Google Scholar 

  • Magnani, L. (2013). Is abduction ignorance-preserving? Conventions, models, and fictions in science. Logic Journal of IGPL, 21, 882–914.

    Article  Google Scholar 

  • Magnani, L. (2015). The eco-cognitive model of abduction: Ἀπαγωγή now: Naturalizing the logic of abduction. Journal of Applied Logic, 13, 285–315.

    Article  Google Scholar 

  • Markman, A. B., & Gentner, D. (1993). Structural alignment during similarity comparisons. Cognitive Psychology, 25, 431–467.

    Article  Google Scholar 

  • Nersessian, N. (1992). In the theoretician’s laboratory: Thought experimenting as mental modeling. PSA, 2, 291–301.

    Google Scholar 

  • Norton, J. D. (2004). On thought experiments: Is there more to the argument? Philosophy of Science, 71, 1391151.

    Article  Google Scholar 

  • Ortony, A. (1993). Metaphor and thought. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Peirce, C. S. (1931–1958). Collected papers of Charles Sanders Peirce. Cambridge, MA: Harvard University Press. Vols. 1–6, C. Hartshorne & P. Weiss (Eds.); Vols. 7–8, Burks, A. W. (Ed.).

  • Peirce, C. S. (1992–1998). The Essential Peirce. Selected philosophical writings. Bloomington and Indianapolis: Indiana University Press. Vol. 1 (1867–1893), N. Houser & C. Kloesel (Eds.); Vol. 2 (1893–1913) The Peirce Edition Project (Ed.).

  • Schrödinger, E. (1983). The present situation in quantum mechanics. In J. Wheeler & W. Zurek (Eds.), Quantum Theory and Measurement, page Part I. Translated by J. D. Trimmer: Princeton University Press, New Jersey.

  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

    Article  Google Scholar 

  • Wittgenstein, L. (1958). Philosophical investigations (G. E. M. Anscombe, Trans.). Oxford: Basil Blackwell Ltd.

  • Woods, J. (2009). Ignorance, inference and proof: Abductive logic meets the criminal law. In G. Tuzet & D. Canale (Eds.), The rules of inference: Inferentialism in law and philosophy. Utrecht: Egea.

    Google Scholar 

  • Woods, J. (2013). Errors of reasoning naturalizing the logic of inference (Vol. 45). London: College Publications.

    Google Scholar 

  • Ylikosky, P. (2009). The illusion of depth of understanding in science. In H. D. Regt, S. Leonelli, & K. Eigner (Eds.), Scientific understanding: Philosophical perspectives (pp. 100–119). Pittsburg: University of Pittsburg Press.

    Chapter  Google Scholar 

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Correspondence to Selene Arfini.

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Arfini, S., Casadio, C. & Magnani, L. Ignorance-Preserving Mental Models Thought Experiments as Abductive Metaphors. Found Sci 24, 391–409 (2019). https://doi.org/10.1007/s10699-018-9564-0

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