Abstract
In the current epistemological debate scientific models are not only considered as useful devices for explaining facts or discovering new entities, laws, and theories, but also rubricated under various new labels: from the classical ones, as abstract entities and idealizations, to the more recent, as fictions, surrogates, credible worlds, missing systems, make-believe, parables, functional, epistemic actions, revealing capacities. An influential article by John Woods, entitled “Against fictionalism” (Woods 2013), usefully provides a rich argumentation concerning the puzzling problems created by the use of the concept of fiction in philosophy, epistemology, and logic, Woods himself further deepened in the recent book Truth in Fiction: Rethinking Its Logic (Woods 2018). By limiting my treatment to the case of models in science, I would like to offer an additional support to this perspective, emphasizing the unsatisfactory character of this intellectual recent trend, and the uselessness of the concept of fiction in illustrating the scientific enterprise. I will contend that it is misleading to analyze models in science by disregarding the dynamic aspects: scientific models in a static perspective (for example when inserted in a textbook) certainly appear fictional to the epistemologist, but their fictional character disappears if a dynamic perspective is adopted. The article also sketches the role of models in science taking advantage of the concept of “epistemic warfare”, which sees scientific enterprise as a complicated struggle for rational knowledge in which it is crucial to distinguish epistemic (for example scientific models) from non epistemic (for example fictions, falsities, propaganda) weapons. A reference to the usefulness of Feyerabend’s counterinduction in criticizing the role of resemblance in model-based cognition is also provided, to further corroborate the thesis indicated by the article title.
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Notes
- 1.
In this perspective I basically agree with the distinction between epistemic and non-epistemic values as limpidly depicted in (Steel 2010).
- 2.
It has to be added that Suárez does not conflate scientific modeling with literary fictionalizing and distinguishes scientific fictions from other kinds of fictions—the scientific ones are constrained by both the logic of inference and, in particular, the requirement to fit in with the empirical domain (Suárez 2009, 2010)—in the framework of an envisaged compatibility of “scientific fiction” with realism. This epistemological acknowledgment is not often present in other stronger followers of fictionalism.
- 3.
- 4.
Of course in the case we are using diagrams to demonstrate already known theorems (for instance in didactic settings), the strategy of manipulations is often already available and the result is not new.
- 5.
On models as epistemic mediators in mathematics cf. (Boumans 2012).
- 6.
A full analysis of the Kölher’s chimpanzee getting hold of a stick to knock a banana hanging out of reach in terms of the mathematical models of the perception and the capture catastrophes is given in (Thom 1988, pp. 62–64). On the role of emotions, for example frustration, in scientific discovery cf. (Thagard 2002).
- 7.
Cartwright (1983), more classically and simply, speaks of “prepared description” of the system in order to make it amenable to mathematical treatment.
- 8.
Further information about the problem of the mapping between models and target systems through interpretation are provided by Contessa (2007, p. 65)—interpretation is seen as more fundamental than surrogative-reasoning: “The model can be used as a generator of hypotheses about the system, hypotheses whose truth or falsity needs to be empirically investigated”. By using the concept of interpretation (analytically and not hermeneutically defined) the author in my opinion also quickly adumbrates the creative aspects in science, that coincide with the fundamental problem of model-based and manipulative abduction (cf. (Magnani 2009, chapters one and two)).
- 9.
On the puzzling relationships between similarity and representations, in the framework of intentionality, cf. (Giere 2007).
- 10.
I endorse many of the considerations by Chakravartty (2010), who stresses the unwelcome division between informational and functional perspective on models and representations in science, which negatively affects the epistemology of scientific modeling.
- 11.
I am convinced that knowledge about concepts such as resemblance, imaginability, conceivability, plausibility, persuasiveness, credit worthiness (Mäki 2009, pp. 39–40) would take advantage of being studied in the framework of the rigorous and interdisciplinary field of abductive cognition (Magnani 2009), which surprisingly is largely disregarded in the studies of the “friends of fiction”, with the exception of Sugden (2000; 2009).
- 12.
We should not forget what Morrison reminds us: “Laws are constantly being revised and rejected; consequently, we can never claim that they are true or false” (Morrison 2009, p. 128).
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Acknowledgements
Parts of this article were excerpted from L. Magnani (2012), Scientific models are not fictions. Model-based science as epistemic warfare, in L. Magnani and Li Ping (eds.) (2012), Philosophy and Cognitive Science. Western and Eastern Studies, Series “Sapere”, Vol. 2, Springer, Heidelberg/Berlin, pp. 1–38, and originally published in chapter two and three of L. Magnani, The Abductive Structure of Scientific Creativity An Essay on the Ecology of Cognition. Copyright (2017), Springer, Cham, Switzerland.
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Magnani, L. (2020). Fictionalists Disregard the Dynamic Nature of Scientific Models. In: Bertolotti, T. (eds) Cognition in 3E: Emergent, Embodied, Extended. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-030-46339-7_5
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