Abstract
Fiora Salis compares the fictional and the artifactual views of models. She argues that both accounts contain several deep insights concerning the nature of scientific models but they also face some difficult challenges. She then puts forward an account of the ontology of models intended to incorporate the benefits of both views avoiding their main difficulties. Her key idea is that models are human-made artifacts that are akin to literary works of fiction. In this view, models are complex objects that are constituted by a model description and the model content generated within a game of make-believe. As per the fiction view, model descriptions are construed as props in a game of make-believe, where props are concrete objects that prescribe certain imaginings. As per the artifactual view, model descriptions are construed as concrete representational tools that enable and constrain a scientist’s cognitive processes and provide intersubjective epistemic access to their imaginings.
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Notes
- 1.
See the contrast he explicitly draws between his approach and the psychological account of model-based reasoning advanced by Nersessian (1999).
- 2.
See Weisberg (2007) for a similar idea that does not appeal to the analogy with fiction.
- 3.
See Salis and Frigg (2020) for a thorough analysis of make-believe in the context of the scientific imagination.
- 4.
Vorms (2011) emphasizes the crucial role of the format of a representational tool for the inferences that can be drawn from it.
- 5.
See Salis (2019) for a similar development of the analogy between models and literary works of fiction that does not integrate the original insight of the artifactual view of models.
- 6.
See Salis (2013a) for a review of these controversies.
- 7.
See Salis (2019) for a thorough criticism of the direct fiction view of models that identifies further specific problems.
- 8.
According to Frigg and Nguyen, these two steps correspond to two parts of the model description D, namely DX (where ‘X’ stands in for the vehicle X) and DI (where ‘I’ stands for the interpretation that transforms X into a Z-representation). They notice, however, that DX and DI are not always separated as in Newton’s model. In some cases, the imaginary system has the relevant properties and the interpretation is one of simple identity. For example, in Fibonacci’s model of population growth DX specifies a rabbit population and the model is a rabbit-population-representation.
- 9.
An anonymous referee further notices that \({M}_{T}=\langle \_, I\rangle\) looks like a structure without domain, which is not a structure at all.
- 10.
An anonymous referee notices that this distinction holds only if one upholds an indirect view of representation. As mentioned above, the direct view of representation does not fit well with the face-value practice of modeling. In particular, it cannot account for the indirect strategy of model-based science identified by Godfrey-Smith (2006) and for the typical surrogative reasoning that is enabled by models. Knuuttila (2017) explicitly recognizes the latter point as especially problematic.
- 11.
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Salis, F. (2021). Bridging the Gap: The Artifactual View Meets the Fiction View of Models. In: Cassini, A., Redmond, J. (eds) Models and Idealizations in Science. Logic, Epistemology, and the Unity of Science, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-030-65802-1_7
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