Abstract
In this paper I propose an account of representation for scientific models based on Kendall Walton’s “make-believe” theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyze scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modeling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object.
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Notes
- 1.
Note that I use the term “theoretical” only to indicate that scientists do not construct a physical model of the system modeled, and not to imply that the model is derived from some existing theory, like Newtonian mechanics. Recent case studies suggest scientists must often go beyond existing theory to model a system; for example, see Morgan and Morrison (1999).
- 2.
For my own view of the ontology of theoretical modeling, see Toon (2010).
- 3.
Salisbury Cathedral from the Meadows on http://www.nationalgallery.org.uk
- 4.
Versions of this point may be found in Frigg (2006), Hughes (1997) and Suárez (2003), although each draw rather different lessons from it.
- 5.
- 6.
This problem is also raised by Suárez (2003) and Callender and Cohen (2006). As we shall see in section “Models Without Actual Objects”, however, neither provide a solution.
- 7.
- 8.
Of course, this is not to claim that realism in modeling is the same as realism in painting.
- 9.
Of course, in certain cases such objects may be representational. A chair might be used in a work of abstract art, for example, or a table used to represent a shelter in a play.
- 10.
These central features of the account are introduced in Section 1.5 of Walton (1990).
- 11.
The suggestion that Walton’s theory may be applied in the context of scientific modeling is also found in Barberousse (2006), Barberousse and Ludwig (2000) and Frigg (2010). See below for a discussion of Frigg’s views.
- 12.
For more on the ontology of theoretical modeling, see Toon (2010).
- 13.
- 14.
Note also that the position I advocate is distinct from what Arthur Fine (1998) calls fictionalism. As Fine characterizes it, fictionalism is an anti-realist position which argues that a scientific theory may be reliable without being true and without the entities it invokes existing. To classify a model as a representation in Walton’s sense is to say nothing about the truth of the propositions the model prescribes or about the existence of the entities it invokes.
- 15.
The terms “accommodationist” and “eliminativist” are taken from Lamarque (2003).
- 16.
This example reminds us that the same prepared description and equation of motion may serve very different representational functions.
- 17.
Similarly, Frigg (2010) suggests that the problem of models without actual objects can be avoided simply by adopting his distinction between p-representation and t-representation. This alone does not seem sufficient to solve the problem, however: we still require an account of t-representation that can explain how some model systems (like the simplified ether model system) can be representational, without representing any actual object.
- 18.
- 19.
“D.D.I.” stands for “denotation, demonstration, and interpretation”. According to Hughes, these combine in the following way: elements of the physical world are denoted by elements of the model; the model possesses an internal dynamic that allows us to demonstrate theoretical conclusions; these in turn need to be interpreted if we are to make predictions (Hughes 1997, S325).
- 20.
Callender and Cohen also attempt to defer the problem posed by models without actual objects, observing that “the worry arises for all species of representation—not just scientific representation—and there is no reason to suspect that whatever ultimately explains representations of unicorns and golden mountains won’t work for representation of phlogiston and the ether” (2006, 81). There is an important difference between Callender and Cohen’s deferral strategy and my own, however. Callender and Cohen simply express a hope that a solution to the problem for other forms of representation may be applied to scientific models. They tentatively suggest a “Humean strategy”, which provides a relational theory for “atomic” representations and explains representations without actual objects as constructed as “compounds” of other representations. But they do not show whether this can be applied to scientific models, nor whether their account would remain intact if it were. This amounts simply to deferring the problem for scientific representation itself. By contrast, my own account reduces the problem of understanding models without actual objects to the more general problem of understanding imaginings about fictional entities.
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Acknowledgments
This paper is based on a talk delivered at the “Beyond Mimesis and Nominalism: Representation in Art and Science” conference held at London School of Economics and the Courtauld Institute of Art in June 2006. Parts of the paper were also presented at the Philosophy Workshop in Cambridge in June 2006, the CMM Graduate Conference held in Leeds in June 2007, and the “Scientific Models: Semantics and Ontology” workshop, held in Barcelona in July 2007. I would like to thank participants at all of these events. Thanks also to Nancy Cartwright, Stacie Friend, Manuel Garcia-Carpintero, Ronald Giere, Mary Leng, Mauricio Suárez, Paul Teller, Martin Thomson-Jones and Kendall Walton for helpful discussion and correspondence, and to Roman Frigg and two anonymous referees for comments on drafts of this paper. Finally, I would like to thank my PhD supervisor Martin Kusch, and my advisor, the late Peter Lipton. Research for this paper was supported by The Arts and Humanties Research Council, The Darwin Trust of Edinburgh and The Rausing Fund for History and Philosophy of Science. I am very grateful to all of these institutions for their support.
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Toon, A. (2010). Models as Make-Believe. In: Frigg, R., Hunter, M. (eds) Beyond Mimesis and Convention. Boston Studies in the Philosophy of Science, vol 262. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3851-7_5
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