Abstract
An information-semantic account of models as scientific representations is presented, in which scientific models are considered information carrying artifacts, and the representational semantics of models is based on this information-theoretic relation between the model and the external world. In particular, the semantics of models as scientific representations is argued to be independent of the interpretation or the intentionality of the model builders. The information theoretic view can deal with the problems of asymmetry, circularity and relevance that plague other currently popular naturalistic proposals, and which have been used in the literature as arguments against naturalist accounts.
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Notes
- 1.
Exactly what kinds of things models are has been one of the most debated issues in the literature on scientific models. Following Morrison and Morgan (1999) many divide accounts of models in to two traditions; the abstract and the concrete tradition. The abstract tradition includes, for instance, accounts of models as set theoretical structures (Suppes) or models as trajectories through state space (van Fraassen). The concrete tradition includes the accounts, which take the models to be like imaginary system structures that would be concrete, if they were real. Godfrey-Smith is one recent proponent of this view (Godfrey-Smith 2006). There is yet another sense of “model”, and a different use of models in science: a system that is simple and can be more easily investigated can stand in for a larger class of systems (for example the fruit fly as a model of inheritance and genetic regulation of development, or the mouse as a model of human responses to anti-inflammatory drugs). Our concern in this chapter is strictly with models that are scientific representations constructed in order to inform us about some aspects of nature, for instance the causal structure of a real world system. From that perspective models can be seen as public, man-made artifacts (the term “artefact” is borrowed from Knuuttila 2005). They are not abstract entities (Giere 1988) nor thoughts or other mental representations – unless one considers these also to be manmade artifacts. Models can still be abstract – e.g. mathematical or computational models – or concrete, such as Watson & Crick’s physical scale model of the DNA molecule. The fully abstract (“metalogical”) sense of models as set-theoretic structures satisfying a set of axioms is not included in the target of our analysis. Also, symbolic representation of some purely conceptual (mathematical or computational) structure is not included in our present definition of “model”.
- 2.
The intentionalist thus considers scientific models to be qualitatively different from mental representations, in that scientific representations only have derived intentionality, whereas mental representations are intrinsically intentional. They probably also would typically consider scientific models “external” and mental representations “internal”, although this is more debatable since a scientific model can be conceptual, insofar as these are products of an appropriate process of model construction (cf. footnote 1 and the discussion of the model making process, below).
- 3.
The naturalist is thus characterized by the idea that as far as their semantics are concerned, scientific and mental representations are not qualitatively different, nor is one more fundamental than the other. Specifically, the semantics of a scientific representation is not parasitic upon an established semantics for mental representations. Instead, an unified account could apply to both.
- 4.
In the information semantic account the semantics of models as scientific representations is based on (i) their information-carrying relation between of models and their targets and (ii) on the way this relation is established in the model-making process, rather than the partial isomorphism between the model and the target this model-making process creates as an end result, or the interpretations/mental representations which the model makers or users assign to/associate with it – after the fact, as it were.
- 5.
It seems to us that this requirement is more open to argument than the other three. Consider, for example pictures of Descartes, Hume and Kant in a textbook of philosophy. Are they “really” pictures of the men (they are after all used to give us an impression of what they looked like), or are they “really” just pictures of pictures of the men? This is important, because information carrying is a transitive relation, raising a possible objection to the information semantic account. However, this argument can be countered by invoking the role of the model making process – we shall not pursue this argument here any further.
- 6.
What we mean by this is that B is the intentional object of the modeler’s mental state, and that the reference of A is determined by the identity of B.
- 7.
- 8.
Models are typically abstract (lacking features known to be present in the intended target), idealized (incorporating assumptions that are counterfactual, i.e. known to be false about the intended target), and simplified (representing only a few dependencies from among a multitude). This has led some to ask whether the view of models as representational makes any sense, if models are inaccurate or false descriptions of the world. For instance, insofar as idealization is taken to require the assertion of falsehood (e.g. Jones 2005), idealization makes the models false descriptions. However, it is important to make a distinction between the conditions for A to be a representation of B, and the conditions for A to be an accurate or a true representation of B. After all, A can only be false about B if A is about B (a similar approach can be found for example in Callender & Cohen 2006).
- 9.
This interpretation is based on Frigg’s analysis (Frigg 2006).
- 10.
There is a rich variety of information semantics, but in this paper we focus only on causal theories.
- 11.
Information semantics should not, however, be equated with the causal theory of reference (e.g. Kripke 1980). In causal theories of reference a proper name refers to whatever (token) occasioned the original use of the name. Scientific representations are not proper names, but “universals” describing the type structure of the world. Thus in this account the statistical properties of the information gathering method that fixes the reference of models, not just the causal history of model making.
- 12.
In the information semantic literature this goes under the name of the problem of misrepresentation, which plagued early versions (Dretske 1981, Cummins 1989). It receives a technical solution in Eliasmith (2005) and Usher (2001). The problem is one of defining the informational causal-information coupling in a non-circular way (so that models do not turn out to represent whatever happens to cause them). Marius Usher’s (2001) statistical reference theory is an sophisticated example of those theories, where the problem of misrepresentation is taken seriously. The basic idea of it is that when a representation is tokened, the information it carries is about the class of items it carries the most information about, and not about what caused it in a singular case. Usher offers a neat technical definition that uses the notion of mutual information for dealing this problem. According to Usher A represents B if A carries information about B and for any C that A carries information about, this information is lower than for B. (See Usher 2001 for details).
- 13.
This solution resembles the goldmanian analysis of reliable method of knowledge gathering (Goldman 1986). In the philosophy of mind in Fodor’s (1992) information semantics and Ryder’s (2004) account of mental representation in terms of the mind/brain as a Model Making Mechanisms have similar features.
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Acknowledgements
Many thanks to the member of POS group, and especially to Till Grüne-Yanoff, Tomi Kokkonen, Tarja Knuuttila, Jaakko Kuorikoski, Aki Lehtinen, Caterina Marchionni, Uskali Mäki, Samuli Pöyhönen, Jani Raerinne, Matti Sintonen and Petri Ylikoski for constructive criticism on an earlier draft of this chapter.
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Rusanen, A.M., Lappi, O. (2012). An Information Semantic Account of Scientific Models. In: de Regt, H., Hartmann, S., Okasha, S. (eds) EPSA Philosophy of Science: Amsterdam 2009. The European Philosophy of Science Association Proceedings, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2404-4_27
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