TRUTH AND REPRESENTATION IN SCIENCE: TWO INSPIRATIONS FROM ART Beyond Mimesis and Nominalism: Representation in Art and Science

Realists regarding scientific knowledge – those who think that our best scientific representations truly describe both observable and unobservable aspects of the natural world – have special need of a notion of approximate truth. Since theories and models are rarely considered true simpliciter, the realist requires some means of making sense of the claim that they may be false and yet close to the truth, and increasingly so over time. In this paper, I suggest that traditional approaches to approximate truth are insensitive to two crucial features of scientific knowledge, and that for each of these, analogies between representational practices in the sciences and in art prove useful to understanding how this situation can be remedied. First, I outline two distinct ways in which representations deviate from the truth, commonly referred to as ‘abstraction’ and ‘idealization’. Second, I argue that these practices exemplify different conventions of representation, and that for each, the conditions of approximation relevant to explicating the concept of approximate truth must be understood differently. The concept is thus heterogeneous; approximate truth is a virtue that is multiply realized, relative to different contexts of representation. This understanding is facilitated, I suggest, by considering the distinction between realistic and non-realistic representation in art. 1. Varieties of truth in art and science Not so long ago, pursuing the notion that the philosophies of art and science can inform one another in mutually productive ways might have been considered a cultured but rather fringe activity. Recently, however, philosophers more generally have awoken to the import of provocative and substantive analogies between practices of representation in these fields, and it is the spirit of this pursuit that motivates this essay. My primary concern here is to understand the nature of truth in the scientific context, and it will be my contention that this understanding, far from being a simple matter of mastering the T-schema, requires an appreciation of the distinction between two different conventions of representation – one associated with practices of abstraction, and the other with practices of idealization. It is here, I believe, that analogies to practices of representation in art can serve as valuable heuristics towards understanding how and in what manner scientific representations can be true. Truth and Representation in Science: Two Inspirations from Art Anjan Chakravartty 2 The term ‘scientific representation’ is commonly applied to many things, and would benefit from a more precise consideration than I can give it here. For present purposes, let me simply take such representations to include the usual items traditionally held to have representational status in the sciences, viz. theories and models, however these things are best defined, and constituting the ontological categories commonly associated with them: linguistic and mathematical entities, computer simulations, concrete objects, and so on. Other key concepts here will of course include those of abstraction and idealization, and I will have something to say about each in turn. Let me begin, however, with the central concept whose explication this essay is intended to serve. Clearly, not all philosophers of science believe that the sciences are in the truth business, but an impressive diversity do, including different kinds of realists and empiricists. The former take the truths of science to include facts about unobservable entities and processes, and some of the latter acknowledge only truths about the observable, but all believe that scientific knowledge involves or at the very least aspires to substantive truths about the world, in some form or other. This is the first of two assumptions I will make here, at the outset. The second is that descriptions of entities and processes afforded by scientific representations are generally false, strictly speaking. I will not argue for this here, but neither do I take it to be controversial. Even realists and empiricists who think that the sciences are in the truth business will readily admit the hyperbole involved in suggesting that current representations (however circumscribed) are generally, perfectly and comprehensively true. The history of the sciences has made a mockery of that suggestion in the past, and no doubt there is further mockery to come. It seems that anyone who endorses the idea of scientific truth as a reasonable aspiration requires some means of making sense of the idea that inaccurate representations can be close to the truth, and perhaps even get better with respect to truth over time. In the literature this requirement has motivated several accounts of “approximate truth”, in terms of which, it is argued, one may understand such improvements. There would seem to be a widely held intuitive platitude concerning the notion of approximate truth, and Stathis Psillos (1999, p. 277) summarizes it well: ‘A description D...is approximately true of [a state] S if there is another state S′ such that S and S′ are linked by specific conditions of approximation, and D...is true of S′.’ As it stands, however, the Truth and Representation in Science: Two Inspirations from Art Anjan Chakravartty 3 helpfulness of this statement is greatly impaired by the vagueness of the phrase ‘conditions of approximation’. In essence, the remainder of this essay is an attempt to clarify this phrase. I believe that the clarification required is wonderfully illuminated by drawing analogies to certain practices of representation in art, and as a final foreshadowing remark, let me mention briefly the pathbreaking work in this area that informs several of the thoughts to follow. Nelson Goodman (1976) is celebrated for presenting a detailed analysis of the “symbol systems” in terms of which different forms of art express their content. At the end of his book on the subject, Goodman (p. 262) says something particularly striking about the comparison between representations in art and in science: ...have I overlooked the sharpest contrast: that in science, unlike art, the ultimate test is truth? Do not the two domains differ most drastically in that truth means all for the one, nothing for the other? ... Despite rife doctrine, truth by itself matters very little in science. It should be stated immediately that Goodman does not of course think that truth is unimportant in the sciences. Important truths about the natural world are indeed of great interest to scientists, and while one may admit that scientific laws are seldom true as they stand, we have an interest in ‘arriving at the nearest approximation to truth that is compatible with our other interests’ (1976, p. 263). Ultimately, says Goodman, truth can be understood in terms of ‘a matter of fit’ between theories and facts, and as it turns out, just this sort of “fitting” is characteristic of the relationship between art and the world. Truth in both domains should be understood in terms of approximating reality by means of representations. The reason Goodman suggests that truth by itself matters little, is that truth amounts to nothing unless one, in addition to having truthful representations, is properly acculturated with the conventions of representation in terms of which they express their content. It is precisely these conventions that I take to constitute the ‘conditions of approximation’ whose explication is required in order to make sense of the notion of approximate truth, and so by exploring the former, I aim to shed light on the latter. In the Truth and Representation in Science: Two Inspirations from Art Anjan Chakravartty 4 following I will suggest that understanding two central features of scientific knowledge are crucial to illuminating these conditions of approximation, and it is here that analogies to representation in art may prove useful. The first of these features is the distinction between abstraction and idealization in connection with scientific representation, and the second concerns the nature and pragmatics of scientific practice. Let us consider these features in turn. 2. Preliminaries on approximate truth A moment ago I suggested that generally speaking, knowledge contained within scientific representations is usually understood as approximately true at best. Three main families of accounts of approximate truth have emerged in the literature since the 1960s, and before considering the nature and relevance of abstraction and idealization in this context, it will serve us to have a synoptic overview of each of these approaches. I will refer to them respectively as the verisimilitude approach, due to Karl Popper, the possible worlds approach, formulated in different ways by authors including Pavel Tichý, Ilkka Niiniluoto, and Graham Oddie, and the type hierarchy approach, offered by Jerrold Aronson, Rom Harré, and Eileen Cornell Way. Popper was the first to give a definition of what he called ‘verisimilitude’ or ‘truth-likeness’. On his (1972, pp. 231-236) view, scientific theories within a domain may exhibit increasing levels of verisimilitude over time, and this relative ordering can be expressed as follows. Consider a temporallyordered sequence of theories concerning the same subject matter: T1, T2, T3, ... . Now, for each of these theories, consider the set of all of its true consequences (for example, T1) and the set of all of its false consequences (T1). A comparative ranking of the verisimilitude of any two theories can be given, suggests Popper, by comparing their true and false consequences. For any theory Tn, and any previous theory Tconcept of approximate truth is thus heterogeneous, to be explicated as may be appropriate in particularcases, within the myriad contexts of representation to which it may be applied.It is thus the conclusion of this paper that in the sciences, approximate truth is best understood asa virtue that is multiply realized by means of different kinds of representational relationships betweenscientific products such as theories and models on the one hand, and target systems in the world on theother. These different conventions of representation reflect the degrees to which theories and modelsabstract and idealize, and as a consequence, anyone hoping to understand the ways in which theyapproximate truth must take these conventions into serious consideration. In this and perhaps other ways,those interested in the nature of scientific knowledge have things to learn from the nature ofrepresentation in art. ReferencesAronson, J. L. 1990: ‘Verisimilitude and Type Hierarchies’, Philosophical Topics 18: 5-28.Aronson, J. L., R. Harré, & E. C. Way 1994: Realism Rescued: How Scientific Progress is Possible.London: Duckworth.Chakravartty, A. 2007: A Metaphysics for Scientific Realism: Knowing the Unobservable. Cambridge:Cambridge University Press.Goodman, N. 1976: Languages of Art: An Approach to a Theory of Symbols, 2 edition. Indianapolis:Hackett.Greenberg, C. (2003/1939): ‘Avant-Garde and Kitsch’, in C. Harrison & P. Wood (eds.), Art in Theory,1900-2000: An Anthology of Changing Ideas. Oxford: Blackwell.Hacking, I. 1983: Representing and Intervening. Cambridge: Cambridge University Press. 7 For a more leisurely route to this conclusion, see Chakravartty 2007, Part III. Truth and Representation in Science: Two Inspirations from Art Anjan Chakravartty22 Jones, M. R. 2005: ‘Idealization and Abstraction: A Framework’, in M. R. Jones & N. Cartwright (eds.), Idealization XII: Correcting the Model, Poznań Studies in the Philosophy of the Sciences and theHumanities 86: 173-217. McMullin, E. 1985: ‘Galilean Idealization’, Studies in History and Philosophy of Science 16: 247-273.Miller, D. 1974: ‘Popper’s Qualitative Theory of Verisimilitude’, British Journal for the Philosophy ofScience 25: 166-177.Miller 1976: ‘Verisimilitude Redeflated’, British Journal for the Philosophy of Science 27: 363-380.Niiniluoto, I. 1984: Is Science Progressive? Dordrecht: D. Reidel.Niiniluoto, I. 1987: Truthlikeness. Dordrecht: D. Reidel.Niiniluoto, I. 1998: ‘Verisimilitude: The Third Period’, British Journal for the Philosophy of Science 49:1-29.Niiniluoto, I. 1999: Critical Scientific Realism. Oxford: Clarendon.Oddie, G. 1986a: ‘The Poverty of the Popperian Program for Truthlikeness’, Philosophy of Science 53:163-178.Oddie, G. 1986b: Likeness to Truth. Dordrecht: D. Reidel.Oddie, G. 1990: ‘Verisimilitude by Power Relations’, British Journal for the Philosophy of Science 41:129-135.Popper, K. R. 1972: Conjectures and Refutations: The Growth of Knowledge, 4 edition. London:Routledge & Kegan Paul.Psillos, S. 1999: Scientific Realism: How Science Tracks Truth. London: Routledge.Suárez, M. 2003: ‘Scientific Representation: Against Similarity and Isomorphism’, International Studiesin the Philosophy of Science 17: 225-244.Tichý, P. 1974: ‘On Popper’s Definitions of Verisimilitude’, British Journal for the Philosophy ofScience 25: 155-160.Tichý, P. 1976: ‘Verisimilitude Redefined’, British Journal for the Philosophy of Science 27: 25-42.Tichý, P. 1978: ‘Verisimilitude Revisited’, Synthese 38: 175-196.