Dissertation, (2016)

Authors
James Nguyen
School of Advanced Study, University of London
Abstract
Scientific models are important, if not the sole, units of science. This thesis addresses the following question: in virtue of what do scientific models represent their target systems? In Part i I motivate the question, and lay out some important desiderata that any successful answer must meet. This provides a novel conceptual framework in which to think about the question of scientific representation. I then argue against Callender and Cohen’s attempt to diffuse the question. In Part ii I investigate the ideas that scientific models are ‘similar’, or structurally morphic, to their target systems. I argue that these approaches are misguided, and that at best these relationships concern the accuracy of a pre-existing representational relationship. I also pay particular attention to the sense in which target systems can be appropriately taken to exhibit a ‘structure’, and van Fraassen’s recent argument concerning the pragmatic equivalence between representing phenomena and data. My next target is the idea that models should not be seen as objects in their own right, but rather what look like descriptions of them are actually direct descriptions of target systems, albeit not ones that should be understood literally. I argue that these approaches fail to do justice to the practice of scientific modelling. Finally I turn to the idea that how models represent is grounded, in some sense, in their inferential capacity. I compare this approach to anti-representationalism in the philosophy of language and argue that analogous issues arise in the context of scientific representation. Part iii contains my positive proposal. I provide an account of scientific representation based on Goodman and Elgin’s notion of representation-as. The result is a highly conventional account which is the appropriate level of generality to capture all of its instances, whilst remaining informative about the notion. I illustrate it with reference to the Phillips-Newlyn machine, models of proteins, and the Lotka-Volterra model of predator-prey systems. These examples demonstrate how the account must be understood, and how it sheds light on our understanding of how models are used. I finally demonstrate how the account meets the desiderata laid out at the beginning of the thesis, and outline its implications for further questions from the philosophy of science; not limited to issues surrounding the applicability of mathematics, idealisation, and what it takes for a model to be ‘true’.
Keywords No keywords specified (fix it)
Categories No categories specified
(categorize this paper)
Options
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

PhilArchive copy


Upload a copy of this paper     Check publisher's policy     Papers currently archived: 71,259
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

Philosophical Investigations.Ludwig Josef Johann Wittgenstein - 1953 - New York, NY, USA: Wiley-Blackwell.
How the Laws of Physics Lie.Nancy Cartwright - 1983 - Oxford, England: Oxford University Press.
Laws and Symmetry.Bas C. Van Fraassen - 1989 - Oxford, England: Oxford University Press.

View all 248 references / Add more references

Citations of this work BETA

Models, Information and Meaning.Marc Artiga - 2020 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 82:101284.

Add more citations

Similar books and articles

Models as Make-Believe.Adam Toon - 2010 - In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.
Representation and Similarity: Suárez on Necessary and Sufficient Conditions of Scientific Representation.Michael Poznic - 2016 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 47 (2):331-347.
Scientific Representation.Roman Frigg & James Nguyen - 2016 - Stanford Encyclopedia of Philosophy.
Explaining with Models: The Role of Idealizations.Julie Jebeile & Ashley Graham Kennedy - 2015 - International Studies in the Philosophy of Science 29 (4):383-392.
Theories, Models, and Representations.Mauricio Suárez - 1999 - In L. Magnani, N. J. Nersessian & P. Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. pp. 75--83.

Analytics

Added to PP index
2017-06-05

Total views
39 ( #292,975 of 2,518,488 )

Recent downloads (6 months)
9 ( #79,070 of 2,518,488 )

How can I increase my downloads?

Downloads

My notes