David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
In Ed Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford (2006)
Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point. Scientists spend a great deal of time building, testing, comparing and revising models, and much journal space is dedicated to introducing, applying and interpreting these valuable tools. In short, models are one of the principal instruments of modern science. Philosophers are acknowledging the importance of models with increasing attention and are probing the assorted roles that models play in scientific practice. The result has been an incredible proliferation of model-types in the philosophical literature. Probing models, phenomenological models, computational models, developmental models, explanatory models, impoverished models, testing models, idealized models, theoretical models, scale models, heuristic models, caricature models, didactic models, fantasy models, toy models, imaginary models, mathematical models, substitute models, iconic models, formal models, analogue models and instrumental models are but some of the notions that are used to categorize models. While at first glance this abundance is overwhelming, it can quickly be brought under control by recognizing that these notions pertain to different problems that arise in connection with models. For example, models raise questions in semantics (what is the representational function that models perform?), ontology (what kind of things are models?), epistemology (how do we learn with models?), and, of course, in philosophy of science (how do models relate to theory?; what are the implications of a model based approach to science for the debates over scientific realism, reductionism, explanation and laws of nature?).
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
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
No references found.
Citations of this work BETA
Anouk Barberousse, Sara Franceschelli & Cyrille Imbert (2009). Computer Simulations as Experiments. Synthese 169 (3):557 - 574.
Julian Reiss (2012). The Explanation Paradox. Journal of Economic Methodology 19 (1):43-62.
Similar books and articles
S. Ducheyne (2008). Towards an Ontology of Scientific Models. Metaphysica 9 (1):119-127.
Carl F. Craver (2006). When Mechanistic Models Explain. Synthese 153 (3):355-376.
Ronald N. Giere (1999). Using Models to Represent Reality. In. In L. Magnani, N. J. Nersessian & P. Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. 41--57.
Stephan Hartmann (1995). Models as a Tool for Theory Construction: Some Strategies of Preliminary Physics. In William Herfel et al (ed.), Theories and Models in Scientific Processes. Rodopi.
Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.
Roman Frigg & Stephan Hartmann (2005). Scientific Models. In Sahotra Sarkar et al (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge.
Added to index2010-07-25
Total downloads22 ( #75,143 of 1,096,632 )
Recent downloads (6 months)2 ( #162,598 of 1,096,632 )
How can I increase my downloads?