- 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, and 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 general 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?).
- Semantics: Models and Representation
… 1.1 Representational models I: models of phenomena
… 1.2 Representational models II: models of data
… 1.3 Models of theory
- Ontology: What Are Models?
… 2.1 Physical objects
… 2.2 Fictional objects
… 2.3 Set-theoretic structures
… 2.4 Descriptions
… 2.5 Equations
… 2.6 Gerrymandered ontologies
- Epistemology: Learning with Models
… 3.1 Learning about the model: experiments, thought experiments1 and simulation
… 3.2 Converting knowledge about the model into knowledge about the target
- Models and Theory
… 4.1 The two extremes: the syntactic and the semantic view of theories
… 4.2 Models as independent of theories
- Models and Other Debates in the Philosophy of Science
… 5.1 Models and the realism versus antirealism debate
… 5.2 Model and reductionism
… 5.3 Models and laws of nature
… 5.4 Models and scientific explanation
… Academic Tools
… Other Internet Resources
… Related Entries
Recommended by "Massimi (Michela) - Are Scientific Theories True?". First published Mon Feb 27, 2006; substantive revision Mon Jun 25, 2012. For the full text, see Stanford Archive: Models in Science.
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