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No Learning from Minimal Models

Published online by Cambridge University Press:  01 January 2022

Roberto Fumagalli
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Abstract

This article examines the issue of whether consideration of so-called minimal models can prompt learning about real-world targets. Using a widely cited example as a test case, it argues against the increasingly popular view that consideration of minimal models can prompt learning about such targets. The article criticizes influential defenses of this view for failing to explicate by virtue of what properties or features minimal models supposedly prompt learning. It then argues that consideration of minimal models cannot prompt learning about real-world targets unless one supplements these models with additional information or presuppositions concerning such targets.

Type
Models and Measurement
Information
Philosophy of Science , Volume 82 , Issue 5 , December 2015 , pp. 798 - 809
Copyright
Copyright © The Philosophy of Science Association

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