Minimal Model Explanations

Philosophy of Science 81 (3):349-376 (2014)
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Abstract

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems.

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Author Profiles

Collin Rice
Colorado State University
Robert W. Batterman
University of Pittsburgh

Citations of this work

Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
Understanding (with) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2018 - British Journal for the Philosophy of Science 69 (4):1069-1099.
Explanatory Abstractions.Lina Jansson & Juha Saatsi - 2019 - British Journal for the Philosophy of Science 70 (3):817–844.
Minimal phenomenal experience.Thomas Metzinger - 2020 - Philosophy and the Mind Sciences 1 (I):1-44.

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References found in this work

How the laws of physics lie.Nancy Cartwright - 1983 - New York: Oxford University Press.
Mathematics and Scientific Representation.Christopher Pincock - 2012 - Oxford and New York: Oxford University Press USA.
Three Kinds of Idealization.Michael Weisberg - 2007 - Journal of Philosophy 104 (12):639-659.

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