Minimal Model Explanations
Philosophy of Science 81 (3):349-376 (2014)
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.Author's Profile
DOI
10.1086/676677
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Citations of this work
Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
Are More Details Better? On the Norms of Completeness for Mechanistic Explanations.Carl F. Craver & David M. Kaplan - 2020 - British Journal for the Philosophy of Science 71 (1):287-319.
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.
Epistemic Advantage on the Margin: A Network Standpoint Epistemology.Jingyi Wu - 2022 - Philosophy and Phenomenological Research:1-23.
References found in this work
Scientific Explanation and the Causal Structure of the World.Wesley C. Salmon - 1984 - Princeton University Press.
Simulation and Similarity: Using Models to Understand the World.Michael Weisberg - 2013 - Oxford University Press.
Mathematics and Scientific Representation.Christopher Pincock - 2012 - Oxford and New York: Oxford University Press USA.