Synthese 194 (5):1743–1764 (2017)

Authors
Wesley Fang
Shanxi University
Abstract
Michael Weisberg’s account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method. Then I consider whether Weisberg’s account could capture the judgments involved in this practice. I argue that his account fails for three reasons. First, his account is simply too abstract to capture what is going on in MLE. Second, it implies an atomistic conception of similarity, while MLE operates in a holistic manner. Third, Weisberg’s atomistic conception of similarity can be traced back to a problematic set-theoretic approach to the structure of models. Finally, I tentatively suggest how these problems might be solved by a holistic approach in which models and targets are compared in a non-set-theoretic fashion
Keywords Weisberg  Weighted feature-matching account  Similarity  Model  Set-theoretic  Holistic  Scientific representation
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Reprint years 2017
DOI 10.1007/s11229-016-1018-z
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References found in this work BETA

The Structure of Scientific Theories.Frederick Suppe (ed.) - 1974 - Urbana, University of Illinois Press.
Explaining Science: A Cognitive Approach.Jeffrey S. Poland - 1988 - Philosophical Review 100 (4):653-656.

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Implementation as Resemblance.André Curtis-Trudel - 2021 - Philosophy of Science 88 (5):1021-1032.

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