Holistic modeling: an objection to Weisberg’s weighted feature-matching account
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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 (MLE). 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.
KeywordsWeisberg Weighted feature-matching account Similarity Model Set-theoretic Holistic Scientific representation
I am grateful to a number of friends and colleagues for feedback on early drafts of this work, including Pierrick Bourrat, Mark Colyvan, Paul Griffiths, Qiaoying Lu, John Matthewson and Arnaud Pocheville. Special thanks is due to Paul Griffiths and Arnaud Pocheville, who gave me extremely useful help and encouragement over the course of developing this work. Thanks to the National Social Science Fund of China (Grant Number: 14ZDB018).
Compliance with ethical standards
Conflict of Interest
the author declares that he or she has no conflict of interest.
This study was funded by the National Social Science Fund of China (Grant Number: 14ZDB018).
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