ABSTRACT
This chapter aims at examining the similarity view of the model–world relationship, especially Michael Weisberg’s weighted feature-matching account, in order to pave the way for developing the author’s holistic account in the next chapter. Weisberg’s account 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, the author outlines one common way by which scientists judge whether a model is similar enough to its target, namely, the maximum likelihood estimation (MLE) method. Then whether Weisberg’s account can capture the judgments involved in this practice is considered. It is argued 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.
