In this thesis, I present a novel account of scientific modeling that achieves the stability and generalizability of static approaches with the flexibility and practical relevance of diachronic approaches. In this account, modeling is characterized by the use of a similarity relation for the purpose of surrogate reasoning. Many criticisms of similarity are based on the fact that there is no way to objectively assess similarity between two things that share some, but not all, features. This account does not rely on the inherently flawed notion of objectively assessing similarity. Instead, the focus is on subjective assessment of similarity, within the specific context of an agent using the similarity for surrogate reasoning. This account captures the diversity of models while providing coherence among common features and functions, as evidenced by application to a series of interrelated examples in a case study from mid-twentieth century cognitive psychology. The similarity/difference account advocated in this thesis is particularly significant because its demonstrated success, evidenced by the case study, dispels several misconceptions about the study of scientific models. Advocates of static approaches claim that a diachronic approach cannot provide the generalizability necessary for a unified account, but the functional and agent-oriented similarity/difference account proves otherwise. Advocates of practice-based approaches often suggest that imilarity is too restrictive to capture the diversity of scientific models, but the similarity/difference account demonstrates that this concern only applies to a radically naturalized concept of similarity. As part of an agent-oriented account, a non-naturalized concept of similarity can be flexible enough to capture the full range of scientific models. Combining a diachronic approach with the similarity relation usually associated with static approaches results in an account that can circumvent the issues usually associated with either diachronic approaches or similarity alone.

In this thesis, I present a novel account of scientific modeling that achieves the stability and generalizability of static approaches with the flexibility and practical relevance of diachronic approaches. In this account, modeling is characterized by the use of a similarity relation for the purpose of surrogate reasoning. Many criticisms of similarity are based on the fact that there is no way to objectively assess similarity between two things that share some, but not all, features. This account does not rely on the inherently flawed notion of objectively assessing similarity. Instead, the focus is on subjective assessment of similarity, within the specific context of an agent using the similarity for surrogate reasoning. This account captures the diversity of models while providing coherence among common features and functions, as evidenced by application to a series of interrelated examples in a case study from mid-twentieth century cognitive psychology. The similarity/difference account advocated in this thesis is particularly significant because its demonstrated success, evidenced by the case study, dispels several misconceptions about the study of scientific models. Advocates of static approaches claim that a diachronic approach cannot provide the generalizability necessary for a unified account, but the functional and agent-oriented similarity/difference account proves otherwise. Advocates of practice-based approaches often suggest that imilarity is too restrictive to capture the diversity of scientific models, but the similarity/difference account demonstrates that this concern only applies to a radically naturalized concept of similarity. As part of an agent-oriented account, a non-naturalized concept of similarity can be flexible enough to capture the full range of scientific models. Combining a diachronic approach with the similarity relation usually associated with static approaches results in an account that can circumvent the issues usually associated with either diachronic approaches or similarity alone.

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