Zach, Martin (2022) A Complementary Account of Scientific Modelling: Modelling Mechanisms in Cancer Immunology. [Preprint]
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Abstract
According to a widely held view, scientific modelling consists in entertaining a set of model descriptions that specify a model. Rather than studying the phenomenon of interest directly, scientists investigate the phenomenon indirectly via a model in the hope of learning about some of the phenomenon’s features. I call this view the description-driven modelling (DDM) account. I argue that although an accurate description of much of scientific research, the DDM account is found wanting as regards the mechanistic modelling found in many branches of biology. By analysing research practices in cancer immunology concerning the development of mechanistic models of the process of cancer metastasis, this paper presents and argues for a complementary account of scientific modelling, herein called the experimentation-driven modelling (EDM) account. In EDM, scientists investigate a set of experimental systems and then integrate the results obtained from experiments into a mechanistic model. While EDM shares some key features with DDM, the two are epistemically very different approaches to research.
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| Item Type: | Preprint | ||||||
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| Additional Information: | Forthcoming in the British Journal for the Philosophy of Science | ||||||
| Keywords: | cancer; experimentation; immunology; scientific modeling; mechanisms | ||||||
| Subjects: | General Issues > Data Specific Sciences > Biology General Issues > Confirmation/Induction General Issues > Experimentation General Issues > Explanation General Issues > Models and Idealization |
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| Depositing User: | Dr. Martin Zach | ||||||
| Date Deposited: | 21 Jun 2022 17:41 | ||||||
| Last Modified: | 21 Jun 2022 17:41 | ||||||
| Item ID: | 20777 | ||||||
| Subjects: | General Issues > Data Specific Sciences > Biology General Issues > Confirmation/Induction General Issues > Experimentation General Issues > Explanation General Issues > Models and Idealization |
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| Date: | 2022 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/20777 |
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