Studying Model Transfer in Science
Philosophers and historians of science have recently started to discuss the role of empirical methods for their field. This is a pressing methodological issue, given that formal-mathematical models, experimental tools, ethnographic approaches, and simulation techniques are already accepted in philosophy of science, while historians of science draw increasingly on scientometric methods and tools from digital humanities. The need for discussing the usefulness of empirical methods is also fostered by the fact that the data relevant to study the development, the social organization, and the procedures of science are readily available. In this talk, I want to push the debate one step further by discussing the role of quantitative-empirical methods, such as network analysis. I argue that empirical network analysis is particularly useful for those areas of philosophy of science that draw on the traditional historical case study methodology – such as philosophy of science in practice and integrated history and philosophy of science – because it allows for mitigating a number of methodological challenges arising from the case study methodology. By focusing on the example of model transfer in science, I will discuss the advantages of empirical network analysis but caution that it cannot replace more traditional philosophical methods. Rather, I suggest, it must rely on them to fully develop its potentials.
Catherine Herfeld is professor of philosophy and history of economics at Leibniz University Hannover. Her research interests cover topics in history, methodology, and philosophy of economics. In the context of an ERC Starting Grant, she currently researches the questions of why and how models are transferred across different domains and in which way such model transfers can lead to progress in economics. She is also interested in the history of rational choice theories in economics, including their development in, and diffusion across different institutional contexts.
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