Philosophy of Science 70 (2):444-447 (2003)

Ioannis Votsis
Heinrich Heine University Düsseldorf
This is certainly true. Simulationists and experimentalists face equally relevant challenges when it comes to establishing that the results of their simulation or experiment are informative about the real world. But it is one thing to point this fact out, and it is another to understand how those challenges are overcome, under differing circumstances, and in varying contexts. It is here that Marcel Boumans’ contribution becomes especially valuable. He presents an example from economics in which a mathematical model performs the role, not of a representational entity, but of a data sensor. Boumans argues, and I concur, that the manner in which such models are assessed is particularly interesting. They cannot be assessed merely by being confronted with facts about the world, since these models are themselves used in generating the relevant data about the phenomena in question. The relevant strategy for assessing these models is calibration. In other words, rather than being held side by side with the relevant bit of the world, the models are held up against other instruments that are antecedently believed to be reliable sources of data.
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DOI 10.1086/375479
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Social Empiricism.Miriam Solomon - 2003 - Philosophy 78 (303):132-136.
Societies of Minds: Science as Distributed Computing.Paul Thagard - 1991 - Studies in History and Philosophy of Science Part A 24 (1):49-67.

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