Modeling in Philosophy of Science

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Hartmann, Stephan (2008)
  • Subject: PHI

Models are a principle instrument of modern science. They are built, applied, tested, compared, revised and interpreted in an expansive scientific literature. Throughout this paper, I will argue that models are also a valuable tool for the philosopher of science. In particular, I will discuss how the methodology of Bayesian Networks can elucidate two central problems in the philosophy of science. The first thesis I will explore is the variety-of-evidence thesis, which argues that the more varied the supporting evidence, the greater the degree of confirmation for a given hypothesis. However, when investigated using Bayesian methodology, this thesis turns out not to be sacrosanct. In fact, under certain conditions, a hypothesis receives more confirmation from evidence that is obtained from one rather than more instruments, and from evidence that confirms one rather than more testable consequences of the hypothesis. The second challenge that I will investigate is scientific theory change. This application highlights a different virtue of modeling methodology. In particular, I will argue that Bayesian modeling illustrates how two seemingly unrelated aspects of theory change, namely the (Kuhnian) stability of (normal) science and the ability of anomalies to over turn that stability and lead to theory change, are in fact united by a single underlying principle, in this case, coherence. In the end, I will argue that these two examples bring out some metatheoretical reflections regarding the following questions: What are the differences between modeling in science and modeling in philosophy? What is the scope of the modeling method in philosophy? And what does this imply for our understanding of Bayesianism?Article
  • References (26)
    26 references, page 1 of 3

    Bovens, L. and S. Hartmann (2003). Solving the Riddle of Coherence, Mind 112, 601{634.

    Bovens, L. and S. Hartmann (2004). Bayesian Epistemology. Oxford: Oxford University Press.

    Dietrich, F. and L. Moretti (2005). On Coherent Sets and the Transmission of Con rmation. Philosophy of Science 72(3): 403-424.

    Dorling, J (1979). Bayesian Personalism, the Methodology of Research Programmes, and Duhem's Problem, Studies in History and Philosophy of Science 10, 177-87.

    Earman, J. (1992). Bayes or Bust ? A Critical Examination of Bayesian Con rmation Theory. Cambridge, MA: MIT Press.

    Frigg, R. and S. Hartmann (2005). Models in Science. In: The Stanford Encyclopedia of Philosophy (Spring 2006 Edition).

    Gahde, U. (1996). Anomalies and the Revision of Theory-Nets. Notes on the Advance of Mercury's Perihelion. In: M.L. Dalla Chiara et. al. (eds.), Structures and Norms in Science. Dordrecht: Kluwer.

    Giere, R. (1988). Explaining Science: A Cognitive Approach. Chicago: University of Chicago Press.

    Hartmann, S. (1999). Models and Stories in Hadron Physics, in: Morgan and Morrison (1999), 326{346.

    Hartmann, S. (2001). E ective Field Theories, Reduction and Scienti c Explanation, Studies in History and Philosophy of Modern Physics 32B, 267-304.

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