Journal for General Philosophy of Science

, Volume 48, Issue 1, pp 35–57

Interdisciplinarity as Hybrid Modeling

Article
  • 340 Downloads

Abstract

In this paper, I present a philosophical analysis of interdisciplinary scientific activities. I suggest that it is a fruitful approach to view interdisciplinarity in light of the recent literature on scientific representations. For this purpose I develop a meta-representational model in which interdisciplinarity is viewed in part as a process of integrating distinct scientific representational approaches. The analysis suggests that present methods for the evaluation of interdisciplinary projects places too much emphasis non-epistemic aspects of disciplinary integrations while more or less ignoring whether specific interdisciplinary collaborations puts us in a better, or worse, epistemic position. This leads to the conclusion that there are very good reasons for recommending a more cautious, systematic, and stringent approach to the development, evaluation, and execution of interdisciplinary science.

Keywords

Interdisciplinarity Modelling Philosophy of science Scientific representation 

References

  1. Aldrich, J. (2014). Interdisciplinarity: Its role in a discipline-based academy. Oxford: Oxford University Press.CrossRefGoogle Scholar
  2. Andersen, H., & Wagenknecht, S. (2013). Epistemic dependence in interdisciplinary groups. Synthese, 190, 1881–1898.CrossRefGoogle Scholar
  3. American Psychiatric Association. (Ed.). (1980). Diagnostic and statistical manual of mental disorders: DSM-III. Arlington: American Psychiatric Association.Google Scholar
  4. Bridgman, P. (1927). The logic of modern physics. New York: Macmillan.Google Scholar
  5. Bridgman, P. (1954). Remarks on the present state of operationalism. The Scientific Monthly, 79, 224–226.Google Scholar
  6. Cartwright, N. (1983). How the laws of physics lie. Oxford: Oxford University Press.CrossRefGoogle Scholar
  7. Cartwright, N. (1999). The dappled world: A study of the boundaries of science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  8. Chakravartty, A. (2010). Informational versus functional theories of scientific representation. Synthese, 172, 197–213.CrossRefGoogle Scholar
  9. Collin, F. (2011). Science studies as naturalized philosophy. Dordrecht: Springer.CrossRefGoogle Scholar
  10. Collins, H. (1985). Changing order: Replication and induction in scientific practice. London: The University of Chicago Press.Google Scholar
  11. Cooper, J., Kendell, R., Gurland, B., Sharpe, L., & Copeland, J. (1972). Psychiatric diagnosis in New York and London: A comparative study of mental hospital admissions. Oxford: Oxford University Press.Google Scholar
  12. Davidson, D. (1974). On the very idea of a conceptual scheme. In Proceedings and Addresses of the American Philosophical Association.Google Scholar
  13. Dobzhansky, T. (1937). Genetics and the origin of species. New York: Columbia Universty Press.Google Scholar
  14. EC. (2014). Guidance for evaluators of Horizon 2020 proposals [Online]. EC. http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/pse/h2020-evaluation-faq_en.pdf. Accessed 8 Jan 2015.
  15. Ellenberger, H. (1970). The discovery of the unconscious: The history and evolution of dynamic psychiatry. London: Allen Lane.Google Scholar
  16. Frances, A. (2013). The past, present and future of psychiatric diagnosis. World Psychiatry, 12, 111–112.CrossRefGoogle Scholar
  17. Fried, E., & Nesse, R. (2015). Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study. Journal of Affective Disorders, 172, 96–102.CrossRefGoogle Scholar
  18. Frodeman, R. (2013). Philosophy dedisciplined. Synthese, 190, 1917–1936.CrossRefGoogle Scholar
  19. Frodeman, R. (2014). Sustainable knowledge: A theory of interdisciplinarity. Basingstoke: Palgrave Pivot.CrossRefGoogle Scholar
  20. Frodeman, R., Thompson Klein, J., & Mitcham, C. (2010). The Oxford handbook of interdisciplinarity. Oxford: Oxford University Press.Google Scholar
  21. Fulford, K., & N. Sartorius. (2009). The secret history of ICD and the hidden future of DSM. In M. R. Broome & L. Bortolotti (Eds.), Psychiatry as cognitive neuroscience (pp. 29–48). Oxford: Oxford University Press.CrossRefGoogle Scholar
  22. Fuller, S. (2010). Deviant interdisciplinarity. In R. Frodeman, J. Thompson Klein & C. Mitcham (Eds.), The Oxford handbook of interdisciplinarity (pp. 50–64). Oxford: Oxford University Press.Google Scholar
  23. Galison, P. (1997). Image and logic: A material culture of microphysics. Chicago: University of Chicago Press.Google Scholar
  24. Giere, R. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  25. Giere, R. (1999a). Science without laws. Chicago: University of Chicago Press.Google Scholar
  26. Giere, R. (1999b). Using models to represent reality. In L. Magnani, N. J. Nersessian & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 41–57). New York: Kluwer/Plenum.CrossRefGoogle Scholar
  27. Giere, R. (2004). How models are used to represent reality. Philosophy of Science, 71, 742–752.CrossRefGoogle Scholar
  28. Giere, R. (2006a). Perspectival pluralism. In S. Kellert, H. Longino & C. Waters (Eds.), Scientific pluralism (pp. 26–41). Minneapolis: University of Minnesota Press.Google Scholar
  29. Giere, R. (2006b). Scientific perspectivism. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  30. Giere, R. (2010). An agent-based conception of models and scientific representation. Synthese, 172, 269–281.CrossRefGoogle Scholar
  31. Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33, 587–606.CrossRefGoogle Scholar
  32. Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., & Kruger, L. (1989). The empire of chance: How probability changed science and everyday life. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  33. Godfrey-Smith, P. (2009). Models and fictions in science. Philosophical Studies, 143, 101–116.CrossRefGoogle Scholar
  34. Goodman, N. (1976). Languages of art: An approach to a theory of symbols. Indianapolis: Hackett.Google Scholar
  35. Hacking, I. (1983). Representing and intervening: Introductory topics in the philosophy of natural science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  36. Hanson, N. (1958). Patterns of discovery; an inquiry into the conceptual foundations of science. Cambridge: Cambridge University Press.Google Scholar
  37. Hempel, C. (1961). Introduction to problems of taxonomy. In J. Zubin, (Ed.), Field studies in the mental disorders (pp. 3–22). New York. Grune & Stratton.Google Scholar
  38. Hoffmann, M., Schmidt, J. C., & Nersessian, N. J. (2013). Philosophy of and as interdisciplinarity. Synthese, 190, 1857–1864.CrossRefGoogle Scholar
  39. Holbrook, J. (2013). What is interdisciplinary communication? Reflections on the very idea of disciplinary integration. Synthese, 190, 1865–1879.CrossRefGoogle Scholar
  40. Hyman, S. (2011). Diagnosing the DSM: Diagnostic classification needs fundamental reform. Cerebrum, 2011, 6.Google Scholar
  41. Ingram, R., & Luxton, D. (2005). Vulnerability-stress models. In B. Hankin & J. Abela (Eds.), Development of psychopathology: A vulnerability-stress perspective (pp. 32–46). New York: Sage Publications.CrossRefGoogle Scholar
  42. Jansson, L. P., Handest, J., Nielsen, D. Sæbye, & Parnas, J. (2002). Exploring boundaries of schizophrenia: A comparison of ICD-10 with other diagnostic systems. World Psychiatry, 1, 109–114.Google Scholar
  43. Jansson, L., & Parnas, J. (2007). Competing definitions of schizophrenia: What can be learned from polydiagnostic studies? Schizophrenia Bulletin, 33, 1178–1200.CrossRefGoogle Scholar
  44. Kellert, S. (2009). Borrowed knowledge: Chaos theory and the challenge of learning across disciplines. Chicago: University of Chicago Press.Google Scholar
  45. Kellert, S., Longino, H., & Waters, C. K. (Eds.). (2006). Scientific pluralism. Minneapolis: University of Minnesota Press.Google Scholar
  46. Kitcher, P. (1992). Freud’s dream: A complete interdisciplinary science of mind. Cambridge, MA: MIT Press.Google Scholar
  47. Kitcher, P (2007). Freud’s interdisciplinary Fiasco. In A. Brook (Ed.), The prehistory of cognitive science (pp. 230–249). New York: Palgrave Macmillan.Google Scholar
  48. Klein, J. (1990). Interdisciplinarity: History, theory, and practice. Detroit: Wayne State University.Google Scholar
  49. Klein, J. (2005). Humanities, culture, and interdisciplinarity: The changing American academy. Albany: State University of New York Press.Google Scholar
  50. Klein, J. (2010). A taxonomy of interdisciplinarity. In R. Frodeman, J. Thompson Klein & C. Mitcham (Eds.), The Oxford handbook of interdisciplinarity (pp. 15–30). Oxford: Oxford University Press.Google Scholar
  51. Kuhn, T. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.Google Scholar
  52. Latour, B. (1988). The Pasteurization of France. Cambridge: Harvard University Press.Google Scholar
  53. Longino, H. (2006). Theoretical pluralism and the scientific study of behaviour. In S. H. Kellert, H. Longino, & C. K. Waters (Eds.), Scientific pluralism (pp. 102–131). Minneapolis: University of Minnesota Press.Google Scholar
  54. Mcmullin, E. (1985). Galilean idealization. Studies in History and Philosophy of Science, 16, 247–273.CrossRefGoogle Scholar
  55. Meehl, P. (1962). Schizotaxia, schizotypy, schizophrenia. American Psychologist, 17, 827–838.CrossRefGoogle Scholar
  56. Mirowski, P. (1989). More heat than light: Economics as social physics, physics as nature’s economics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  57. Mitchell, S., L. Daston, G. Gigerenzer, N. Sesardic & Sloep P. (1997). The Why’s and How’s of Interdisciplinarity. In Weingart P, Mitchell SD, Richerson PJ & Maasen S (Eds.), Human by nature: Between biology and the social sciences (pp. 103–150). Erlbaum Press.Google Scholar
  58. Mitchell, S. (2002). Integrative pluralism. Biology and Philosophy, 17, 55–70.CrossRefGoogle Scholar
  59. Mitchell, S. (2003). Biological complexity and integrative pluralism. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  60. Parnas, J. (2013). The Breivik case and ‘conditio psychiatrica’. World Psychiatry, 12, 22–23.CrossRefGoogle Scholar
  61. Putnam, H. (Ed.). (1975). The meaning of “meaning”. In Mind, language and reality; Philosophical papers (Vol. 2, pp. 215–271). Cambridge: Cambridge University Press.Google Scholar
  62. Reichenbach, H. (1958). The philosophy of space & time. New York: Dover.Google Scholar
  63. Rosenhan, D. L. (1973). On being sane in insane places. Science, 179, 250–258.CrossRefGoogle Scholar
  64. Sato, Y., & Berrios, G. (2001). Operationalism in psychiatry: A conceptual history of operational diagnostic criteria. Clinical Psychiatry, 43, 704–713.Google Scholar
  65. Shapere, D. (1966). Meaning and scientific change. In R. G. Colodny (Ed.), Mind and Cosmos: Essays in Contemporary Science and Philosophy (pp. 41–85). Pittsburgh: University of Pittsburgh Press.Google Scholar
  66. Suarez, M. (2003). Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science, 17, 225–244.CrossRefGoogle Scholar
  67. Suarez, M. (2004). An inferential conception of scientific representation. Philosophy of Science, 71, 767–779.CrossRefGoogle Scholar
  68. Suarez, M. (2009). Fictions in science: Philosophical essays on modeling and idealization. New York: Routledge.Google Scholar
  69. Suppes, P. (1962). Models of data. In E. Nagel, P. Suppes & A. Tarski (Eds.), Logic, Methodology and Philosophy of Science: Proceedings of the 1960 international Congress (pp. 252–261). Stanford: Stanford University Press.Google Scholar
  70. Thomson-Jones, M. (2012). Modeling without mathematics. Philosophy of Science, 79, 761–772.CrossRefGoogle Scholar
  71. Van Fraassen, B. (1980). The scientific image. Oxford: Oxford University Press.CrossRefGoogle Scholar
  72. Van Fraassen, B. (2008). Scientific representation: Paradoxes of perspective. Oxford: Oxford University Press.CrossRefGoogle Scholar
  73. Weisberg, M. (2007). Who is a Modeler? British Journal for the Philosophy of Science, 58, 207–233.CrossRefGoogle Scholar
  74. Weisberg, M. (2013). Simulation and similarity: Using models to understand the world. New York: Oxford University Press.CrossRefGoogle Scholar
  75. Wimsatt, W. (1987). False models as means to truer theories. In M. H. Nitecki & A. Hoffman (Eds.), Neutral models in biology (pp. 23–55). New York: Oxford University Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department for the Study of CultureUniversity of Southern DenmarkOdenseDenmark

Personalised recommendations