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Discovering Mechanisms: A Computational Philosophy of Science Perspective

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Discovery Science (DS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2226))

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Abstract

A task in the philosophy of discovery is to find reasoning strategies for discovery, which fall into three categories: strategies for generation, evaluation and revision. Because mechanisms are often what is discovered in biology, a newc haracterization of mechanism aids in their discovery. A computational system for discovering mechanisms is sketched, consisting of a simulator, a library of mechanism schemas and components, and a discoverer for generating, evaluating and revising proposed mechanism schemas. Revisions go through stages from howp ossibly to howplausibly to howactually .

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References

  1. Beatty, John (1995), “The Evolutionary Contingency Thesis,” in James G. Lennox and Gereon Wolters (eds.), Concepts, Theories, and Rationality in the Biological Sciences. Pittsburgh, PA: University of Pittsburgh Press, pp. 45–81.

    Google Scholar 

  2. Bechtel, William and Robert C. Richardson (1993), Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research. Princeton, N. J.: Princeton University Press.

    Google Scholar 

  3. Craver, Carl (2001), “Role Functions, Mechanisms, and Hierarchy,” Philosophy of Science 68: 53–74.

    Article  Google Scholar 

  4. Craver, Carl and Lindley Darden (2001), “Discovering Mechanisms in Neurobiology: The Case of Spatial Memory,” in Peter Machamer, R. Grush, and P. McLaughlin (eds.), Theory and Method in the Neurosciences. Pittsburgh, PA: University of Pittsburgh Press, pp. 112–137.

    Google Scholar 

  5. Darden, Lindley (1987), “Viewing the History of Science as Compiled Hindsight,” AI Magazine 8(2): 33–41.

    Google Scholar 

  6. Darden, Lindley (1990), “Diagnosing and Fixing Faults in Theories,” in J. Shrager and P. Langley (eds.), Computational Models of Scientific Discovery and Theory Formation. San Mateo, CA: Morgan Kaufmann, pp. 319–346.

    Google Scholar 

  7. Darden, Lindley (1991), Theory Change in Science: Strategies from Mendelian Genetics. New York: Oxford University Press.

    Google Scholar 

  8. Darden, Lindley (1998), “Anomaly-Driven Theory Redesign: Computational Philosophy of Science Experiments,” in Terrell W. Bynum and James Moor (eds.), The Digital Phoenix: How Computers are Changing Philosophy. Oxford: Blackwell, pp. 62–78. Available: http://www.inform.umd.edu/PHIL/faculty/LDarden/Research/pubs/

    Google Scholar 

  9. Darden, Lindley (forthcoming), “Strategies for Discovering Mechanisms: Schema Instantiation, Modular Subassembly, Forward Chaining/Backtracking,” Presented at PSA 2000, Vancouver. Preprint available: http://www.inform.umd.edu/PHIL/faculty/LDarden/Research/pubs

  10. Darden, Lindley and Joseph A. Cain (1989), “Selection Type Theories,” Philosophy of Science 56: 106–129. Available: http://www.inform.umd.edu/PHIL/faculty/LDarden/Research/pubs/

    Article  Google Scholar 

  11. Darden, Lindley and Carl Craver (in press), “Strategies in the Interfield Discovery of the Mechanism of Protein Synthesis,” Studies in History and Philosophy of Biological and Biomedical Sciences.

    Google Scholar 

  12. Darden, Lindley, Dale Moberg, Sunil Thadani, and John Josephson, (July 1992), “A Computational Approach to Scientific Theory Revision: The TRANSGENE Experiments,” Technical Report 92-LD-TRANSGENE, Laboratory for Artificial Intelligence Research, The Ohio State University. Columbus, Ohio, USA.

    Google Scholar 

  13. Dunbar, Kevin (1995), “HowScien tists Really Reason: Scientific Reasoning in Real-World Laboratories,” in R. J. Sternberg and J. E. Davidson (eds.), The Nature of Insight. Cambridge, MA: MIT Press, pp. 365–395.

    Google Scholar 

  14. Glennan, Stuart S. (1996), “Mechanisms and The Nature of Causation,” Erkenntnis 44: 49–71.

    Article  Google Scholar 

  15. Goel, Ashok and B. Chandrasekaran, (1989) “Functional Representation of Designs and Redesign Problem Solving,” in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI, August 1989, pp. 1388–1394.

    Google Scholar 

  16. Holyoak, Keith J. and Paul Thagard (1995), Mental Leaps: Analogy in Creative Thought. Cambridge, MA: MIT Press.

    Google Scholar 

  17. Karp, Peter (1990), “Hypothesis Formation as Design,” in J. Shrager and P. Langley (eds.), Computational Models of Scientific Discovery and Theory Formation. San Mateo, CA: Morgan Kaufmann, pp. 275–317.

    Google Scholar 

  18. Karp, Peter (1993), “A Qualitative Biochemistry and its Application to the Regulation of the Tryptophan Operon,” in L. Hunter (ed.), Artificial Intelligence and Molecular Biology. Cambridge, MA: AAAI Press and MIT Press, pp. 289–324.

    Google Scholar 

  19. Karp, Peter D. (2000), “An Ontology for Biological Function Based on Molecular Interactions,” Bioinformatics 16:269–285.

    Article  MathSciNet  Google Scholar 

  20. Machamer, Peter, Lindley Darden, and Carl Carver (2000), “Thinking About Mechanisms,” Philosophy of Science 67: 1–25.

    Article  MathSciNet  Google Scholar 

  21. Moberg, Dale and John Josephson (1990), “Diagnosing and Fixing Faults in Theories, Appendix A: An Implementation Note,” in J. Shrager and P. Langley (eds.), Computational Models of Scientific Discovery and Theory Formation. San Mateo, CA: Morgan Kaufmann, pp. 347–353.

    Google Scholar 

  22. Morowitz, Harold (1985), “Models for Biomedical Research: A New Perspective,” Report of the Committee on Models for Biomedical Research. Washington, D.C.: National Academy Press.

    Google Scholar 

  23. Morowitz, Harold and Temple Smith (1987), “Report of the Matrix of Biological KnowledgeWorkshop, July 13-August 14, 1987,” Sante Fe, NM: Sante Fe Institute.

    Google Scholar 

  24. Piatetsky-Shapiro, Gregory and William J. Frawley (eds.) (1991), Knowledge Discovery in Databases. Cambridge, MA: MIT Press.

    Google Scholar 

  25. Simon, Herbert A. (1977), Models of Discovery. Dordrecht: Reidel.

    MATH  Google Scholar 

  26. Swanson, Don R. (1990), “Medical Literature as a Potential Source of New Knowledge,” Bull. Med. Libr. Assoc. 78:29–37.

    Google Scholar 

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© 2001 Springer-Verlag Berlin Heidelberg

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Darden, L. (2001). Discovering Mechanisms: A Computational Philosophy of Science Perspective. In: Jantke, K.P., Shinohara, A. (eds) Discovery Science. DS 2001. Lecture Notes in Computer Science(), vol 2226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45650-3_2

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  • DOI: https://doi.org/10.1007/3-540-45650-3_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42956-2

  • Online ISBN: 978-3-540-45650-6

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