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The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective

Published online by Cambridge University Press:  01 January 2022

Leah Henderson ,
Noah D. Goodman ,
Joshua B. Tenenbaum  and
James F. Woodward
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Abstract

Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher-level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea that higher-level theories guide learning at lower levels. In addition, they help resolve certain issues for Bayesians, such as scientific preference for simplicity and the problem of new theories.

Type
Research Article
Information
Philosophy of Science , Volume 77 , Issue 2 , April 2010 , pp. 172 - 200
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

‡

This work was supported in part by the James S. McDonnell Foundation Causal Learning Collaborative. Thanks to Zoubin Ghahramani for providing the code that we modified to produce the results and figures in the section on Bayesian curve fitting. We are extremely grateful to Charles Kemp for his contributions, especially helpful discussions of hierarchical Bayesian models in general as well as in connection to philosophy of science. We thank Alison Gopnik for encouraging and supporting this project, and we are grateful to Franz Huber, John Norton, Ken Schaffner, and Jiji Zhang for reading earlier versions of the manuscript and making helpful criticisms.

References

Carey, S., and Spelke, E. 1996. “Science and Core Knowledge.” Philosophy of Science 63:515–33.CrossRefGoogle Scholar
Crick, F. H. C. 1958. “On Protein Synthesis.” Symposia of the Society for Experimental Biology 12:139–63.Google ScholarPubMed
Davidson, E. 2006. The Regulatory Genome: Gene Regulatory Networks in Development and Evolution. Burlington, MA: Academic Press.Google Scholar
Dowe, D. L., Gardner, S., and Oppy, G. 2007. “Bayes Not Bust! Why Simplicity Is No Problem for Bayesians.” British Journal of the Philosophy of Science 58:709–54.CrossRefGoogle Scholar
Earman, J. 1992. Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. Cambridge, MA: MIT Press.Google Scholar
Forster, M. 1995. “Bayes and Bust: Simplicity as a Problem for a Probabilist's Approach to Confirmation.” British Journal of the Philosophy of Science 46:399–424.CrossRefGoogle Scholar
Forster, M. R., and Sober, E. 1994. “How to Tell When Simpler, More Unified, or Less Ad Hoc Theories Provide More Accurate Predictions.” British Journal of the Philosophy of Science 45:1–35.CrossRefGoogle Scholar
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. 2004. Bayesian Data Analysis. Boca Raton, FL: Chapman & Hall.Google Scholar
Giere, R. N. 1996. “The Scientist as Adult.” Philosophy of Science 63:538–41.CrossRefGoogle Scholar
Gillies, D. 2001. “Bayesianism and the Fixity of the Theoretical Framework.” In Foundations of Bayesianism, ed. Corfield, D. and Williamson, J., 363–80. Dordrecht: Kluwer.Google Scholar
Glymour, C. 1980. Theory and Evidence. Princeton, NJ: Princeton University Press.Google Scholar
Godfrey-Smith, P. 2003. Theory and Reality: An Introduction to the Philosophy of Science. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Gopnik, A. 1996. “The Scientist as Child.” Philosophy of Science 63:485–514.CrossRefGoogle Scholar
Griffiths, T., and Tenenbaum, J. B. 2007. “Two Proposals for Causal Grammars.” In Causal Learning: Psychology, Philosophy and Computation, ed. Gopnik, A. and Schulz, L., 323–43. Oxford: Oxford University Press.Google Scholar
Hempel, C. G. 1958. “The Theoretician's Dilemma.” In Minnesota Studies in the Philosophy of Science, Vol. 2, Concepts, Theories and the Mind-Body Problem, ed. Feigl, H., Scriven, M., and Maxwell, G., 37–99. Minneapolis: University of Minnesota Press.Google Scholar
Jefferys, W. H., and Berger, J. O. 1992. “Ockham's Razor and Bayesian Analysis.”American Scientist 80:64–72.Google Scholar
Kemp, C. 2007. “The Acquisition of Inductive Constraints.” PhD diss., Massachusetts Institute of Technology.Google Scholar
Kemp, C., Griffiths, T. L., and Tenenbaum, J. B. 2004. “Discovering Latent Classes in Relational Data.” MIT AI Memo 2004-019, Massachusetts Institute of Technology.Google Scholar
Kemp, C., and Tenenbaum, J. B. 2008. “The Discovery of Structural Form.” Proceedings of the National Academy of Sciences 105:10687–92.CrossRefGoogle ScholarPubMed
Kuhn, T. S. 1962. The Structure of Scientific Revolutions. Chicago: University of Chicago Press.Google Scholar
Lakatos, I. 1978. “Falsification and the Methodology of Scientific Research Programmes.” In The Methodology of Scientific Research Programmes, ed. Worrall, J. and Currie, G., 8–101. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Laudan, L. 1978. Progress and Its Problems. Berkeley: University of California Press.Google Scholar
MacKay, D. J. C. 2003. Information Theory, Inference and Learning Algorithms. Cambridge: Cambridge University Press.Google Scholar
Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., and Griffiths, T. 2006. “Structured Priors for Structure Learning.” In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, ed. Dechter, R. and Bacchus, F. Corvallis, OR: Association for Uncertainty in Artificial Intelligence.Google Scholar
Popper, K. R. 1934. The Logic of Scientific Discovery. London: Hutchinson.Google Scholar
Popper, K. R. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge. London: Routledge & Kegan Paul.Google Scholar
Quine, W. V. 1970. Philosophy of Logic. Cambridge, MA: Harvard University Press.Google Scholar
Rosenkrantz, R. D. 1977. Inference, Method and Decision: Towards a Bayesian Philosophy of Science. Dordrecht: Synthese Library.CrossRefGoogle Scholar
Tenenbaum, J. B., Griffiths, T. L., and Nigoyi, S. 2007. “Intuitive Theories as Grammars for Causal Inference.” In Causal Learning: Psychology, Philosophy and Computation, ed. Gopnik, A. and Schulz, L., 301–22. Oxford: Oxford University Press.Google Scholar
Tenenbaum, J. B., and Nigoyi, S. 2003. “Learning Causal Laws.” In Proceedings of the 25th Annual Conference of the Cognitive Science Society, ed. Alterman, R. and Kirsh, D., 1152–57. Mahwah, NJ: Erlbaum.Google Scholar
Wellman, H. M., and Gelman, S. A. 1992. “Cognitive Development: Foundational Theories of Core Domains.” Annual Review of Psychology 43:337–75.CrossRefGoogle ScholarPubMed
White, R. 2005. “Why Favour Simplicity?” Analysis 65 (3): 205–10..CrossRefGoogle Scholar