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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.

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