Abstract
Motivated by the renewed interest in knowledge discovery from data (KDD) by the artificial intelligence community, this paper provides a critical assessment of the model of data-driven induction for scientific discovery. The most influential research program using this model is developed by the BACON team. Two of the main claims by this research program, the descriptive and constructive power of data-driven induction, are evaluated by means of two historical cases studies: the discovery of the sine law of refraction in optics and Kepler’s third law of planetary motion. I will provide evidence that the data used by the BACON program—despite the claims being made—does not correspond with the historical data available to Kepler and his contemporaries. Secondly, it is shown that for the two cases the method by which the general law was arrived at did not involve data-driven induction. Finally, the value of the data-driven induction as a general model for scientific discovery is being questioned.
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Acknowledgements
Apart from the LRR10 conference, this paper was presented at a research seminar at the Sydney Center for the Foundations of Science at Sydney University where the author was invited as a visiting fellow in 2011. The text benefited from valuable comments by two anonymous referees as well as comments by Stephen Gaukroger, Ofer Gal and John Schuster.
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Heeffer, A. (2014). Data-Driven Induction in Scientific Discovery: A Critical Assessment Based on Kepler’s Discoveries. In: Weber, E., Wouters, D., Meheus, J. (eds) Logic, Reasoning, and Rationality. Logic, Argumentation & Reasoning, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9011-6_4
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