@inproceedings{1250089,
  abstract     = {{Motivated by the renewed interest in knowledge discovery from data (KDD) by the artificial intelligence community, this paper provides a critical assessment of models of discovery in science. The most influential research program, called BACON, is using the model of data-driven induction. 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 concentrate on the first claim that the computational model used by BACON provides an explanation of the real historical cases. The explanatory power of BACON’s model will be challenged by providing evidence that the data used by the program – despite the claims being made by the authors – does not correspond with the historical data available to Kepler and his contemporaries. Furthermore, 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. An outline of an alternative model of hypothesis generation and testing will be offered for one of the case studies.}},
  author       = {{Heeffer, Albrecht}},
  booktitle    = {{Third international conference on philosophy and cognitive science, Proceedings}},
  editor       = {{Li, Ping}},
  keywords     = {{models,Scientific discovery}},
  language     = {{eng}},
  location     = {{Guangzhou, China}},
  pages        = {{45--60}},
  publisher    = {{Sun Yat Sen University}},
  title        = {{Models of scientific discovery: what do they explain?}},
  year         = {{2011}},
}