Testability and ockham's razor: How formal and statistical learning theory converge in the new Riddle of induction [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Journal of Philosophical Logic 38 (5):471 - 489 (2009)
Nelson Goodman’s new riddle of induction forcefully illustrates a challenge that must be confronted by any adequate theory of inductive inference: provide some basis for choosing among alternative hypotheses that fit past data but make divergent predictions. One response to this challenge is to distinguish among alternatives by means of some epistemically significant characteristic beyond fit with the data. Statistical learning theory takes this approach by showing how a concept similar to Popper’s notion of degrees of testability is linked to minimizing expected predictive error. In contrast, formal learning theory appeals to Ockham’s razor, which it justifies by reference to the goal of enhancing efficient convergence to the truth. In this essay, I show that, despite their differences, statistical and formal learning theory yield precisely the same result for a class of inductive problems that I call strongly VC ordered , of which Goodman’s riddle is just one example.
|Keywords||Goodman New riddle of induction Ockham’s razor Simplicity Testability Formal learning theory Statistical learning theory|
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References found in this work BETA
Nelson Goodman (1983). Fact, Fiction, and Forecast. Harvard University Press.
Gilbert Harman & Sanjeev Kulkarni (2007). Reliable Reasoning: Induction and Statistical Learning Theory. A Bradford Book.
Peter Godfrey-Smith (2003). Goodman's Problem and Scientific Methodology. Journal of Philosophy 100 (11):573 - 590.
Carl G. Hempel (1945). Studies in the Logic of Confirmation (II.). Mind 54 (214):97-121.
Citations of this work BETA
Kristen Intemann & Inmaculada de Melo-Martín (2014). Are There Limits to Scientists' Obligations to Seek and Engage Dissenters? Synthese 191 (12):2751-2765.
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