Efficient convergence implies ockham's razor
| Abstract | A finite data set is consistent with infinitely many alternative theories. Scientific realists recommend that we prefer the simplest one. Anti-realists ask how a fixed simplicity bias could track the truth when the truth might be complex. It is no solution to impose a prior probability distribution biased toward simplicity, for such a distribution merely embodies the bias at issue without explaining its efficacy. In this note, I argue, on the basis of computational learning theory, that a fixed simplicity bias is necessary if inquiry is to converge to the right answer efficiently, whatever the right answer might be. Efficiency is understood in the sense of minimizing the least fixed bound on retractions or errors prior to convergence. | |||||||||
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Similar books and articlesR. Sorensen (2011). Simpler Without a Simplest: Ockham's Razor Implies Epistemic Dilemmas. Analysis 71 (2):260-264.
Daniel Steel (2009). Testability and Ockham's Razor: How Formal and Statistical Learning Theory Converge in the New Riddle of Induction. Journal of Philosophical Logic 38 (5):471 - 489.
Arnold Zellner, Hugo A. Keuzenkamp & Michael McAleer (eds.) (2001). Simplicity, Inference and Modeling: Keeping It Sophisticatedly Simple. Cambridge University Press.
Kevin T. Kelly (2004). Justification as Truth-Finding Efficiency: How Ockham's Razor Works. Minds and Machines 14 (4):485-505.
Kevin Kelly (2004). Justification as Truth-Finding Efficiency: How Ockham's Razor Works. Minds and Machines 14 (4):485-505.
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