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Learning, Simplicity, Truth, and Misinformation Kevin T. Kelly
 

Summary: Learning, Simplicity, Truth, and Misinformation
Kevin T. Kelly
Department of Philosophy
Carnegie Mellon University
kk3n@andrew.cmu.edu
March 5, 2005
Abstract
Both in learning and in natural science, one faces the problem of selecting among
a range of theories, all of which are compatible with the available evidence. The
traditional response to this problem has been to select the simplest such theory on the
basis of "Ockham's Razor". But how can a fixed bias toward simplicity help us find
possibly complex truths? I survey the current, textbook answers to this question and
find them all to be wishful, circular, or irrelevant. Then I present a new approach
based on minimizing the number of reversals of opinion prior to convergence to the
truth. According to this alternative approach, Ockham's razor is a good idea when it
seems to be (e.g., in selecting among parametrized models) and is not a good idea when
it feels dubious (e.g., in the inference of arbitrary computable functions). Hence, the
proposed vindication of Ockham's razor can be used to separate vindicated applications
of Ockham's razor from spurious ones.
0.1 Introduction

  

Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University

 

Collections: Mathematics