Fact-Free Learning

Enriqueta Aragones, Itzhak Gilboa, Andrew Postlewaite, David Schmeidler

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

People may be surprised by noticing certain regularities that hold in existing knowledge they have had for some time. That is, they may learn without getting new factual information. We argue that this can be partly explained by computational complexity. We show that, given a knowledge base, finding a small set of variables that obtain a certain value of R2 is computationally hard, in the sense that this term is used in computer science.We discuss some of the implications of this result and of fact-free learning in general.

Original languageEnglish
Title of host publicationCase-Based Predictions
Subtitle of host publicationAn Axiomatic Approach to Prediction, Classification and Statistical Learning
PublisherTaylor and Francis
Pages185-210
Number of pages26
ISBN (Electronic)9789814366182
ISBN (Print)981436617X, 9789814366175
DOIs
StatePublished - 1 Jan 2012

Keywords

  • Bounded rationality
  • Complexity
  • Learning
  • Regression

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