Fact-Free Learning

Enriqueta Aragones, Itzhak Gilboa, Andrew Postlewaite and David Schmeidler

No 1491, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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 database, finding a small set of variables that obtain a certain value of R^2 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.

Keywords: Computational complexity; Linear regression; Rule-based reasoning (search for similar items in EconPapers)
JEL-codes: C8 D8 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2004-11
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Published in American Economic Review (2005), 95: 1355-1368

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Related works:
Chapter: Fact-Free Learning (2012)
Journal Article: Fact-Free Learning (2005)
Working Paper: Fact-Free Learning (2005)
Working Paper: Fact-Free Learning (2004)
Working Paper: Fact-Free Learning (2003)
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