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Fact-Free Learning

Enriqueta Aragones, Itzhak Gilboa, Andrew Postlewaite and David Schmeidler

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

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.

Keywords: Learning; Behavioral Economics (search for similar items in EconPapers)
JEL-codes: D11 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2003-10-01, Revised 2004-12-01
New Economics Papers: this item is included in nep-cbe, nep-cmp, nep-edu, nep-evo, nep-mic and nep-pke
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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