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.
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Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2005.
"Fact-Free Learning,"
American Economic Review,
American Economic Association, vol. 95(5), pages 1355-1368, December.
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Paper
Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2003.
"Fact-Free Learning,"
PIER Working Paper Archive
05-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Dec 2004.
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Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2003.
"Fact-Free Learning,"
PIER Working Paper Archive
03-023, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
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Find related papers by JEL classification: C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs D8 - Microeconomics - - Information, Knowledge, and Uncertainty
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Other versions:
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"Interactive Unawareness,"
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Other versions:
HEIFETZ, Aviad & MEIER, Martin & SCHIPPER, Burkhard C., 2004.
"Interactive unawareness,"
CORE Discussion Papers
2004059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
[Downloadable!]
Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2004.
"Rationality of Belief,"
Levine's Bibliography
122247000000000690, UCLA Department of Economics.
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