AbstractPeople 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.
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Bibliographic InfoPaper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 05-002.
Length: 32 pages
Date of creation: 01 Oct 2003
Date of revision: 01 Dec 2004
Learning; Behavioral Economics;
Other versions of this item:
- 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.
- Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2004. "Fact-Free Learning," Cowles Foundation Discussion Papers 1491, Cowles Foundation for Research in Economics, Yale University.
- D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-01-09 (All new papers)
- NEP-CBE-2005-01-09 (Cognitive & Behavioural Economics)
- NEP-CMP-2005-01-09 (Computational Economics)
- NEP-EDU-2005-01-09 (Education)
- NEP-EVO-2005-01-09 (Evolutionary Economics)
- NEP-MIC-2005-01-09 (Microeconomics)
- NEP-PKE-2005-01-09 (Post Keynesian Economics)
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