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)
New Economics Papers: this item is included in nep-cbe, nep-cmp, nep-edu, nep-evo, nep-mic and nep-pke
Date: Written 2003-10-01
View list of references View citations in EconPapers

Downloads: (external link)
http://www.econ.upenn.edu/Centers/pier/Archive/05-002.pdf (application/pdf)

Related works:
Working Paper: Fact-Free Learning (2004)
Working Paper: Fact-Free Learning (2003)
Journal Article: Fact-Free Learning (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Access Statistics for this paper

More papers in PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania
Address: 3718 Locust Walk, Philadelphia, PA 19104
Contact information at EDIRC.
Series data maintained by Dolly Guarini ().