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 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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- Aragones, Enriqueta & Gilboa, Itzhak & Postlewaite, Andrew & Schmeidler, David, 2014.
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- Enriqueta Aragonès & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2013. "Rhetoric and Analogies," Working Papers 706, Barcelona Graduate School of Economics.
- Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2013. "Rhetoric and Analogies," PIER Working Paper Archive 13-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
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19452, Harvard University OpenScholar.
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- repec:cup:cbooks:9780521802345 is not listed on IDEAS
- Dekel, Eddie & Lipman, Barton L. & Rustichini, Aldo, 1998. "Recent developments in modeling unforeseen contingencies," European Economic Review, Elsevier, vol. 42(3-5), pages 523-542, May.
- Bray, Margaret M & Savin, Nathan E, 1986. "Rational Expectations Equilibria, Learning, and Model Specification," Econometrica, Econometric Society, vol. 54(5), pages 1129-60, September.
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