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Fact-Free Learning

Author

Listed:
  • Enriqueta Aragones

    (Autonomous University of Barcelona - Institut d'Anà lisi Econòmica (CSIC))

  • Itzhak Gilboa

    (Eitan Berglas School of Economics, Tel Aviv University)

  • Andrew Postlewaite

    (Department of Economics, University of Pennsylvania)

  • David Schmeidler

    (Eitan Berglas School of Economics, Tel Aviv University)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:pen:papers:05-002
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Learning; Behavioral Economics;

    JEL classification:

    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory

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