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Fact-Free Learning

Author

Listed:
  • Itzhak Gilboa

    (TAU - Tel Aviv University)

  • Enriqueta Aragones
  • Andrew Postlewaite
  • David Schmeidler
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 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.

Suggested Citation

  • Itzhak Gilboa & Enriqueta Aragones & Andrew Postlewaite & David Schmeidler, 2005. "Fact-Free Learning," Post-Print hal-00481243, HAL.
    • Handle: RePEc:hal:journl:hal-00481243
    DOI: 10.1257/000282805775014308
    as

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    References listed on IDEAS

    as
    1. La Porta, Rafael & Lopez-de-Silanes, Florencio & Shleifer, Andrei & Vishny, Robert, 1999. "The Quality of Government," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 15(1), pages 222-279, April.
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    More about this item

    Keywords

    Computational complexity; Linear regression; Rule-based reasoning;
    All these keywords.

    JEL classification:

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

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