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Fact-Free Learning

Author

Listed:
  • Enriqueta Aragones

    (Institute d'Analisi Economica, CSIC)

  • Itzhak Gilboa

    (School of Economics, Tel Aviv University)

  • Andrew Postlewaite

    (Economics, University of Pennsylvania)

  • David Schmeidler

    (School of Mathematical Sciences, Tel Aviv Univ.)

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

  • 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.
    • Handle: RePEc:cwl:cwldpp:1491
    as

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    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d14/d1491.pdf
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    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, 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.
    • Itzhak Gilboa & Enriqueta Aragones & Andrew Postlewaite & David Schmeidler, 2005. "Fact-Free Learning," Post-Print hal-00481243, HAL.

    References listed on IDEAS

    as
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    3. Aragones, Enriqueta & Gilboa, Itzhak & Postlewaite, Andrew & Schmeidler, David, 2014. "Rhetoric and analogies," Research in Economics, Elsevier, vol. 68(1), pages 1-10.
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    6. Itzhak Gilboa, 1990. "Philosophical Applications of Kolmogorov's Complexity Measure," Discussion Papers 923, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    7. Dekel, Eddie & Lipman, Barton L & Rustichini, Aldo, 2001. "Representing Preferences with a Unique Subjective State Space," Econometrica, Econometric Society, vol. 69(4), pages 891-934, July.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

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

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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