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Fact-Free LearningEnriqueta AragonesSpanish Council for Scientific Research (CSIC) - Insitute for Economic Analysis Itzhak GilboaYale University - Cowles Foundation; Tel Aviv University - Eitan Berglas School of Economics Andrew PostlewaiteUniversity of Pennsylvania - Department of Economics David SchmeidlerTel Aviv University October 2004 PIER Working Paper No. 03-023; Cowles Foundation Discussion Paper No. 1491 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.
Note: An updated version of this abstract can be found at: http://ssrn.com/abstract=643545 Number of Pages in PDF File: 35 Keywords: Computational complexity, Linear regression, Rule-based reasoning JEL Classification: C8, D8 working papers seriesDate posted: November 24, 2004Suggested CitationContact Information
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