Spanish Council for Scientific Research (CSIC) - Insitute for Economic Analysis
Yale University - Cowles Foundation; Tel Aviv University - Eitan Berglas School of Economics
University of Pennsylvania - Department of Economics
Tel Aviv University
PIER Working Paper No. 03-023; Cowles Foundation Discussion Paper No. 1491
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, D8working papers series
Date posted: November 24, 2004
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