You are not currently logged in.
Access JSTOR through your library or other institution:
Enriqueta Aragones, Itzhak Gilboa, Andrew Postlewaite and David Schmeidler
The American Economic Review
Vol. 95, No. 5 (Dec., 2005), pp. 1355-1368
Published by: American Economic Association
Stable URL: http://www.jstor.org/stable/4132755
Page Count: 14
You can always find the topics here!Topics: Knowledge bases, Polynomials, Economic theory, Mathematical problems, Economic agents, Econometrics, Linear regression, Economic growth models, Economic modeling, Economic models
Were these topics helpful?See something inaccurate? Let us know!
Select the topics that are inaccurate.
Preview not available
People may be surprised to notice 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.
The American Economic Review © 2005 American Economic Association