Kolmogorov has defined the complexity of a sequence of bits to be the minimal size of (the description of) a Turing machine which can regenerate the given sequence. This paper contains two notes on possible applications of this complexity notion to philosophy in general and the philosophy of science in particular. The first presents simplicism--a theory prescribing that people would tend to choose the simplest theory to explain observations, where "simple" is defined by (a version of) Kolmogorov's measure. The second suggests a reinterpretation of a simple observation, saying that reality is almost surely too complex to understand, terms such as "good" and "evil" almost surely too complex to define, and so forth.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
file. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by Northwestern University, Center for Mathematical Studies in Economics and Management Science in its series Discussion Papers with number
923.
Length: Date of creation: Oct 1990 Date of revision: Handle: RePEc:nwu:cmsems:923
Contact details of provider: Postal: Center for Mathematical Studies in Economics and Management Science, Northwestern University, 580 Jacobs Center, 2001 Sheridan Road, Evanston, IL 60208-2014 Phone: 847/491-3527 Fax: 847/491-2530 Email: Web page: http://www.kellogg.northwestern.edu/research/math/ More information through EDIRC
Order Information: Email:
For technical questions regarding this item, or to correct its listing, contact: (Fran Walker).
Related research
Keywords:
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
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.
[Downloadable!]
Other versions:
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.
[Downloadable!]
Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2005.
"Fact-Free Learning,"
American Economic Review,
American Economic Association, vol. 95(5), pages 1355-1368, December.
[Downloadable!]