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A Stochastic Complexity Perspective of Induction in Economics and Inference in Dynamics
Velupillai, K. Vela
Rissanen's fertile and pioneering minimum description length principle (MDL) has been viewed from the point of view of statistical estimation theory, information theory, as stochastic complexity theory - i.e., a computable approximation of Kolomogorov Complexity - or Solomonoff's recursion theoretic induction principle or as analogous to Kolmogorov's sufficient statistics. All these - and many more - interpretations are valid, interesting and fertile. In this paper I view it from two points of view: those of an algorithmic economist and a dynamical system theorist. From these points of view I suggest, first, a recasting of Jevon's sceptical vision of induction in the light of MDL; and a complexity interpretation of an undecidable question in dynamics.
Keyword(s): Economics
Publication Date:
2007
Type: Working paper
Peer-Reviewed: Yes
Language(s): English
Institution: NUI Galway
Citation(s): Velupillai, K. V., (2007) "A Stochastic Complexity Perspective of Induction in Economics and Inference in Dynamics" (Working Paper No. 0127) Department of Economics, National University of Ireland, Galway.
Publisher(s): National University of Ireland, Galway
File Format(s): application/pdf
First Indexed: 2010-05-11 11:10:35 Last Updated: 2010-11-26 09:21:02