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A Stochastic Complexity Perspective of Induction in Economics and Inference in Dynamics |
Velupillai, K. Vela
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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.
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Keyword(s):
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Economics |
Publication Date:
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2007 |
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Type:
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Working paper |
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Peer-Reviewed:
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Yes |
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Language(s):
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English |
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Institution:
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NUI Galway |
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Citation(s):
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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. |
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Publisher(s):
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National University of Ireland, Galway |
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File Format(s):
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application/pdf |
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First Indexed:
2010-05-11 11:10:35 Last Updated:
2010-11-26 09:21:02 |
