A Stochastic Complexity Perspective of Induction in Economics and Inference in Dynamics
AbstractRissanen'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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Bibliographic InfoPaper provided by National University of Ireland Galway, Department of Economics in its series Working Papers with number 0127.
Date of creation: 2007
Date of revision: 2007
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Other versions of this item:
- K. Vela Velupillai, 2007. "A Stochastic Complexity Perspective of Induction in Economics and Inference in Dynamics," Department of Economics Working Papers 0726, Department of Economics, University of Trento, Italia.
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