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dc.contributor.authorVelupillai, K. Velaen
dc.identifier.citationVelupillai, 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.en
dc.description.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.en
dc.publisherNational University of Ireland, Galwayen
dc.relation.ispartofseriesworking papers;0127en
dc.titleA Stochastic Complexity Perspective of Induction in Economics and Inference in Dynamicsen
dc.typeWorking Paperen

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