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Analyzing Market Economies From the Perspective of Information Production, Policy, and Self-organized Equilibrium

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  • Dominique, C-Rene

Abstract

A modern market economy is an exceedingly complex, infinite-dimensional, stochastic dynamical system. The failure of mainstream economists to characterize its dynamics may well be due to its intractability. This paper argues that the characterization of its dynamics becomes almost trivial when it is analyzed from the perspective of information production. Whether its Jacobian matrix is specifiable or not, a Lyapunov spectrum can be constructed from which the potential Kolmogorov-Sinai or Shannon entropy can be assessed. But, a self-organized equilibrium must first obtain, and for that a suitable policy must be operational.

Suggested Citation

  • Dominique, C-Rene, 2016. "Analyzing Market Economies From the Perspective of Information Production, Policy, and Self-organized Equilibrium," MPRA Paper 70725, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:70725
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    References listed on IDEAS

    as
    1. Cornelis A. Los, 2000. "Visualization of Chaos for Finance Majors," School of Economics and Public Policy Working Papers 2000-07, University of Adelaide, School of Economics and Public Policy.
    2. Medio,Alfredo & Gallo,Giampaolo, 1995. "Chaotic Dynamics," Cambridge Books, Cambridge University Press, number 9780521484619.
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    More about this item

    Keywords

    Complexity; Kolmogorov-Sinai entropy; Shannon entropy; Lyapunov spectrum; Lyapunov dimension; Efficient policy; Self-organized equilibrium.;
    All these keywords.

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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