Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Statistical Inference of the Principle of Maximum Entropy Production

Version 1 : Received: 28 February 2021 / Approved: 2 March 2021 / Online: 2 March 2021 (16:53:04 CET)

How to cite: Veening, M. A Statistical Inference of the Principle of Maximum Entropy Production. Preprints 2021, 2021030110 (doi: 10.20944/preprints202103.0110.v1). Veening, M. A Statistical Inference of the Principle of Maximum Entropy Production. Preprints 2021, 2021030110 (doi: 10.20944/preprints202103.0110.v1).

Abstract

The maximization of entropy S within a closed system is accepted as an inevitability (as the second law of thermodynamics) by statistical inference alone. The Maximum Entropy Production Principle (MEPP) states that not only S maximizes, but $\dot{S}$ as well: a system will dissipate as fast as possible. There is still no consensus on the general validity of this MEPP, even though it shows remarkable explanatory power (both qualitatively and quantitatively), and has been empirically demonstrated for many domains. In this theoretical paper I provide a generalization of entropy gradients, to show that the MEPP actually follows from the same statistical inference, as that of the 2nd law of thermodynamics. For this generalization I only use the concepts of super-statespaces and microstate-density. These concepts also allow for the abstraction of 'Self Organizing Criticality' to a bifurcating local difference in this density, and allow for a generalization of the fundamentally unresolved concepts of 'chaos' and 'order'.

Subject Areas

entropy production maximization; complexity; thermodynamics; nonlinear dynamics; statistical mechanics; autopoiesis; entropy; entropy production; life

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