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Open AccessArticle

Energy Dissipation and Information Flow in Coupled Markovian Systems

1
Department of Physics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
2
Department of Physics, University of California, Berkeley, CA 94720, USA
*
Authors to whom correspondence should be addressed.
Entropy 2018, 20(9), 707; https://doi.org/10.3390/e20090707
Received: 30 June 2018 / Revised: 11 September 2018 / Accepted: 13 September 2018 / Published: 14 September 2018
(This article belongs to the Special Issue Thermodynamics of Information Processing)
A stochastic system under the influence of a stochastic environment is correlated with both present and future states of the environment. Such a system can be seen as implicitly implementing a predictive model of future environmental states. The non-predictive model complexity has been shown to lower-bound the thermodynamic dissipation. Here we explore these statistical and physical quantities at steady state in simple models. We show that under quasi-static driving this model complexity saturates the dissipation. Beyond the quasi-static limit, we demonstrate a lower bound on the ratio of this model complexity to total dissipation, that is realized in the limit of weak driving. View Full-Text
Keywords: work; dissipation; quasi-static; information; prediction; learning; nostalgia work; dissipation; quasi-static; information; prediction; learning; nostalgia
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MDPI and ACS Style

Quenneville, M.E.; Sivak, D.A. Energy Dissipation and Information Flow in Coupled Markovian Systems. Entropy 2018, 20, 707. https://doi.org/10.3390/e20090707

AMA Style

Quenneville ME, Sivak DA. Energy Dissipation and Information Flow in Coupled Markovian Systems. Entropy. 2018; 20(9):707. https://doi.org/10.3390/e20090707

Chicago/Turabian Style

Quenneville, Matthew E.; Sivak, David A. 2018. "Energy Dissipation and Information Flow in Coupled Markovian Systems" Entropy 20, no. 9: 707. https://doi.org/10.3390/e20090707

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