Life efficiency does not always increase with the dissipation rate

Baiesi, Marco; Maes, Christian

doi:10.1088/2399-6528/aab654

Cited by 28 publications

(18 citation statements)

References 78 publications

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“…[25] are less tight than ours is that they are a consequence of uncertainty relations, which for this molecular motor model do not yield a particularly sharp prediction of the efficiency, as discussed in Ref. [30].…”

Section: Discussioncontrasting

confidence: 62%

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Degree of coupling and efficiency of energy converters far-from-equilibrium

Vroylandt¹,

Lacoste²,

Verley³

2018

J. Stat. Mech.

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In this paper, we introduce a real symmetric and positive semi-definite matrix, which we call the non-equilibrium conductance matrix, and which generalizes the Onsager response matrix for a system in a non-equilibrium stationary state. We then express the thermodynamic efficiency in terms of the coefficients of this matrix using a parametrization similar to the one used near equilibrium. This framework, then valid arbitrarily far from equilibrium allows to set bounds on the thermodynamic efficiency by a universal function depending only on the degree of coupling between input and output currents. It also leads to new general power-efficiency trade-offs valid for macroscopic machines that are compared to trade-offs previously obtained from uncertainty relations. We illustrate our results on an unicycle heat to heat converter and on a discrete model of molecular motor.

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Section: Discussioncontrasting

confidence: 62%

“…This allows to connect the edge, cycle and physical affinities through dual forms of Eqs. (29)(30) [11,19]:…”

Section: Physical Cycle and Edge Currents And Affinitiesmentioning

confidence: 99%

Degree of coupling and efficiency of energy converters far-from-equilibrium

Vroylandt¹,

Lacoste²,

Verley³

2018

J. Stat. Mech.

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“…In principle, it is possible to quantify the "amount of nonequilibrium" without making reference to an equilibrium distribution, in particular by measuring the amount of probability flux in a system (e.g., instantaneous entropy production [131,132] or the norm of the probability fluxes [133,134]). However, there is not necessarily a clear relationship between the amount of probability flux and the capacity of a system to carry out self-maintenance functions [135]. We leave exploration of these alternative viability functions for future work.…”

Section: The Viability Functionmentioning

confidence: 99%

Semantic information, autonomous agency and non-equilibrium statistical physics

2018

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Shannon information theory provides various measures of so-called "syntactic information", which reflect the amount of statistical correlation between systems. In contrast, the concept of "semantic information" refers to those correlations which carry significance or "meaning" for a given system. Semantic information plays an important role in many fields, including biology, cognitive science, and philosophy, and there has been a long-standing interest in formulating a broadly applicable and formal theory of semantic information. In this paper we introduce such a theory. We define semantic information as the syntactic information that a physical system has about its environment which is causally necessary for the system to maintain its own existence. "Causal necessity" is defined in terms of counter-factual interventions which scramble correlations between the system and its environment, while "maintaining existence" is defined in terms of the system's ability to keep itself in a low entropy state. We also use recent results in nonequilibrium statistical physics to analyze semantic information from a thermodynamic point of view. Our framework is grounded in the intrinsic dynamics of a system coupled to an environment, and is applicable to any physical system, living or otherwise. It leads to formal definitions of several concepts that have been intuitively understood to be related to semantic information, including "value of information", "semantic content", and "agency".2Much of our approach can also be used to quantify semantic information in any dynamical system, not just physical systems. For the purposes of this paper, however, we focus our attention on physical systems.

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“…Several results suggest that entropy production is not sufficient for characterizing the physics of systems far from equilibrium [38], as it is the case for life processes [39]. Dissipation determines the response of equilibrium systems but far from equilibrium we need to take into account also non-dissipative aspects [11,15,40].…”

mentioning

confidence: 99%

Kinetic uncertainty relation

Terlizzi

Baiesi

2018

J. Phys. A: Math. Theor.

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105

103

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Relative fluctuations of observables in discrete stochastic systems are bounded at all times by the mean dynamical activity in the system, quantified by the mean number of jumps. This constitutes a kinetic uncertainty relation that is fundamentally different from the thermodynamic uncertainty relation recently discussed in the literature. The thermodynamic constraint is more relevant close to equilibrium while the kinetic constraint is the limiting factor of the precision of a observables in regimes far from equilibrium. This is visualized for paradigmatic simple systems and with an example of molecular motor dynamics. Our approach is based on the recent fluctuation response inequality by Dechant and Sasa [arXiv:1804.08250] and can be applied to generic Markov jump systems, which describe a wide class of phenomena and observables, including the irreversible predator-prey dynamics that we use as an illustration.

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Life efficiency does not always increase with the dissipation rate

Cited by 28 publications

References 78 publications

Degree of coupling and efficiency of energy converters far-from-equilibrium

Degree of coupling and efficiency of energy converters far-from-equilibrium

Semantic information, autonomous agency and non-equilibrium statistical physics

Kinetic uncertainty relation

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