Generalising thermodynamic efficiency of interactions: inferential, information-geometric and computational perspectives

Abstract Self-organizing systems consume energy to generate internal order. The concept of thermodynamic efficiency, drawing from statistical physics and information theory, has previously been proposed to characterise a change in control parameter by relating the resulting predictability gain to the required amount of work. However, previous studies have taken a system-centric perspective and considered only single control parameters. Here, considering systems at or near equilibrium, we generalise thermodynamic efficiency to multiple control parameters and extend the definition of thermodynamic efficiency to protocols in arbitrary directions, by introducing directional efficiency . Taking an observer-centric perspective, we derive two novel formulations. The first, an inferential form , relates efficiency to fluctuations of macroscopic observables, interpreting thermodynamic efficiency in terms of how well the system parameters can be inferred from observable macroscopic behaviour. The second, an information-geometric form , expresses efficiency in terms of the Fisher information matrix, interpreting it with respect to how difficult it is to navigate the statistical manifold defined by the control protocol. This observer-centric perspective is contrasted with the existing system-centric view, where efficiency is considered an intrinsic property of the system.

Authors

Institutions

Publication Details

Journal
Journal of Physics Complexity
Published
2026-03-31
DOI
https://doi.org/10.1088/2632-072x/ae59e1
Primary Topic
Advanced Thermodynamics and Statistical Mechanics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generalising thermodynamic efficiency of interactions: inferential, information-geometric and computational perspectives

Qianyang Chen, Mikhail Prokopenko, Nihat Ay
Journal of Physics Complexity
Advanced Thermodynamics and Statistical Mechanics
article

Generalising thermodynamic efficiency of interactions: inferential, information-geometric and computational perspectives

Qianyang Chen, Mikhail Prokopenko, Nihat Ay
article en

Abstract

Abstract Self-organizing systems consume energy to generate internal order. The concept of thermodynamic efficiency, drawing from statistical physics and information theory, has previously been proposed to characterise a change in control parameter by relating the resulting predictability gain to the required amount of work. However, previous studies have taken a system-centric perspective and considered only single control parameters. Here, considering systems at or near equilibrium, we generalise thermodynamic efficiency to multiple control parameters and extend the definition of thermodynamic efficiency to protocols in arbitrary directions, by introducing directional efficiency . Taking an observer-centric perspective, we derive two novel formulations. The first, an inferential form , relates efficiency to fluctuations of macroscopic observables, interpreting thermodynamic efficiency in terms of how well the system parameters can be inferred from observable macroscopic behaviour. The second, an information-geometric form , expresses efficiency in terms of the Fisher information matrix, interpreting it with respect to how difficult it is to navigate the statistical manifold defined by the control protocol. This observer-centric perspective is contrasted with the existing system-centric view, where efficiency is considered an intrinsic property of the system.

Journal of Physics ComplexityVol. 7(2)
The University of Sydney (AU), Universität Hamburg (DE), UNSW Sydney (AU), Hamburg University of Technology (DE)
Affordable and clean energy
Openalex Percentile: Top 35%
Advanced Thermodynamics and Statistical Mechanics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.