- Open Access
Reliability of a nonlinear fluctuation-dissipation relation as a test of Markovianity
Phys. Rev. E 113, 044130 – Published 23 April, 2026
DOI: https://doi.org/10.1103/485b-sb1x
Abstract
Markov processes are widely applied in modeling random phenomena, and the corresponding models can be analyzed within an established theoretical framework. It is thus desirable to have reliable tests for the (non-)Markovianity of a physical or biological system at hand. One such test, suggested by Engbring et al. [Phys. Rev. X 13, 021034 (2023)], is based on a nonlinear fluctuation-dissipation relation (NL-FDR) that compares spontaneous fluctuations with the response to (nonweak) external perturbations. A violation of this NL-FDR indicates non-Markovianity. However, estimating the relevant statistics for the variable conjugated to the perturbation from finite experimental data can be challenging. Comparing noisy estimates of the spontaneous and the response statistics can lead to apparent deviations from the prediction of the NL-FDR. So, when is a violation of the NL-FDR due to non-Markovian features of the system and when is it due to a finite sample size? We consider this question for the simplest Markovian example, the overdamped harmonic oscillator with thermal white noise and subject to a constant force perturbation. We derive analytical expressions for the variances of the estimators for the spontaneous correlation function and for the time-dependent mean response. We confirm previous findings that there is an optimal amplitude for the perturbation at which the test of non-Markovianity is most reliable. In terms of the original variable, the optimal amplitude is such that the response to the perturbation is of the order of one standard deviation of the spontaneous fluctuations. Put differently, the optimal amplitude neither corresponds to the linear-response regime of very weak perturbations nor to the regime of very strong perturbations where the response is largely deterministic. At large amplitudes, we encounter heavy-tailed distributions of certain estimators, which we discuss.
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References (32)
- H. Risken, The Fokker-Planck Equation, 2nd ed. (Springer, Berlin, 1989).
- N. G. van Kampen, Stochastic Processes in Physics and Chemistry, Revised and Enlarged ed. (North-Holland, Amsterdam, 1992).
- C. W. Gardiner, Handbook of Stochastic Methods, 2nd ed. (Springer-Verlag, Berlin, 1985).
- K. Engbring, D. Boriskovsky, Y. Roichman, and B. Lindner., A nonlinear fluctuation-dissipation test for Markovian systems, Phys. Rev. X 13, 021034 (2023).
- L. Willareth, I. M. Sokolov, Y. Roichman, and B. Lindner, Generalized fluctuation-dissipation theorem as a test of the Markovianity of a system, Europhys. Lett. 118, 20001 (2017).
- R. Satija, A. M. Berezhkovskii, and D. E. Makarov, Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes, Proc. Natl. Acad. Sci. USA 117, 27116 (2020).
- R. Zwanzig, Nonequilibrium Statistical Mechanics (Oxford University Press, Oxford, 2001).
- A. Lapolla and A. Godec, Manifestations of projection-induced memory: General theory and the tilted single file, Front. Phys. 7, 182 (2019).
- L. Onsager and S. Machlup, Fluctuations and irreversible processes, Phys. Rev. 91, 1505 (1953).
- A. Lapolla and A. Godec, Toolbox for quantifying memory in dynamics along reaction coordinates, Phys. Rev. Res. 3, L022018 (2021).
- A. M. Berezhkovskii and D. E. Makarov, Single-molecule test for Markovianity of the dynamics along a reaction coordinate, J. Phys. Chem. Lett. 9, 2190 (2018).
- K. Song, D. E. Makarov, and E. Vouga, Compression algorithms reveal memory effects and static disorder in single-molecule trajectories, Phys. Rev. Res. 5, L012026 (2023).
- L. Dinis, P. Martin, J. Barral, J. Prost, and J. F. Joanny, Fluctuation-response theorem for the active noisy oscillator of the hair-cell bundle, Phys. Rev. Lett. 109, 160602 (2012).
- G. Gradziuk, G. Torregrosa, and C. P. Broedersz, Irreversibility in linear systems with colored noise, Phys. Rev. E 105, 024118 (2022).
- S. Thapa, D. Zaretzky, R. Vatash, G. Gradziuk, C. Broedersz, Y. Shokef, and Y. Roichman, Nonequilibrium probability currents in optically-driven colloidal suspensions, SciPost Phys. 17, 096 (2024).
- J. Honerkamp, Stochastic Dynamical Systems. Concepts, Numerical Methods, Data Analysis (Wiley/VCH, Weinheim, 1993).
- M. S. Bartlett, On the theoretical specification and sampling properties of autocorrelated time-series, Supplement to the J. R. Stat. Soc. 8, 27 (1946).
- P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, 2nd ed., Springer Series in Statistics (Springer, New York, 1991).
- K. Neusser, Time Series Econometrics, Springer Texts in Business and Economics (Springer, Bern, 2016).
- M. S. Fadali, Introduction to Random Signals, Estimation Theory, and Kalman Filtering (Springer, Singapore, 2024).
- K. Weise and W. Wöger, Meßunsicherheit und Meßdatenauswertung (Wiley-VCH, Weinheim, 1999).
- C. Gardiner, Elements of Stochastic Methods (AIP Publishing, Melville, 2021).
- M. A. Pinsky and S. Karlin, An Introduction to Stochastic Modeling, 4th ed. (Academic Press, Burlington, 2011).
- R. Goerlich, B. Sorkin, D. Boriskovsky, L. B. Pires, B. Lindner, C. Genet, and Y. Roichman, Consistent thermodynamics reconstructed from transitions between nonequilibrium steady-states, arXiv:2601.03245.
- R. Goerlich, A. Tartar, Y. Roichman, and I. M. Sokolov, Fluctuation-response relation for a nonequilibrium system with resolved Markovian embedding, arXiv:2601.05198.
- G. Podhaisky, I. M. Sokolov, Y. Roichman, and B. Lindner, Markov test reliability, Zenodo (2025), https://doi.org/10.5281/zenodo.19349444.
- R. A. Maller, G. Müller, and A. Szimayer, Ornstein-Uhlenbeck processes and extensions, in Handbook of Financial Time Series, edited by T. G. Andersen, R. A. Davis, J.-P. Kreiß, and T. Mikosch (Springer, Berlin, 2009), pp. 421–437.
- A. G. Cherstvy, S. Thapa, Y. Mardoukhi, A. V. Chechkin, and R. Metzler, Time averages and their statistical variation for the Ornstein-Uhlenbeck process: Role of initial particle distributions and relaxation to stationarity, Phys. Rev. E 98, 022134 (2018).
- R. L. Stratonovich, Topics in the Theory of Random Noise. Volume I, Mathematics and its Applications (Gordon and Breach, New York, 1963), Vol. 3.
- G. Skoulakis, A recursive formula for computing central moments of a multivariate lognormal distribution, Am. Stat. 62, 147 (2008).
- K. F. Riley, M. P. Hobson, and S. J. Bence, Mathematical Methods for Physics and Engineering. A Comprehensive Guide, 2nd ed. (Cambridge University Press, Cambridge, 2002).
- Wolfram Research, Inc., Mathematica, Version 14.2, Champaign, IL (2025).