August  2019, 24(8): 4341-4366. doi: 10.3934/dcdsb.2019122

Stochastic dynamics Ⅱ: Finite random dynamical systems, linear representation, and entropy production

1. 

Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218-2608, USA

2. 

Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA

* Corresponding author: Felix X.-F. Ye

Received  April 2018 Revised  January 2019 Published  June 2019

We study finite state random dynamical systems (RDS) and their induced Markov chains (MC) as stochastic models for complex dynamics. The linear representation of deterministic maps in RDS is a matrix-valued random variable whose expectation corresponds to the transition matrix of the MC. The instantaneous Gibbs entropy, Shannon-Khinchin entropy of a step, and the entropy production rate of the MC are discussed. These three concepts, as key anchoring points in applications of stochastic dynamics, characterize respectively the uncertainties of a system at instant time $ t $, the randomness generated in a step in the dynamics, and the dynamical asymmetry with respect to time reversal. The stationary entropy production rate, expressed in terms of the cycle distributions, has found an expression in terms of the probability of the deterministic maps with single attractor in the maximum entropy RDS. For finite RDS with invertible transformations, the non-negative entropy production rate of its MC is bounded above by the Kullback-Leibler divergence of the probability of the deterministic maps with respect to its time-reversal dual probability.

Citation: Felix X.-F. Ye, Hong Qian. Stochastic dynamics Ⅱ: Finite random dynamical systems, linear representation, and entropy production. Discrete & Continuous Dynamical Systems - B, 2019, 24 (8) : 4341-4366. doi: 10.3934/dcdsb.2019122
References:
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A. Ben-Naim, Entropy and the Second Law: Interpretation and Misss-interpretationsss, World Scientific, 2012. doi: 10.1142/8333.  Google Scholar

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P. G. Bergmann and J. L. Lebowitz, New approach to nonequilibrium processes, Phys. Rev., 99 (1955), 578-587.  doi: 10.1103/PhysRev.99.578.  Google Scholar

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P. Bollobas, Modern Graph Theory, Graduate Texts in Mathematics, Springer New York, 1998. doi: 10.1007/978-1-4612-0619-4.  Google Scholar

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R. T. Cox, The statistical method of gibbs in irreversible change, Rev. Mod. Phys., 22 (1950), 238-248.  doi: 10.1103/RevModPhys.22.238.  Google Scholar

[11]

P. Diaconis and D. Freedman, Iterated random functions, SIAM Review, 41 (1999), 45-76.  doi: 10.1137/S0036144598338446.  Google Scholar

[12]

P. Gaspard and X.-J. Wang, Noise, chaos, and ($\epsilon$, $\tau$)-entropy per unit time, Phys. Rep., 235 (1993), 291-343.  doi: 10.1016/0370-1573(93)90012-3.  Google Scholar

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T. Hill, Free Energy Transduction and Biochemical Cycle Kinetics, Dover Books on Chemistry, Dover Publications, 2004. doi: 10.1007/978-1-4612-3558-3.  Google Scholar

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A. Hobson, A new theorem of information theory, Journal of Statistical Physics, 1 (1969), 383-391.  doi: 10.1007/BF01106578.  Google Scholar

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D.-Q. Jiang, M. Qian and M.-P. Qian, Mathematical Theory of Nonequilibrium Steady States: On the Frontier of Probability and Dynamical Systems, Lecture Notes in Mathematics, 1833. Springer-Verlag, Berlin, 2004. doi: 10.1007/b94615.  Google Scholar

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S. Kalpazidou, Cycle Representations of Markov Processes, Stochastic Modelling and Applied Probability, Springer, New York, 2006.  Google Scholar

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A. Khinchin, Mathematical Foundations of Information Theory, Translated by R. A. Silverman and M. D. Friedman. Dover Publications, Inc., New York, N. Y., 1957.  Google Scholar

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Y. Kifer, Random Perturbations of Dynamical Systems, Progress in Probability, Birkhäuser Boston, Inc., Boston, MA, 1988. doi: 10.1007/978-1-4615-8181-9.  Google Scholar

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E. L. King and C. Altman, A schematic method of deriving the rate laws for enzyme-catalyzed reactions, The Journal of Physical Chemistry, 60 (1956), 1375-1378.  doi: 10.1021/j150544a010.  Google Scholar

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A. N. Kolmogorov, Three approaches to the quantitative definition of information, International Journal of Computer Mathematics, 2 (1968), 157-168.  doi: 10.1080/00207166808803030.  Google Scholar

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A. Lasota and M. Mackey, Chaos, Fractals, and Noise: Stochastic Aspects of Dynamics, Second edition. Applied Mathematical Sciences, 97. Springer-Verlag, New York, 1994. doi: 10.1007/978-1-4612-4286-4.  Google Scholar

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[24]

J.-L. Luo, C. Van den Broeck and G. Nicolis, Stability criteria and fluctuations around nonequilibrium states, Zeitschrift für Physik B Condensed Matter, 56 (1984), 165–170. doi: 10.1007/BF01469698.  Google Scholar

[25]

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[26]

D. Mumford, The dawning age of stochasticity, in Mathematics: Frontiers and Perspectives (eds. V. Arnold, M. Atiyah, P. Lax and B. Mazur), 197–218, Amer. Math. Soc., Providence, RI, 2000.  Google Scholar

[27]

J. G. Propp and D. B. Wilson, Exact sampling with coupled Markov chains and applications to statistical mechanics, Random Structures & Algorithms, 9 (1996), 223-252.  doi: 10.1002/(SICI)1098-2418(199608/09)9:1/2<223::AID-RSA14>3.0.CO;2-O.  Google Scholar

[28]

H. Qian, A decomposition of irreversible diffusion processes without detailed balance, Journal of Mathematical Physics, 54 (2013), 053302, 10pp. doi: 10.1063/1.4803847.  Google Scholar

[29]

H. Qian, Nonlinear stochastic dynamics of complex systems, i: a chemical reaction kinetic perspective with mesoscopic nonequilibrium thermodynamics, arXiv: 1605.08070. Google Scholar

[30]

A. N. Quas, On representations of Markov chains by random smooth maps, Bulletin of the London Mathematical Society, 23 (1991), 487-492.  doi: 10.1112/blms/23.5.487.  Google Scholar

[31]

H. Risken, The Fokker-Planck Equation: Methods of Solution and Applications, Second edition. Springer Series in Synergetics, 18. Springer-Verlag, Berlin, 1989. doi: 10.1007/978-3-642-96807-5.  Google Scholar

[32]

D. Ruelle, Positivity of entropy production in nonequilibrium statistical mechanics, Journal of Statistical Physics, 85 (1996), 1-23.  doi: 10.1007/BF02175553.  Google Scholar

[33]

M. Scheutzow, Attractors for ergodic and monotone random dynamical systems, in Seminar on Stochastic Analysis, Random Fields and Applications V (eds. R. C. Dalang, F. Russo and M. Dozzi), Birkhäuser Basel, Basel, 59 (2008), 331–344. doi: 10.1007/978-3-7643-8458-6_18.  Google Scholar

[34]

J. Schnakenberg, Network theory of microscopic and macroscopic behavior of master equation systems, Rev. Mod. Phys., 48 (1976), 571-585.  doi: 10.1103/RevModPhys.48.571.  Google Scholar

[35]

U. Seifert, Entropy production along a stochastic trajectory and an integral fluctuation theorem, Phys. Rev. Lett., 95 (2005), 040602. doi: 10.1103/PhysRevLett.95.040602.  Google Scholar

[36]

A. Swishchuk and S. Islam, Random Dynamical Systems in Finance, Chapman and Hall/CRC, New York, 2013. doi: 10.1201/b14989.  Google Scholar

[37]

M. Tribus, Thermostatics and Thermodynamics: An Introduction to Energy, Information and States of Matter, with Engineering Applications, University series in basic engineering, Van Nostrand, 1961. Google Scholar

[38]

N. Van Kampen, Stochastic Processes in Physics and Chemistry, Lecture Notes in Mathematics, 888. North-Holland Publishing Co., Amsterdam-New York, 1981.  Google Scholar

[39]

J. Voigt, Stochastic operators, information, and entropy, Communications in Mathematical Physics, 81 (1981), 31-38.  doi: 10.1007/BF01941799.  Google Scholar

[40]

F. X.-F. Ye, Y.-A. Ma and H. Qian, Estimate exponential memory decay in hidden Markov model and its applications, arXiv: 1710.06078. Google Scholar

[41]

F. X.-F. YeY. Wang and H. Qian, Stochastic dynamics: Markov chains and random transformations, Discrete and Continuous Dynamical Systems - B, 21 (2016), 2337-2361.  doi: 10.3934/dcdsb.2016050.  Google Scholar

show all references

References:
[1]

H. Arbabi and I. Mezic, Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator, SIAM J. Applied Dynamical Systems, 16 (2017), 2096-2126.  doi: 10.1137/17M1125236.  Google Scholar

[2]

L. Arnold, Trends and open problems in the theory of random dynamical systems, in Probability Towards 2000 (eds. L. Accardi and C. C. Heyde), Springer New York, New York, NY, 1998, 34–46. doi: 10.1007/978-1-4612-2224-8_2.  Google Scholar

[3]

L. Arnold, Random Dynamical Systems, Springer Monographs in Mathematics, Springer-Verlag, Berlin, 1998. doi: 10.1007/978-3-662-12878-7.  Google Scholar

[4]

L. Arnold and H. Crauel, Random dynamical systems, in Lyapunov Exponents (Oberwolfach, 1990), vol. 1486 of Lecture Notes in Math., Springer, Berlin, 1991, 1–22. doi: 10.1007/BFb0086654.  Google Scholar

[5]

V. Baladi, Positive Transfer Operators and Decay of Correlations, Advanced series in nonlinear dynamics, World Scientific Publishing Company, 2000. doi: 10.1142/9789812813633.  Google Scholar

[6]

A. Ben-Naim, Entropy and the Second Law: Interpretation and Misss-interpretationsss, World Scientific, 2012. doi: 10.1142/8333.  Google Scholar

[7]

P. G. Bergmann and J. L. Lebowitz, New approach to nonequilibrium processes, Phys. Rev., 99 (1955), 578-587.  doi: 10.1103/PhysRev.99.578.  Google Scholar

[8]

P. Bollobas, Modern Graph Theory, Graduate Texts in Mathematics, Springer New York, 1998. doi: 10.1007/978-1-4612-0619-4.  Google Scholar

[9]

S. R. Caplan and D. Zeilberger, T. L. Hill's graphical method for solving linear equations, Advances in Applied Mathematics, 3 (1982), 377-383.  doi: 10.1016/S0196-8858(82)80011-0.  Google Scholar

[10]

R. T. Cox, The statistical method of gibbs in irreversible change, Rev. Mod. Phys., 22 (1950), 238-248.  doi: 10.1103/RevModPhys.22.238.  Google Scholar

[11]

P. Diaconis and D. Freedman, Iterated random functions, SIAM Review, 41 (1999), 45-76.  doi: 10.1137/S0036144598338446.  Google Scholar

[12]

P. Gaspard and X.-J. Wang, Noise, chaos, and ($\epsilon$, $\tau$)-entropy per unit time, Phys. Rep., 235 (1993), 291-343.  doi: 10.1016/0370-1573(93)90012-3.  Google Scholar

[13]

T. Hill, Free Energy Transduction and Biochemical Cycle Kinetics, Dover Books on Chemistry, Dover Publications, 2004. doi: 10.1007/978-1-4612-3558-3.  Google Scholar

[14]

A. Hobson, A new theorem of information theory, Journal of Statistical Physics, 1 (1969), 383-391.  doi: 10.1007/BF01106578.  Google Scholar

[15]

W. Huang, H. Qian, S. Wang, F. X.-F. Ye and Y. Yi, Synchronization in discrete-time, discrete-state random dynamical systems, In preparation. Google Scholar

[16]

D.-Q. Jiang, M. Qian and M.-P. Qian, Mathematical Theory of Nonequilibrium Steady States: On the Frontier of Probability and Dynamical Systems, Lecture Notes in Mathematics, 1833. Springer-Verlag, Berlin, 2004. doi: 10.1007/b94615.  Google Scholar

[17]

S. Kalpazidou, Cycle Representations of Markov Processes, Stochastic Modelling and Applied Probability, Springer, New York, 2006.  Google Scholar

[18]

A. Khinchin, Mathematical Foundations of Information Theory, Translated by R. A. Silverman and M. D. Friedman. Dover Publications, Inc., New York, N. Y., 1957.  Google Scholar

[19]

Y. Kifer, Random Perturbations of Dynamical Systems, Progress in Probability, Birkhäuser Boston, Inc., Boston, MA, 1988. doi: 10.1007/978-1-4615-8181-9.  Google Scholar

[20]

E. L. King and C. Altman, A schematic method of deriving the rate laws for enzyme-catalyzed reactions, The Journal of Physical Chemistry, 60 (1956), 1375-1378.  doi: 10.1021/j150544a010.  Google Scholar

[21]

A. N. Kolmogorov, Three approaches to the quantitative definition of information, International Journal of Computer Mathematics, 2 (1968), 157-168.  doi: 10.1080/00207166808803030.  Google Scholar

[22]

A. Lasota and M. Mackey, Chaos, Fractals, and Noise: Stochastic Aspects of Dynamics, Second edition. Applied Mathematical Sciences, 97. Springer-Verlag, New York, 1994. doi: 10.1007/978-1-4612-4286-4.  Google Scholar

[23]

K. K. Lin, Stimulus-response reliability of biological networks, in Nonautonomous Dynamical Systems in the Life Sciences (eds. P. E. Kloeden and C. Pötzsche), Springer International Publishing, Cham, 2013,135–161. doi: 10.1007/978-3-319-03080-7_4.  Google Scholar

[24]

J.-L. Luo, C. Van den Broeck and G. Nicolis, Stability criteria and fluctuations around nonequilibrium states, Zeitschrift für Physik B Condensed Matter, 56 (1984), 165–170. doi: 10.1007/BF01469698.  Google Scholar

[25]

Y.-A. MaH. Qian and F. X.-F. Ye, Stochastic dynamics: Models for intrinsic and extrinsic noises and their applications (in Chinese), Scientia Sinica Mathematica, 47 (2017), 1693-1702.   Google Scholar

[26]

D. Mumford, The dawning age of stochasticity, in Mathematics: Frontiers and Perspectives (eds. V. Arnold, M. Atiyah, P. Lax and B. Mazur), 197–218, Amer. Math. Soc., Providence, RI, 2000.  Google Scholar

[27]

J. G. Propp and D. B. Wilson, Exact sampling with coupled Markov chains and applications to statistical mechanics, Random Structures & Algorithms, 9 (1996), 223-252.  doi: 10.1002/(SICI)1098-2418(199608/09)9:1/2<223::AID-RSA14>3.0.CO;2-O.  Google Scholar

[28]

H. Qian, A decomposition of irreversible diffusion processes without detailed balance, Journal of Mathematical Physics, 54 (2013), 053302, 10pp. doi: 10.1063/1.4803847.  Google Scholar

[29]

H. Qian, Nonlinear stochastic dynamics of complex systems, i: a chemical reaction kinetic perspective with mesoscopic nonequilibrium thermodynamics, arXiv: 1605.08070. Google Scholar

[30]

A. N. Quas, On representations of Markov chains by random smooth maps, Bulletin of the London Mathematical Society, 23 (1991), 487-492.  doi: 10.1112/blms/23.5.487.  Google Scholar

[31]

H. Risken, The Fokker-Planck Equation: Methods of Solution and Applications, Second edition. Springer Series in Synergetics, 18. Springer-Verlag, Berlin, 1989. doi: 10.1007/978-3-642-96807-5.  Google Scholar

[32]

D. Ruelle, Positivity of entropy production in nonequilibrium statistical mechanics, Journal of Statistical Physics, 85 (1996), 1-23.  doi: 10.1007/BF02175553.  Google Scholar

[33]

M. Scheutzow, Attractors for ergodic and monotone random dynamical systems, in Seminar on Stochastic Analysis, Random Fields and Applications V (eds. R. C. Dalang, F. Russo and M. Dozzi), Birkhäuser Basel, Basel, 59 (2008), 331–344. doi: 10.1007/978-3-7643-8458-6_18.  Google Scholar

[34]

J. Schnakenberg, Network theory of microscopic and macroscopic behavior of master equation systems, Rev. Mod. Phys., 48 (1976), 571-585.  doi: 10.1103/RevModPhys.48.571.  Google Scholar

[35]

U. Seifert, Entropy production along a stochastic trajectory and an integral fluctuation theorem, Phys. Rev. Lett., 95 (2005), 040602. doi: 10.1103/PhysRevLett.95.040602.  Google Scholar

[36]

A. Swishchuk and S. Islam, Random Dynamical Systems in Finance, Chapman and Hall/CRC, New York, 2013. doi: 10.1201/b14989.  Google Scholar

[37]

M. Tribus, Thermostatics and Thermodynamics: An Introduction to Energy, Information and States of Matter, with Engineering Applications, University series in basic engineering, Van Nostrand, 1961. Google Scholar

[38]

N. Van Kampen, Stochastic Processes in Physics and Chemistry, Lecture Notes in Mathematics, 888. North-Holland Publishing Co., Amsterdam-New York, 1981.  Google Scholar

[39]

J. Voigt, Stochastic operators, information, and entropy, Communications in Mathematical Physics, 81 (1981), 31-38.  doi: 10.1007/BF01941799.  Google Scholar

[40]

F. X.-F. Ye, Y.-A. Ma and H. Qian, Estimate exponential memory decay in hidden Markov model and its applications, arXiv: 1710.06078. Google Scholar

[41]

F. X.-F. YeY. Wang and H. Qian, Stochastic dynamics: Markov chains and random transformations, Discrete and Continuous Dynamical Systems - B, 21 (2016), 2337-2361.  doi: 10.3934/dcdsb.2016050.  Google Scholar

Figure 1.  Random maps are generated via this Markov chain. This is the illustration of state transition diagram for the Markov chain
Figure 2.  A 3-state completely connected MC. The transition matrix of derived chain dynamics is in (34)
Figure 3.  A 4-state MC with initial state 1
Figure 4.  $ e(\mathcal{T}_1) = M_{21}M_{31}, e(\mathcal{T}_2) = M_{12}M_{31}, e(\mathcal{T}_3) = M_{21}M_{13} $
Table 1.  The derived chain $ \eta_t $ and the cycles formed for this sample trajectory $ X_t $ [16]
$ t $ 0 1 2 3 4 5 6 7 8
$ X_t(\omega) $ 2 1 3 1 3 1 1 2 2
$ \eta_t(\omega) $ [2] [2, 1] [2, 1, 3] [2, 1] [2, 1, 3] [2, 1] [2, 1] [2] [2]
$ cycles $ (1, 3) (3, 1) (1) (1, 2) (2)
$ t $ 0 1 2 3 4 5 6 7 8
$ X_t(\omega) $ 2 1 3 1 3 1 1 2 2
$ \eta_t(\omega) $ [2] [2, 1] [2, 1, 3] [2, 1] [2, 1, 3] [2, 1] [2, 1] [2] [2]
$ cycles $ (1, 3) (3, 1) (1) (1, 2) (2)
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