Takahiro Sagawa
Title
Information Thermodynamics on Causal Networks
Abstract
Nonequilibrium thermodynamics of information processing by "Maxwell's demon" has recently been a hot topic in both terms of theory [1,2] and experiment [3,4]. A comprehensive theoretical framework of information thermodynamics has been established [5], which combines nonequilibrium thermodynamics and information theory. However, this theoretical framework was only applicable to simple information processing that mainly consists of measurement and feedback control between only two systems (i.e., the engine and the demon).
In this talk, I will discuss a much more general theory of thermodynamics of information processing: information thermodynamics on causal networks [6]. It is applicable to complex nonequilibrium dynamics with information flows induced by interactions between multiple fluctuating systems. Characterizing the dynamics by causal networks (Bayesian networks), we obtain novel generalizations of the second law of thermodynamics and the fluctuation theorem, which include an informational quantity called transfer entropy [7]. Our result implies that the entropy production in a single system in the presence of multiple other systems is bounded by the information flow between these systems. I will also show that our general theory is useful to characterize the robustness of biological signal transduction inside cells [8].
[1] T. Sagawa and M. Ueda, Phys. Rev. Lett. 104, 090602 (2010).
[2] T. Sagawa and M. Ueda, Phys. Rev. Lett. 109, 180602 (2012).
[3] S. Toyabe, T. Sagawa, M. Ueda, E. Muneyuki, and M. Sano, Nature Physics 6, 988 (2010).
[4] J. V. Koski, V. F. Maisi, T. Sagawa, J. P. Pekola, Phys. Rev. Lett. 113, 030601 (2014).
[5] T. Sagawa and M. Ueda, New J. Phys. 15, 125012 (2013).
[6] S. Ito and T. Sagawa, Phys. Rev. Lett. 111, 180603 (2013).
[7] T. Schreiber, Phys. Rev. Lett. 85, 461 (2000).
[8] S. Ito and T. Sagawa, arXiv:1406.5810 (2014).