Information flow and entropy production on Bayesian networks

Abstract: In this article, we review a general theoretical framework of thermodynamics of information on the basis of Bayesian networks. This framework can describe a broad class of nonequilibrium dynamics of multiple interacting systems with complex information exchanges. For such situations, we discuss a generalization of the second law of thermodynamics including information contents. The key concept here is an informational quantity called the transfer entropy, which describes the directional information transfer in stochastic dynamics. The generalized second law gives the fundamental lower bound of the entropy production in nonequilibrium dynamics, and sheds modern light on the paradox of "Maxwell's demon" that performs measurements and feedback control at the level of thermal fluctuations.