Susanne Still, David A. Sivak, Anthony J. Bell, Gavin E. Crooks
A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an (implicit) model of the environmental variables. The system's state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by the energy dissipated during the interaction between system and environment. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate energetically efficiently has to be predictive.
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http://arxiv.org/abs/1203.3271
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