Max Planck Institute for Dynamics and Self-Organization -- Department for Nonlinear Dynamics and Network Dynamics Group
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Friday, 07.06.2013 11 c.t.

Thermodynamics of Prediction

by Prof. Dr. Susanne Still
from Manoa Department of Information and Computer Sciences, University of Hawaiʻi, USA

Contact person: Annette Witt


Ludwig Prandtl lecture hall


I will discuss the fundamental equivalence between thermodynamic inefficiency, measured by dissipation, and information processing inefficiency, measured by nonpredictive information. The dynamics of any system responding to a stochastic environmental signal can be interpreted as computing an implicit model of the driving signal. The system’s state retains information about past environmental fluctuations, and a fraction of this information is predictive of future fluctuations. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the inefficiency of the model. We find that instantaneous nonpredictive information: 1) is proportional to the work dissipated during an environmental change; 2) provides a lower bound on the overall dissipation; 3) augments the lower bound on heat generated due to information erasure (Landauer's principle). Our results hold far from thermodynamic equilibrium and are thus applicable to a wide range of systems, including biomolecular machines, neurons, and potential future nano computing devices. These results 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 with maximal energetic efficiency, has to be predictive.

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