Intrinsic Transfer Entropy

ORAL

Abstract

Quantifying information flow within a system is paramount to understanding its behavior. One common, though flawed, method of doing this is via the \emph{transfer entropy}. The transfer entropy is a particular form of conditional mutual information, which captures both \emph{intrinsic dependence} between variables as well as \emph{conditional dependence}. Here, we propose a new method of quantifying information flow, the \emph{intrinsic transfer entropy}. Rather than utilizing the conditional mutual information, intrinsic transfer entropy uses the \emph{intrinsic mutual information} from information-theoretic cryptography. This provides for the first time a concrete method of separately quantifying intrinsic information flow from conditional information flow. We apply this measure to a variety of systems to demonstrate its usefulness.

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Authors

  • Ryan James

    Univ of California - Davis

  • C. Jennings

    Stanford University, None, none, Univ of California - Merced, Dr. Remeis-Sternwarte & ECAP, Universität Erlangen-Nürnberg, NASA GSFC, Lawrence Livermore Natl Lab, Lawrence Livermore National Laboratory, Goddard Space Flight Center-NASA, University of Nevada, Reno, Cal Poly - San Luis Obispo, Victoria University of Wellington, Wellington 6021, New Zealand, Penn State, Micron School of Materials Science & Engineering, Department of Physics, Boise State University, Autonomous University of Zacatecas, Micron School of Materials Science & Engineering, Boise State University, Naval Research Laboratory, Independent Researcher, Cornell University, UC Santa Cruz, Middle Georgia State University, University of California, Merced, Stanford University, California, Institute of Medical Biology, Singapore; Stanford University, California, University of California Merced, Kent State University, ORNL, UC Berkeley, LLNL, Physics, Hokkaido University, Japan, Physics, UC San Diego, Physics, California State University, Fresno, Department of Physics and Astronomy, California State University Long Beach, Long Beach, California 90840, USA, Department of Physics, Virginia Tech, Blacksburg, Virginia 24061, USA, Department of Physics, Southern University of Science and Technology, Shenzen 518055, China, Virginia Tech, Sotera Defense Solutions, Inc., Univ of California - Santa Cruz, College of Staten Island, UC Irvine, Naval Research Laboratory, Washington, DC 20375, Virginia Tech, Blacksburg, VA 24061, University of California, Merced CA 95343

  • James Crutchfield

    Univ of California - Davis