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Measuring information transfer

Schreiber1

  • 1Max Planck Institute for the Physics of Complex Systems, Nothnitzer Strasse 38, 01187 Dresden, Germany.

Physical Review Letters
|September 16, 2000
PubMed
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We introduce transfer entropy, a new measure to quantify statistical coherence between systems. This method distinguishes true information exchange from shared information due to common influences, revealing interaction dynamics.

Area of Science:

  • Information theory
  • Statistical physics
  • Complex systems analysis

Background:

  • Standard measures like time-delayed mutual information struggle to differentiate true information exchange from shared information arising from common histories or external inputs.
  • Understanding directed information flow is crucial for analyzing complex systems.

Purpose of the Study:

  • To derive a novel information-theoretic measure for quantifying statistical coherence between time-evolving systems.
  • To develop a method that can distinguish genuine information transfer from spurious correlations due to common influences.

Main Methods:

  • Development of a new information-theoretic measure based on conditioning transition probabilities.
  • Exclusion of influences from common history and input signals through appropriate conditioning.

Related Experiment Videos

  • Application of the measure to identify driving and responding elements within interacting subsystems.

    • Main Results:

    • The proposed measure, transfer entropy, effectively quantifies statistical coherence between systems.
    • It successfully distinguishes between information that is actually exchanged and information shared due to common factors.
    • The method accurately identifies driving and responding components and detects asymmetric interactions.

    Conclusions:

    • Transfer entropy provides a robust tool for analyzing directed information flow in complex systems.
    • This measure enhances our ability to understand the causal relationships and interaction asymmetries between subsystems.
    • The findings have implications for fields requiring the analysis of dynamic system interactions.