Causal Information Rate

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Abstract

Information processing is common in complex systems, and information geometric theory provides a useful tool to elucidate the characteristics of non-equilibrium processes, such as rare, extreme events, from the perspective of geometry. In particular, their time-evolutions can be viewed by the rate (information rate) at which new information is revealed (a new statistical state is accessed). In this paper, we extend this concept and develop a new information-geometric measure of causality by calculating the effect of one variable on the information rate of the other variable. We apply the proposed causal information rate to the Kramers equation and compare it with the entropy-based causality measure (information flow). Overall, the causal information rate is a sensitive method for identifying causal relations.
Original languageEnglish
Article number1087
Number of pages20
JournalEntropy
Volume23
Issue number8
DOIs
Publication statusPublished - 21 Aug 2021

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Keywords

  • information geometry
  • information length
  • information rate
  • causality
  • abrupt events
  • entropy
  • Information geometry
  • Information length
  • Abrupt events
  • Information rate
  • Entropy
  • Causality

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)

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