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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Causal Information Rate.

Eun-Jin Kim1, Adrian-Josue Guel-Cortez1

  • 1Center for Fluid and Complex Systems, Coventry University, Priory St., Coventry CV1 5FB, UK.

Entropy (Basel, Switzerland)
|August 27, 2021
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Summary

We introduce a new information-geometric measure of causality to analyze complex systems. This causal information rate effectively identifies causal relationships by examining how one variable influences another

Keywords:
abrupt eventscausalityentropyinformation geometryinformation lengthinformation rate

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Area of Science:

  • Complex systems analysis
  • Information geometry
  • Non-equilibrium thermodynamics

Background:

  • Information processing is fundamental in complex systems.
  • Information geometric theory offers geometric insights into non-equilibrium processes, including extreme events.
  • Time evolution in these systems can be understood by the rate of new information revealed.

Purpose of the Study:

  • To extend the concept of information rate.
  • To develop a novel information-geometric measure of causality.
  • To quantify the causal influence of one variable on the information rate of another.

Main Methods:

  • Developing a new information-geometric measure of causality.
  • Calculating the effect of one variable on the information rate of another.
  • Applying the causal information rate to the Kramers equation.

Main Results:

  • The proposed causal information rate was applied to the Kramers equation.
  • Comparison with the entropy-based causality measure (information flow) was performed.
  • The causal information rate demonstrated sensitivity in identifying causal relations.

Conclusions:

  • The causal information rate is a sensitive and effective method for identifying causal relationships in complex systems.
  • This new measure extends the application of information geometry to causality.
  • The approach provides a valuable tool for understanding non-equilibrium processes.