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Mutual Information Disentangles Interactions from Changing Environments.

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This study introduces a new method to separate internal system interactions from environmental influences using mutual information. This approach helps distinguish complex system dynamics from external environmental changes.

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Area of Science:

  • Complex Systems
  • Statistical Physics
  • Information Theory

Background:

  • Real-world systems exhibit complex internal interactions.
  • These systems are influenced by dynamic, unobserved environments acting as couplings.
  • Distinguishing internal dynamics from environmental effects is challenging.

Purpose of the Study:

  • To develop a method for disentangling internal system couplings from environmental couplings.
  • To introduce a paradigmatic interacting model in a switching environment.
  • To provide a general approach for discriminating complex internal interactions from environmental changes.

Main Methods:

  • Introduction of a paradigmatic interacting model.
  • Analysis of a system in a switching, unobserved environment.
  • Utilizing limiting properties of mutual information.

Main Results:

  • Mutual information properties allow disentangling of internal and environmental couplings.
  • The method effectively separates system dynamics from environmental influences.
  • Demonstration of a general approach for discrimination.

Conclusions:

  • The proposed method successfully disentangles internal and environmental couplings.
  • This approach offers a general framework for analyzing complex systems in dynamic environments.
  • The findings contribute to understanding system-environment interactions and developing robust analytical tools.