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Information decomposition in complex systems via machine learning.

Kieran A Murphy1, Dani S Bassett1,2,3,4,5,6

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Proceedings of the National Academy of Sciences of the United States of America
|March 18, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method to break down complex system information. It identifies key variations relevant to macroscopic behavior, making complex system analysis more practical.

Keywords:
amorphous plasticitycomplex systemsinformation theorymachine learning for science

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Area of Science:

  • Complex Systems Science
  • Information Theory
  • Machine Learning

Background:

  • Understanding complex systems requires linking component-level variations to macroscopic behavior.
  • Mutual information connects variations across scales but is computationally challenging to analyze.
  • Current methods struggle with information distribution across numerous measurements.

Purpose of the Study:

  • To develop a practical and general methodology for decomposing information in complex systems.
  • To identify variations most relevant to macroscale behavior using machine learning.
  • To make information decomposition feasible for large datasets.

Main Methods:

  • Proposed a machine learning methodology for information decomposition.
  • Utilized a distributed information bottleneck as a learning objective.
  • Applied the method to a Boolean circuit and an amorphous material undergoing plastic deformation.

Main Results:

  • Successfully decomposed the entropy of system states in both studied complex systems.
  • Identified variations most relevant to macroscale behavior, bit by bit.
  • Demonstrated the practicality of information decomposition for large-scale data.

Conclusions:

  • The developed methodology offers a practical approach to information decomposition in complex systems.
  • This method facilitates the study of micro- to macroscale connections.
  • It advances the ability to identify meaningful variations in complex data relevant to system behavior.