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Area of Science:

  • Complex Systems Science
  • Information Theory
  • Computational Neuroscience

Background:

  • Partial Information Decomposition (PID) is a key tool for analyzing complex systems.
  • Current PID methods have limitations, requiring source/target assignments and specific mutual information structures.

Purpose of the Study:

  • To introduce a generalized information decomposition (GID) that removes source/target distinctions.
  • To extend information analysis to arbitrary prior-posterior updates.
  • To explore novel insights into higher-order synergies and their relationship with system complexity.

Main Methods:

  • Developed a GID based on the decomposition of Kullback-Leibler divergence.
  • Applied GID to information-theoretic measures expressible as linear combinations of KL divergences.
  • Investigated the relationship between synergistic information and Tononi-Sporns-Edelman (TSE) complexity.

Main Results:

  • The GID accommodates a broader range of information-theoretic measures, including total correlation and negentropy.
  • Demonstrated that synergistic information is intrinsically linked to TSE complexity.
  • Showed that high synergistic information necessitates a balance between integration and segregation, similar to high TSE complexity.

Conclusions:

  • The GID offers a more flexible framework for analyzing information in complex systems.
  • This approach provides a deeper understanding of higher-order interactions and synergistic information.
  • The findings suggest potential for new empirical applications in understanding complex system dynamics.