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Statistical physics of complex information dynamics.

Arsham Ghavasieh1,2, Carlo Nicolini3, Manlio De Domenico1

  • 1Fondazione Bruno Kessler, Via Sommarive 18, 38123 Povo (TN), Italy.

Physical Review. E
|December 17, 2020
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Summary
This summary is machine-generated.

This study introduces a unified statistical field theory for information dynamics in complex systems. This framework unifies diverse spreading processes and reveals how system modularity and hierarchy enhance functional diversity.

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Area of Science:

  • Complex Systems Science
  • Statistical Physics
  • Information Theory

Background:

  • Complex systems rely on information exchange for emergent phenomena like phase transitions and collective behaviors.
  • Existing models for information exchange (e.g., diffusion, random walks) lack a unified, physically grounded framework.
  • Understanding the link between microscopic interactions and macroscopic effects in information dynamics remains a challenge.

Purpose of the Study:

  • To develop a unified statistical field theory for information dynamics in complex systems.
  • To provide a framework for analyzing diverse information spreading processes on networks.
  • To establish a method for quantifying functional diversity in complex systems.

Main Methods:

  • Development of a statistical field theory for information dynamics.
  • Modeling information operators as a statistical ensemble.
  • Utilizing the density matrix formalism for analyzing complex dynamics.
  • Application of von Neumann entropy to measure functional diversity.

Main Results:

  • The proposed framework unifies various dynamical processes governing information evolution.
  • Information operators form a statistical ensemble, defining a density matrix for dynamic analysis.
  • Von Neumann entropy of the ensemble serves as a measure of functional diversity.
  • Modularity and hierarchy are identified as key features promoting functional diversity.

Conclusions:

  • The statistical field theory offers a unified approach to information dynamics in complex systems.
  • Functional diversity, measurable via von Neumann entropy, is crucial for system function.
  • System architectures featuring modularity and hierarchy are favored for maintaining functional diversity.