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Information theory quantifies complex system interactions. Analyzing random walkers reveals information measures align best with microscopic components and intrinsic dynamics for accurate mechanistic insights.

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Area of Science:

  • Complex Systems Science
  • Information Theory
  • Statistical Mechanics

Background:

  • Understanding complex systems requires analyzing emergent phenomena from component interactions.
  • Information theory offers robust measures for quantifying interdependencies.
  • Relating information measures to underlying mechanistic structures is crucial but challenging.

Purpose of the Study:

  • To analytically investigate the relationship between information-theoretic measures and mechanistic properties.
  • To explore how coarse-graining and timescales affect information measures in a simple interactive system.
  • To clarify the mechanistic origins of emergent behaviors and statistical phenomena.

Main Methods:

  • Information-theoretic analysis of interactive random walkers with Gaussian noise.
  • Focus on partial information decomposition, causal emergence, and integrated information.
  • Comparison of information measures at microscopic versus coarse-grained levels and across different timescales.

Main Results:

  • Information measures correlate more reliably with mechanistic properties at the microscopic level.
  • Information measures are more accurate when calculated over timescales matching the system's intrinsic dynamics.
  • Coarse-graining obscures the relationship between information measures and mechanistic structure.

Conclusions:

  • Information-theoretic measures provide deeper insights into system mechanics when applied to microscopic components and appropriate timescales.
  • Separating system dynamics from steady-state distributions can enhance the interpretability of information-theoretic analyses.
  • This study provides foundational understanding for applying information theory to complex systems.