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Unified framework for information integration based on information geometry.

Masafumi Oizumi1,2, Naotsugu Tsuchiya2,3,4, Shun-Ichi Amari5

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This study introduces a novel information geometry framework to quantify multiple causal influences in complex systems. It resolves mathematical challenges in measuring integrated information for a more accurate understanding of system dynamics.

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Area of Science:

  • Complex Systems Analysis
  • Information Theory
  • Consciousness Studies

Background:

  • Assessing causal influences is crucial across scientific fields.
  • Integrated information quantifies causal influences among system elements, originating from consciousness studies.
  • Existing methods struggle with quantifying multiple influences due to overestimation and confounding factors.

Purpose of the Study:

  • To develop a theoretical framework for quantifying multiple causal influences holistically.
  • To address mathematical difficulties in measuring integrated information.
  • To provide a unified approach for analyzing causal relationships in complex systems.

Main Methods:

  • Utilized information geometry to develop a novel theoretical framework.
  • Derived a measure of integrated information based on probability distribution divergence.
  • Geometrically interpreted integrated information as the divergence between actual and approximated system distributions.

Main Results:

  • Proposed a holistic framework to quantify multiple causal influences, avoiding overestimation and confounding.
  • Derived a geometrically interpreted measure of integrated information.
  • Harmonized various information-theoretic measures (mutual information, transfer entropy, etc.) within the framework.

Conclusions:

  • The proposed framework offers a robust method for quantifying integrated information and multiple causal influences.
  • It provides intuitive geometric interpretations and unifies diverse information-theoretic measures.
  • Applicable to consciousness studies and broader complex systems analysis for hierarchical causal relationship assessment.