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Upper bounds for integrated information.

Alireza Zaeemzadeh1, Giulio Tononi1

  • 1Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, United States of America.

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|August 5, 2024
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Summary
This summary is machine-generated.

Integrated information theory quantifies system irreducibility. This study explores the maximum achievable integrated information (II) for mechanisms, finding shared components limit maximal II and offering design techniques for maximizing II.

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Area of Science:

  • Theoretical physics
  • Cognitive science
  • Information theory

Background:

  • Integrated Information Theory (IIT) offers a mathematical framework for consciousness.
  • IIT quantifies system irreducibility using mechanism integrated information (MII).
  • MII measures causal powers of a mechanism not explained by its parts.

Purpose of the Study:

  • Investigate the upper bound of MII.
  • Determine conditions for achieving maximal MII.
  • Develop methods for designing systems with maximized MII.

Main Methods:

  • Analyzing mechanisms in isolation and in groups.
  • Examining causal relations among mechanisms.
  • Developing theoretical results on MII bounds and achievement.

Main Results:

  • Mechanisms sharing parts cannot all achieve maximal MII.
  • New techniques are introduced for designing systems to maximize MII.
  • Identified constraints and symmetries for computational reduction.

Conclusions:

  • Understanding MII bounds is crucial for IIT applications.
  • System design can be optimized for specific MII targets.
  • Results facilitate comparison of connectivity profiles based on maximal MII.