Integrated information as a common signature of dynamical and information-processing complexity
Pedro A M Mediano1, Fernando E Rosas2, Juan Carlos Farah3
1Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom.
Chaos (Woodbury, N.Y.)
|February 2, 2022
Summary
Integrated Information Theory (IIT) offers a unified framework for complexity science. It bridges information-processing and dynamical systems by identifying common complexity signatures across diverse models.
Area of Science:
- Complexity Science
- Theoretical Neuroscience
- Systems Theory
Background:
- Divergence between information-processing and dynamical approaches hinders progress in complexity science.
- Shared goals suggest underlying commonalities in complex systems.
Purpose of the Study:
- To propose Integrated Information Theory (IIT) as a unifying framework for complexity science.
- To demonstrate IIT's ability to capture diverse complexity signatures across different systems.
Main Methods:
- Leveraging metrics from the integrated information decomposition framework.
- Applying IIT to analyze networks of coupled oscillators and cellular automata.
Main Results:
- Integrated information effectively captures metastability and criticality in oscillator networks.
- IIT identifies distributed computation and emergent stable particles in cellular automata.
- The framework operates without idiosyncratic or ad hoc criteria.
Conclusions:
- An agnostic application of IIT can bridge the gap between informational and dynamical approaches to complexity.
- IIT provides a pragmatic framework for studying complexity in general multivariate systems.
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