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Measuring autonomy and emergence via Granger causality.
1School of Informatics and Sackler Centre for Conciousness Science, University of Sussex, Brighton, BN1 9QJ, UK. a.k.seth@sussex.ac.uk
Artificial Life
|January 14, 2010
Summary
This study introduces quantitative measures for autonomy and emergence in artificial life, using Granger causality to assess predictability and causal relationships in complex systems.
Area of Science:
- Artificial Life
- Cognitive Science
- Behavioral Science
Background:
- Concepts of emergence and autonomy are crucial in artificial life and cognitive sciences.
- Quantitative and practical measures for these phenomena are currently limited.
Purpose of the Study:
- To introduce quantitative and practicable measures for autonomy and emergence.
- To validate these measures using agent-based models.
Main Methods:
- Utilizing multivariate autoregression and Granger causality.
- Developing G-autonomy and G-emergence metrics.
- Applying measures to agent-based models of predation and flocking.
Main Results:
- G-autonomy quantifies a variable's predictive power from its own past versus external factors.
- G-emergence assesses a process's dependence and independence from its causal factors.
- Predation models showed enhanced autonomy with evolutionary adaptation; flocking models demonstrated emergence and downward causation.
Conclusions:
- The proposed G-autonomy and G-emergence measures offer practical tools for studying complex systems.
- These metrics can quantify key phenomena in artificial life, potentially linking to consciousness.
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