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Measuring autonomy and emergence via Granger causality
Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness.
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- Published
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- Published version
Journal
Artificial LifeISSN
10645462Publisher
MIT PressExternal DOI
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2Volume
16Page range
179-196Pages
18.0Department affiliated with
- Informatics Publications
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- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06First Open Access (FOA) Date
2016-03-22First Compliant Deposit (FCD) Date
2017-03-14Usage metrics
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