Measuring Autonomy and Emergence via Granger Causality
Artificial Life January 12, 2010 DOI: 10.1162/artl.2010.16.2.16204 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Autonomy Granger causality Causality physics Consciousness Process computing Causation Adaptation eye Cognitive psychology Econometrics Epistemology |
| Citations | 85 |
| Key points | Proposes that G-autonomy and G-emergence, derived from Granger causality, provide quantitative and practicable measures for autonomy and emergence, validated through agent-based models. |
Abstract
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.