Distinguishing Causal Interactions in Neural Populations
Neural Computation
Defining Agency: Individuality, Normativity, Asymmetry, and Spatio-temporality in Action
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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I introduce a quantitative measure of autonomy based on a time series analysis adapted from 'Granger causality'. A system is considered autonomous if prediction of its future evolution is enhanced by considering its own past states, as compared to predictions based on past states of a set of external variables. The proposed measure, G-autonomy, amplifies the notion of autonomy as 'self-determination'. I illustrate G-autonomy by application to example time series data and to an agent-based model of predator-prey behaviour. Analysis of the predator-prey model shows that evolutionary adaptation can enhance G-autonomy.