Abstract
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.
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Seth, A.K. (2007). Measuring Autonomy by Multivariate Autoregressive Modelling. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds) Advances in Artificial Life. ECAL 2007. Lecture Notes in Computer Science(), vol 4648. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74913-4_48
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DOI: https://doi.org/10.1007/978-3-540-74913-4_48
Publisher Name: Springer, Berlin, Heidelberg
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