Autonomy: An Information-Theoretic Perspective

We present a tentative proposal for a quantitative measure of autonomy. This is something that, surprisingly, seems to be missing from the literature, even though autonomy is considered to be a basic concept in many disciplines, including artificial life. We work in an information-theoretic setting for which the distinction between system and environment is the starting point. We propose as a measure for autonomy the conditional mutual information between consecutive states of the system conditioned on the history of the environment. This works well when the system cannot influence the environment at all. When, in contrast, the system has full control over its environment, we should rather neglect the environment history and simply take the mutual information between consecutive system states as a measure of autonomy. This discussion reveals some subtle facets of the concept of autonomy, ultimately leading towards the ambitious issue of autonomy as a fundamental (defining) concept of life itself. In our formalization, these aspects question the seemingly innocent system-environment distinction we took for granted and raise the issue of the attribution of control, i.e., the responsibility for observed effects. To further explore these issues, we evaluate our autonomy measure for simple automata, an agent moving in space, gliders in the game of life, and the tessellation automaton for autopoiesis by Varela et al.