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Related Experiment Videos

Autonomy: an information theoretic perspective.

Nils Bertschinger1, Eckehard Olbrich, Nihat Ay

  • 1Max Planck Institute for Mathematics in the Sciences, Inselstr. 22, D 04103 Leipzig, Germany. Nils.Bertschinger@mis.mpg.de

Bio Systems
|September 28, 2007
PubMed
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We propose a quantitative measure for autonomy, a fundamental concept in artificial life. Our information-theoretic approach quantifies system autonomy by analyzing system-environment interactions and causal relationships.

Area of Science:

  • Artificial Life
  • Information Theory
  • Systems Science

Background:

  • Autonomy is a foundational concept across numerous disciplines, yet quantitative measures are scarce.
  • Existing literature lacks a robust framework for measuring autonomy, particularly in artificial systems.

Purpose of the Study:

  • To propose and evaluate a novel quantitative measure for system autonomy.
  • To explore the implications of system-environment interactions on autonomy.
  • To address ambiguities in causal attribution within complex systems.

Main Methods:

  • Utilizing an information-theoretic framework with a focus on system-environment distinctions.
  • Developing a measure based on conditional mutual information between system states, considering environment history.

Related Experiment Videos

  • Introducing a "causal" autonomy measure for systems with known interaction structures.

    • Main Results:

    • The proposed measure effectively quantifies autonomy in scenarios with limited system influence and full system control.
    • Ambiguities in causal attribution are resolved for systems with known interaction structures.
    • Synergetic interactions present challenges, highlighting limitations in attributing causation solely to the system or environment.

    Conclusions:

    • The developed quantitative measure offers a novel approach to understanding autonomy in artificial systems.
    • The study reveals complexities in the system-environment distinction and control attribution.
    • Further research is needed to address autonomy in systems with synergetic interactions.