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Transients as the Basis for Information Flow in Complex Adaptive Systems.

William Sulis1

  • 1Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, ON L8N 3K7, Canada.

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|December 3, 2020
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Summary
This summary is machine-generated.

Complex adaptive systems rely on meaningful information, not just quantity. Spatio-temporal transients, not instantaneous states, drive system behavior and environmental parsing.

Keywords:
Sulis machinesTIGoRScomplex adaptive systemsinformationsaliencesemanticstransients

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Area of Science:

  • Complex Systems Science
  • Information Theory
  • Theoretical Biology

Background:

  • Information is crucial for complex adaptive systems (CAS) like organisms and social insects.
  • System behavior is governed more by information than energy.
  • The significance of information lies in its salience (meaning), not its quantity.

Purpose of the Study:

  • To shift theoretical focus from instantaneous states to spatio-temporal transients for studying information flow in CAS.
  • To explore how salience, linked to transients, shapes system responses.
  • To model primitive information processing in simple complex systems.

Main Methods:

  • Analysis of information flow in naturally occurring CAS.
  • Introduction of Transient Induced Global Response Synchronization (TIGoRS) as a model for salience.
  • Development of a Sulis machine model for systems exhibiting TIGoRS.

Main Results:

  • Salience in CAS is associated with spatio-temporal transients, not just current events.
  • TIGoRS demonstrates how sparse sampling of transients can elicit stable collective responses.
  • CAS can parse their environment into salient and non-salient stimuli through TIGoRS.

Conclusions:

  • Spatio-temporal transients are key to understanding information processing in CAS.
  • TIGoRS provides a mechanism for primitive stimulus-response relationships in complex systems.
  • Complex systems with TIGoRS function as rudimentary automata (Sulis machines).