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Information modification and particle collisions in distributed computation.

Joseph T Lizier1, Mikhail Prokopenko, Albert Y Zomaya

  • 1CSIRO Information and Communications Technology Centre, P.O. Box 76, Epping, New South Wales 1710, Australia. jlizier@it.usyd.edu.au

Chaos (Woodbury, N.Y.)
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Summary
This summary is machine-generated.

This study introduces separable information, a new method to quantify information modification in distributed computation. It provides the first quantitative evidence that particle collisions in cellular automata are key information modification events.

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Area of Science:

  • Theoretical Computer Science
  • Information Theory
  • Complex Systems

Background:

  • Distributed computation involves information storage, transfer, and modification.
  • Quantifying these operations locally in space and time is crucial for understanding computational dynamics.
  • Previous work focused on quantifying information storage and transfer.

Purpose of the Study:

  • To introduce a method for quantifying information modification at each spatiotemporal point.
  • To develop a measure that identifies misleading information modification events.
  • To provide quantitative evidence for conjectures in cellular automata.

Main Methods:

  • Introduction of the 'separable information' measure.
  • Application of this measure to cellular automata systems.
  • Local quantification of information modification events.

Main Results:

  • The separable information measure successfully quantifies local information modification.
  • It identifies events where source inspection is misleading.
  • Provides the first direct quantitative evidence for particle collisions as dominant modification events in cellular automata.

Conclusions:

  • Separable information offers a novel approach to quantifying information modification in distributed systems.
  • This measure validates the significance of particle collisions in cellular automata dynamics.
  • The findings advance the understanding of information processing in complex computational systems.