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Separating internal and external dynamics of complex systems.

M Argollo de Menezes1, A-L Barabási

  • 1Department of Physics, University of Notre Dame, Indiana 46556, USA.

Physical Review Letters
|August 25, 2004
PubMed
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This study introduces a new method to distinguish between internal dynamics and external influences in complex systems by monitoring component activity. This approach reveals that systems like the Internet and computer chips have stable internal workings, unlike traffic systems driven by external demand.

Area of Science:

  • Complex systems science
  • Network dynamics
  • Statistical physics

Background:

  • Observable behavior of complex systems arises from internal interactions and external influences.
  • Distinguishing internal dynamics from external fluctuations is crucial for understanding system behavior.
  • Multichannel measurements are increasingly common across scientific disciplines.

Purpose of the Study:

  • To develop a method for separating internal dynamics from external fluctuations in complex systems.
  • To systematically identify the origins of fluctuations in diverse real-world systems.
  • To demonstrate the broad applicability of multichannel measurements in complex systems analysis.

Main Methods:

  • Simultaneous monitoring of multiple system components.

Related Experiment Videos

  • Analysis of internal dynamics versus external perturbations.
  • Application of the method to various complex systems, including the Internet, computer chips, highway traffic, and Web traffic.

    • Main Results:

    • The developed method successfully separates internal dynamics from external fluctuations.
    • Internet and computer chip systems exhibit robust internal dynamics.
    • Highway and Web traffic dynamics are primarily driven by external demand.
    • The findings highlight differing mechanisms governing complex systems.

    Conclusions:

    • The proposed method provides a systematic way to determine the origin of fluctuations in complex systems.
    • This technique can differentiate between internally driven and externally influenced system behaviors.
    • The approach has the potential to reveal collective dynamics across a wide range of complex systems as multichannel measurements become standard.