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Pattern-oriented modeling of agent-based complex systems: lessons from ecology.

Volker Grimm1, Eloy Revilla, Uta Berger

  • 1UFZ Umweltforschungszentrum Leipzig-Halle, Department Okologische Systemanalyse, PF 500 136, 04301 Leipzig, Germany. volker.grimm@ufz.de

Science (New York, N.Y.)
|November 15, 2005
PubMed
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Pattern-oriented modeling offers a new framework for understanding complex systems, like ecosystems and cities. This approach decodes internal organization by analyzing patterns, potentially unifying theories of adaptive behavior and complexity.

Area of Science:

  • Complex Systems Science
  • Computational Modeling
  • Ecological Informatics

Background:

  • Agent-based complex systems (e.g., ecosystems, markets, cities) are dynamic networks of interacting agents.
  • Bottom-up simulation models (e.g., cellular automata, agent-based models) are used to study these systems.
  • A general framework for designing, testing, and analyzing bottom-up models is currently lacking.

Purpose of the Study:

  • To introduce pattern-oriented modeling as a unifying strategy for studying agent-based complex systems.
  • To provide a framework for decoding the internal organization of these systems.
  • To advance algorithmic theories connecting adaptive behavior and system complexity.

Main Methods:

  • Development of pattern-oriented modeling (POM) strategy.

Related Experiment Videos

  • Application of POM to ecological modeling advances.
  • Utilizing bottom-up simulation models within the POM framework.

    • Main Results:

    • Pattern-oriented modeling provides a unifying framework for analyzing complex systems.
    • The strategy aids in decoding the internal organization of agent-based systems.
    • Potential for unifying algorithmic theories on adaptive behavior and complexity.

    Conclusions:

    • Pattern-oriented modeling offers a robust strategy for studying complex systems.
    • This approach facilitates understanding the relationship between adaptive behavior and system complexity.
    • POM may lead to significant advancements in computational and ecological modeling.