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This study introduces a novel, generic method to quantify complex system dynamics using the adaptive cycle model. The approach successfully identifies system positions and change drivers across diverse applications, confirming the adaptive cycle

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Area of Science:

  • Complex systems science
  • Ecology
  • Economics

Background:

  • The adaptive cycle metaphor qualitatively describes complex system development through stability and reorganization phases.
  • Existing approaches lack a generic, quantifiable method independent of specific system realizations.

Purpose of the Study:

  • To develop and validate a generic quantitative method for assessing a complex system's position within the adaptive cycle.
  • To identify key drivers of change within complex systems.
  • To demonstrate the method's applicability across diverse scientific domains.

Main Methods:

  • Proposed a novel, generic quantitative method to estimate a system's position in the adaptive cycle.
  • Applied the method to three distinct case studies: abstract genotype interactions, European economies, and a prairie-forest plant ecosystem.
  • Validated the method's ability to capture system dynamics and identify change drivers.

Main Results:

  • The proposed method successfully quantified system positions within the adaptive cycle across all case studies.
  • Drivers of change were effectively identified in abstract genotype interactions, European economies, and the plant ecosystem.
  • The quantitative approach confirmed the conceptual dynamics of the adaptive cycle.

Conclusions:

  • The developed method provides a flexible and applicable tool for understanding complex system dynamics.
  • This quantitative approach enhances the usability of the adaptive cycle metaphor in scientific research.
  • The findings support the adaptive cycle's utility in analyzing dynamic, evolving systems.