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Related Experiment Video

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Measuring complexity in organisms and organizations.

Nancy Rebout1,2, Jean-Christophe Lone1, Arianna De Marco2,3

  • 1Physiologie de la Reproduction et des Comportements, CNRS, INRAE, Université de Tours, Nouzilly, France.

Royal Society Open Science
|May 7, 2021
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Summary
This summary is machine-generated.

We introduce new metrics to quantify system complexity by measuring uncertainty. These indices assess diversity, flexibility, and combinability, offering a novel way to compare complex living organisms and social organizations.

Keywords:
combinabilitydiversityentropyflexibilitysystemuncertainty

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Area of Science:

  • Complex systems science
  • Quantitative sociology
  • Theoretical biology

Background:

  • Defining and quantifying complexity remains a challenge in scientific research.
  • Uncertainty production is a key characteristic of complex systems.
  • Existing metrics may not fully capture the multifaceted nature of complexity.

Purpose of the Study:

  • To introduce novel metrics for quantifying the complexity of living organisms and social organizations.
  • To establish a theoretical framework for complexity measurement based on uncertainty.
  • To provide a method for comparing complexity across diverse systems.

Main Methods:

  • Development of new quantitative indices based on Shannon's uncertainty formula.
  • Assessment of complexity through three dimensions: diversity, flexibility, and combinability.
  • Integration of these dimensions into a tripartite complexity index.

Main Results:

  • The proposed metrics successfully quantify complexity based on system uncertainty levels.
  • The tripartite index provides a comprehensive measure by considering diversity, flexibility, and combinability.
  • A calculation example demonstrates the utility of the indices for comparing social systems.

Conclusions:

  • The new uncertainty-based complexity indices offer a robust theoretical foundation for measurement.
  • These indices enable quantitative comparisons of complexity across different types of systems.
  • The proposed framework is expected to stimulate further research into comparative complexity studies.