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Perspectives on Neuroscience
26:41

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Published on: July 31, 2007

Complexity: against systems.

Dominique Chu1

  • 1School of Computing, University of Kent, CT2 7NF, Canterbury, United Kingdom. d.f.chu@kent.ac.uk

Theory in Biosciences = Theorie in Den Biowissenschaften
|February 3, 2011
PubMed
Summary
This summary is machine-generated.

This study defines complexity as the difficulty in creating or verifying models. Key causes identified are radical openness and contextuality, particularly when system boundaries and abstractions become unclear, as seen in evolutionary examples.

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Area of Science:

  • Systems theory
  • Evolutionary biology
  • Philosophy of science

Background:

  • Complexity is often intuitively understood as difficulty in model generation or plausibility assessment.
  • Existing definitions may not fully capture the challenges arising from system boundaries and abstraction choices.

Purpose of the Study:

  • To define complexity based on the difficulty of generating and assessing model plausibility.
  • To identify and explain the primary drivers of complexity in modeling.
  • To illustrate these concepts using examples from evolutionary studies.

Main Methods:

  • Conceptual analysis of complexity in modeling.
  • Identification of radical openness and contextuality as key factors.
  • Application of the complexity framework to evolutionary examples.

Main Results:

  • Complexity arises from the inherent need to establish artificial boundaries (radical openness) and make simplifications (contextuality) in modeling.
  • Uncertainty in system boundaries and appropriate abstractions leads to significant complexity.
  • Evolutionary examples demonstrate how these factors influence model development and validation.

Conclusions:

  • A refined understanding of complexity is crucial for effective scientific modeling.
  • Radical openness and contextuality are fundamental challenges in representing complex systems.
  • The proposed framework provides a novel perspective on complexity in scientific inquiry, particularly in evolutionary contexts.