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Information theory: A foundation for complexity science.

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Summary
This summary is machine-generated.

Maximum information entropy provides a robust framework for scientific inference, especially in complex systems with limited data. This approach helps resolve underdetermined problems by selecting the most unbiased model consistent with available information.

Keywords:
data-based modelseconomies and ecosystemsentropyinformation-theoretic inferencetheory construction

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Area of Science:

  • Complex Systems Science
  • Statistical Inference
  • Information Theory

Background:

  • Scientific modeling and inference are crucial for understanding complex systems.
  • Insufficient and uncertain information leads to underdetermined inference problems.
  • Multiple models can be consistent with limited available data.

Purpose of the Study:

  • To review the historical development and applications of the maximum information entropy formalism.
  • To provide an overview of maximum entropy applications in complex systems.
  • To discuss recent advances and future directions in information-theoretic inference.

Main Methods:

  • Utilizes the maximum information entropy (MaxEnt) formalism.
  • Reviews historical development and diverse applications of MaxEnt.
  • Discusses extensions of MaxEnt for complex dynamic systems.

Main Results:

  • MaxEnt offers a principled approach to handle uncertainty and insufficient information in inference.
  • The formalism has been successfully applied across numerous scientific disciplines.
  • Recent advances focus on dynamic systems with time-varying constraints.

Conclusions:

  • Maximum information entropy is a powerful tool for inference in complex systems.
  • Its application extends to dynamic systems like ecosystems and economies.
  • Continued research advances information-theoretic inference for complex scientific challenges.