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Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and
1Research Department, London Interdisciplinary School, London E1 1EW, UK.
Entropy (Basel, Switzerland)
|August 29, 2024
Summary
We introduce incoherence, a new information-theoretic measure to quantify uncertainty in complex systems. This method accurately detects system unpredictability and aids in identifying key characteristics like sensitivity and criticality.
Area of Science:
- Complex Systems Science
- Information Theory
- Statistical Modeling
Background:
- Complex systems present challenges for traditional analysis due to unpredictability.
- Accurate detection of complex behavior and associated uncertainty is crucial.
- Existing statistical methods may not fully capture the nuances of complex systems.
Purpose of the Study:
- Introduce a novel information-theoretic measure, "incoherence," to quantify aleatoric uncertainty.
- Evaluate the efficacy of incoherence compared to established statistical tests.
- Demonstrate the application of incoherence in identifying complex system characteristics.
Main Methods:
- Developed a new information-theoretic measure termed "incoherence."
- Utilized an adapted Jensen-Shannon Divergence across an ensemble of outcomes.
- Compared incoherence to established statistical tests on continuous and discrete data.
Main Results:
- Incoherence effectively quantifies aleatoric uncertainty in complex systems.
- The measure demonstrated comparable or superior performance to existing statistical tests.
- Successfully applied incoherence to identify system sensitivity, criticality, and response to perturbations.
Conclusions:
- Incoherence provides a robust method for quantifying uncertainty in complex systems.
- This measure enhances the ability to analyze and understand complex system dynamics.
- Incoherence offers a valuable tool for researchers studying unpredictable and intricate phenomena.
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