25 Years of Self-Organized Criticality: Numerical Detection Methods
R.T. James McAteer, Markus J. Aschwanden, Michaila Dimitropoulou,, Manolis K. Georgoulis, Gunnar Pruessner, Laura Morales, Jack Ireland,, Valentyna Abramenko

TL;DR
This paper reviews 25 years of numerical methods for detecting and characterizing self-organized criticality in data, highlighting advances, applications, and future directions across various scientific fields.
Contribution
It provides a comprehensive overview of detection techniques for SOC, emphasizing recent methods and interdisciplinary insights that enhance understanding and experimental validation.
Findings
Numerical methods have significantly advanced SOC detection capabilities.
Power-law behaviors are prevalent in natural systems and are key to event characterization.
Interdisciplinary approaches are crucial for unambiguous SOC detection.
Abstract
The detection and characterization of self-organized criticality (SOC), in both real and simulated data, has undergone many significant revisions over the past 25 years. The explosive advances in the many numerical methods available for detecting, discriminating, and ultimately testing, SOC have played a critical role in developing our understanding of how systems experience and exhibit SOC. In this article, methods of detecting SOC are reviewed; from correlations to complexity to critical quantities. A description of the basic autocorrelation method leads into a detailed analysis of application-oriented methods developed in the last 25 years. In the second half of this manuscript space-based, time-based and spatial-temporal methods are reviewed and the prevalence of power laws in nature is described, with an emphasis on event detection and characterization. The search for numerical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
