Scale Invariant Avalanches: A Critical Confusion
Osvanny Ramos

TL;DR
This paper clarifies the role of the power-law exponent in scale invariant avalanches, addressing confusion in the field by analyzing its impact on criticality, predictability, and system dynamics across various phenomena.
Contribution
It introduces a criticality condition based on the exponent value, explaining how smaller exponents lead to more predictable and less critical systems, and reviews prediction methods and common misconceptions.
Findings
Smaller exponents reduce critical properties and increase predictability.
The exponent controls the energy balance and size ratio of avalanches.
Logarithmic scales and dissipation influence avalanche analysis.
Abstract
The "Self-organized criticality" (SOC), introduced in 1987 by Bak, Tang and Wiesenfeld, was an attempt to explain the 1/f noise, but it rapidly evolved towards a more ambitious scope: explaining scale invariant avalanches. In two decades, phenomena as diverse as earthquakes, granular piles, snow avalanches, solar flares, superconducting vortices, sub-critical fracture, evolution, and even stock market crashes have been reported to evolve through scale invariant avalanches. The theory, based on the key axiom that a critical state is an attractor of the dynamics, presented an exponent close to -1 (in two dimensions) for the power-law distribution of avalanche sizes. However, the majority of real phenomena classified as SOC present smaller exponents, i.e., larger absolute values of negative exponents, a situation that has provoked a lot of confusion in the field of scale invariant…
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.
Taxonomy
TopicsLandslides and related hazards · Theoretical and Computational Physics · Complex Systems and Time Series Analysis
