Bosiljka Tadic, Alexander Shapoval, Mikhail Shnirman
{"title":"Self-organised dynamics beyond scaling of avalanches: Cyclic stress fluctuations in critical sandpiles","authors":"Bosiljka Tadic, Alexander Shapoval, Mikhail Shnirman","doi":"arxiv-2403.15859","DOIUrl":null,"url":null,"abstract":"Recognising changes in collective dynamics in complex systems is essential\nfor predicting potential events and their development. Possessing intrinsic\nattractors with laws associated with scale invariance, self-organised critical\ndynamics represent a suitable example for quantitatively studying changes in\ncollective behaviour. We consider two prototypal models of self-organised\ncriticality, the sandpile automata with deterministic (Bak-Tang-Wiesenfeld) and\nprobabilistic (Manna model) dynamical rules, focusing on the nature of stress\nfluctuations induced by driving - adding grains during the avalanche\npropagation, and dissipation through avalanches that hit the system boundary.\nOur analysis of stress evolution time series reveals robust cycles modulated by\ncollective fluctuations with dissipative avalanches. These modulated cycles are\nmultifractal within a broad range of time scales. Features of the associated\nsingularity spectra capture the differences in the dynamic rules behind the\nself-organised critical states and their response to the increased driving\nrate, altering the process stochasticity and causing a loss of avalanche\nscaling. In the related sequences of outflow current, the first return\ndistributions are found to follow modified laws that describe different\npathways to the gradual loss of cooperative behaviour in these two models. The\nspontaneous appearance of cycles is another characteristic of self-organised\ncriticality. It can also help identify the prominence of self-organisational\nphenomenology in an empirical time series when underlying interactions and\ndriving modes remain hidden.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"515 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Adaptation and Self-Organizing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2403.15859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Recognising changes in collective dynamics in complex systems is essential
for predicting potential events and their development. Possessing intrinsic
attractors with laws associated with scale invariance, self-organised critical
dynamics represent a suitable example for quantitatively studying changes in
collective behaviour. We consider two prototypal models of self-organised
criticality, the sandpile automata with deterministic (Bak-Tang-Wiesenfeld) and
probabilistic (Manna model) dynamical rules, focusing on the nature of stress
fluctuations induced by driving - adding grains during the avalanche
propagation, and dissipation through avalanches that hit the system boundary.
Our analysis of stress evolution time series reveals robust cycles modulated by
collective fluctuations with dissipative avalanches. These modulated cycles are
multifractal within a broad range of time scales. Features of the associated
singularity spectra capture the differences in the dynamic rules behind the
self-organised critical states and their response to the increased driving
rate, altering the process stochasticity and causing a loss of avalanche
scaling. In the related sequences of outflow current, the first return
distributions are found to follow modified laws that describe different
pathways to the gradual loss of cooperative behaviour in these two models. The
spontaneous appearance of cycles is another characteristic of self-organised
criticality. It can also help identify the prominence of self-organisational
phenomenology in an empirical time series when underlying interactions and
driving modes remain hidden.