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Multifractal descriptors ergodically characterize non-ergodic multiplicative cascade processes

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  • Kelty-Stephen, Damian G.
  • Mangalam, Madhur

Abstract

Biological and psychological processes routinely break ergodicity, meaning they fail to have stable means (Mean) and independent variation over time that we might find in additive white Gaussian noise (awGn). One possible reason for this failure of ergodicity is the failure of biological and psychological processes to exhibit independence across time. Multifractal evidence has long suggested that biological and psychological processes show strong signatures of nonlinear interactions across scales. These cross-scale interactions sooner befit a cascade-dynamical process than awGn. The present work thus compares awGn to simulations of multiplicative binomial cascades, submitting both types of series and shuffled versions of each to the Thirumalai-Mountain method for estimating ergodicity breaking. Estimating ergodicity breaking for original awGn and cascade series allows us to examine the sources of ergodicity breaking across the sequence, e.g., in temporal correlations specifying nonlinear interactions across scales, and examining ergodicity breaking of the shuffled series allows us to assess the raw, sequence-independent contribution of distributional properties (e.g., the heavy tails of a cascade) without the original temporal sequence. Raw cascade fluctuations and the standard deviation (SD) and root mean square (RMS) series describing those raw fluctuations break ergodicity, but nonlinear, cascade-dynamical descriptors: multifractal spectrum width (Δα) and multifractal nonlinearity (tMF), maintain ergodicity. Interestingly, the fundamentally linear descriptor, fractal Hurst exponent (HfGn) shows moderate ergodicity breaking when describing the fundamentally nonlinear cascade processes, but the linear descriptor coefficient of variation (CV) controls for multiplicative relationships between SD and Mean and maintains ergodicity. We conclude that the ergodicity of statistical descriptors depends on how well they can portray nonlinearity (Δα and tMF) or at least multiplicativity (CV) of the underlying cascade processes.

Suggested Citation

  • Kelty-Stephen, Damian G. & Mangalam, Madhur, 2023. "Multifractal descriptors ergodically characterize non-ergodic multiplicative cascade processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
  • Handle: RePEc:eee:phsmap:v:617:y:2023:i:c:s0378437123002066
    DOI: 10.1016/j.physa.2023.128651
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    References listed on IDEAS

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    1. Gorka Muñoz-Gil & Giovanni Volpe & Miguel Angel Garcia-March & Erez Aghion & Aykut Argun & Chang Beom Hong & Tom Bland & Stefano Bo & J. Alberto Conejero & Nicolás Firbas & Òscar Garibo i Orts & Aless, 2021. "Objective comparison of methods to decode anomalous diffusion," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    2. Amir AghaKouchak & Laurie S. Huning & Felicia Chiang & Mojtaba Sadegh & Farshid Vahedifard & Omid Mazdiyasni & Hamed Moftakhari & Iman Mallakpour, 2018. "How do natural hazards cascade to cause disasters?," Nature, Nature, vol. 561(7724), pages 458-460, September.
    3. Miguel Garcia-Castro & Lea Kremer & Christopher D. Reinkemeier & Christian Unkelbach & Carsten Strohmann & Slava Ziegler & Claude Ostermann & Kamal Kumar, 2015. "De novo branching cascades for structural and functional diversity in small molecules," Nature Communications, Nature, vol. 6(1), pages 1-13, May.
    4. Kelty-Stephen, Damian G. & Furmanek, Mariusz P. & Mangalam, Madhur, 2021. "Multifractality distinguishes reactive from proactive cascades in postural control," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    5. Kelty-Stephen, Damian G. & Mangalam, Madhur, 2022. "Fractal and multifractal descriptors restore ergodicity broken by non-Gaussianity in time series," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    6. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    7. Mangalam, Madhur & Carver, Nicole S. & Kelty-Stephen, Damian G., 2020. "Global broadcasting of local fractal fluctuations in a bodywide distributed system supports perception via effortful touch," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
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    1. Kelty-Stephen, Damian G. & Mangalam, Madhur, 2024. "Additivity suppresses multifractal nonlinearity due to multiplicative cascade dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

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