Power-law statistics and universal scaling in the absence of criticality
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Abstract (arXiv)
Critical states are sometimes identified experimentally through power-law statistics or universal scaling functions. We show here that such features naturally emerge from networks in self-sustained irregular regimes away from criticality. In these regimes, statistical physics theory of large interacting systems predict a regime where the nodes have independent and identically distributed dynamics. We thus investigated the statistics of a system in which units are replaced by independent stochastic surrogates, and found the same power-law statistics, indicating that these are not sufficient to establish criticality. We rather suggest that these are universal features of large-scale networks when considered macroscopically. These results put caution on the interpretation of scaling laws found in nature.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Mar 2015.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2021Optimal input representation in neural systems at the edge of chaoscited by 2
- 2022Turing's cascade instability supports the coordination of the mind, brain, and behaviorcited by 0
- 2025The Universe Learning Itself: On the Evolution of Dynamics from the Big Bang to Machine Intelligencecited by 0
- 2026Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reductioncited by 0
- 2026Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reduction28 Sep 2026
- 2025The Universe Learning Itself: On the Evolution of Dynamics from the Big Bang to Machine Intelligence18 Dec 2025
- 2022Turing's cascade instability supports the coordination of the mind, brain, and behavior17 Apr 2022
- 2021Optimal input representation in neural systems at the edge of chaos12 Jul 2021