Predicting the large-scale behaviour of complex systems is challenging because of their underlying nonlinear dynamics. Theoretical evidence now verifies that many complex systems can be simplified and still provide an insightful description of the phenomena of interest.
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References
Barabási, A. L. Network Science (Cambridge Univ. Press, 2016).
Thibeault, V., Allard, A. & Desrosiers, P. Nat. Phys. https://doi.org/10.1038/s41567-023-02303-0 (2024).
Gao, J. et al. Nature 530, 307–312 (2016).
Laurence, E. et al. Phys. Rev. X 9, 011042 (2019).
Vegué, M. et al. PNAS Nexus 2, 150 (2023).
Tu, C. et al. iScience 24, 101912 (2021).
Jiang, J. et al. Proc. Natl Acad. Sci. USA 115, E639–E647 (2018).
Zhang, H. Nat. Ecol. Evol. 6, 1524–1536 (2022).
Prasse, B. et al. Proc. Natl Acad. Sci. USA 119, e2205517119 (2022).
Sanhedrai, H. et al. Nat. Phys. 18, 338–349 (2022).
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I acknowledge the support of the US National Science Foundation under grant #2047488.
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Gao, J. Intrinsic simplicity of complex systems. Nat. Phys. 20, 184–185 (2024). https://doi.org/10.1038/s41567-023-02268-0
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DOI: https://doi.org/10.1038/s41567-023-02268-0