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Constructing minimal models for complex system dynamics

Author

Listed:
  • Baruch Barzel

    (Bar-Ilan University)

  • Yang-Yu Liu

    (Brigham and Women’s Hospital, Harvard Medical School
    Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School)

  • Albert-László Barabási

    (Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School
    Computer Science and Biology, Northeastern University
    Brigham and Women’s Hospital, Harvard Medical School
    Center for Network Science, Central European University)

Abstract

One of the strengths of statistical physics is the ability to reduce macroscopic observations into microscopic models, offering a mechanistic description of a system’s dynamics. This paradigm, rooted in Boltzmann’s gas theory, has found applications from magnetic phenomena to subcellular processes and epidemic spreading. Yet, each of these advances were the result of decades of meticulous model building and validation, which are impossible to replicate in most complex biological, social or technological systems that lack accurate microscopic models. Here we develop a method to infer the microscopic dynamics of a complex system from observations of its response to external perturbations, allowing us to construct the most general class of nonlinear pairwise dynamics that are guaranteed to recover the observed behaviour. The result, which we test against both numerical and empirical data, is an effective dynamic model that can predict the system’s behaviour and provide crucial insights into its inner workings.

Suggested Citation

  • Baruch Barzel & Yang-Yu Liu & Albert-László Barabási, 2015. "Constructing minimal models for complex system dynamics," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8186
    DOI: 10.1038/ncomms8186
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    Cited by:

    1. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
    2. Tu, Chengyi & Fan, Ying & Shi, Tianyu, 2024. "Dimensionality reduction of networked systems with separable coupling-dynamics: Theory and applications," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Pang, Shaopeng & Hao, Fei, 2017. "Optimizing controllability of edge dynamics in complex networks by perturbing network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 217-227.
    4. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. Lv, Changchun & Yuan, Ziwei & Si, Shubin & Duan, Dongli, 2021. "Robustness of scale-free networks with dynamical behavior against multi-node perturbation," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    6. Zhao Li & Ren Zhuoming & Zhao Ziyi & Weng Tongfeng, 2024. "Topological perturbations on resilience of the world trade competition network," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.

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