System Crash as Dynamics of Complex Networks. Yu, Y., Xiao, G., Zhou, J., Wang, Y., Wang, Z., Kurths, J., & Schellnhuber, H. J. Proceedings of the National Academy of Sciences, 113(42):11726–11731, October, 2016. doi abstract bibtex [Significance] System crash, as an essential part of system evolution, sometimes happens in peculiar manners: Weakened systems may survive for a surprisingly long time before suddenly meeting their final ends, whereas seemingly unbeatable giants may drastically crash to virtual nonexistence. We propose a model that describes system crash as a consequence of some relatively simple local information-based individual behaviors: Individuals leave networks according to some most straightforward assessment of current and future benefits/risks. Of note, such a simple rule may enable a single push/mistake to cause multistage-style system crash. Our study helps to make sense of the process where complex systems go into unstoppable cascading declines and provides a viewpoint of predicting the fate of some social/natural systems. [Abstract] Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the '' peculiar'' dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed.
@article{yuSystemCrashDynamics2016,
title = {System Crash as Dynamics of Complex Networks},
author = {Yu, Yi and Xiao, Gaoxi and Zhou, Jie and Wang, Yubo and Wang, Zhen and Kurths, J{\"u}rgen and Schellnhuber, Hans J.},
year = {2016},
month = oct,
volume = {113},
pages = {11726--11731},
issn = {1091-6490},
doi = {10.1073/pnas.1612094113},
abstract = {[Significance]
System crash, as an essential part of system evolution, sometimes happens in peculiar manners: Weakened systems may survive for a surprisingly long time before suddenly meeting their final ends, whereas seemingly unbeatable giants may drastically crash to virtual nonexistence. We propose a model that describes system crash as a consequence of some relatively simple local information-based individual behaviors: Individuals leave networks according to some most straightforward assessment of current and future benefits/risks. Of note, such a simple rule may enable a single push/mistake to cause multistage-style system crash. Our study helps to make sense of the process where complex systems go into unstoppable cascading declines and provides a viewpoint of predicting the fate of some social/natural systems. [Abstract]
Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the '' peculiar'' dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed.},
journal = {Proceedings of the National Academy of Sciences},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14152985,~to-add-doi-URL,complexity,dynamic-system,evolution,instability,networks,non-linearity,resilience,system-catastrophe},
lccn = {INRMM-MiD:c-14152985},
number = {42}
}
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