Evidence map›Paper›PMID 27698137›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2016

System crash as dynamics of complex networks.

Yi Yu, Gaoxi Xiao, Jie Zhou, Yubo Wang, Zhen Wang, Jürgen Kurths, Hans Joachim Schellnhuber

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Opinion dynamics in financial markets via random networks.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  4. Article
  5. From the origin of life to pandemics: emergent phenomena in complex systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  6. Article
  7. Article
  8. Universal behavior of cascading failures in interdependent networks.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yi YuSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.
Gaoxi XiaoSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798; Complexity Institute, Nanyang Technological University, Singapore 639798; EGXXiao@ntu.edu.sg zhenwang0@gmail.com john@pik-potsdam.de.
Jie ZhouDepartment of Physics, East China Normal University, Shanghai 200241, China.
Yubo WangSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.
Zhen WangQingdao University, Qingdao, Shandong 266071, China; Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan; EGXXiao@ntu.edu.sg zhenwang0@gmail.com john@pik-potsdam.de.
Jürgen KurthsPotsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; Department of Physics, Humboldt University, 12489 Berlin, Germany; Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom.
Hans Joachim SchellnhuberPotsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; EGXXiao@ntu.edu.sg zhenwang0@gmail.com john@pik-potsdam.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

cascade behaviorcomplex systemspseudo-steady statesystem crash

Identifiers

PMID27698137
PMCPMC5081585

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.