Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260-2900, USA.
Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, NY 14260-5030, USA.
J R Soc Interface. 2023 Mar;20(200):20220743. doi: 10.1098/rsif.2022.0743. Epub 2023 Mar 15.
Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multi-stage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multi-stage transition. We found that knowledge of both the ongoing multi-stage transition and network structure enables us to calculate effective early warning signals for multi-stage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multi-stage transition, which we systematically characterize.
成功预测复杂系统中突然的重大变化是一个实际问题。这种复杂系统通常形成一个异构网络,可能会显示多阶段的转变,随着环境的逐渐变化,一些节点比其他节点更早地经历状态转变。在这里,我们研究了经历多阶段转变的网络系统的预警信号。我们发现,了解正在进行的多阶段转变和网络结构,使我们能够计算多阶段转变的有效预警信号。此外,我们发现,小部分节点可以预测转变,甚至比使用所有节点更好。即使我们固定网络和动力系统,也没有一个单一的最佳节点子集可以提供良好的预警信号,而一个好的哨兵节点选择取决于多阶段转变中的 tipping 方向和动态的当前阶段,我们系统地对其进行了特征描述。