Identifying causal gateways and mediators in complex spatio-temporal systems
By Jakob Runge, V. Petoukhov, Jonathan F. Donges, J. Hlinka, N. Jajcay, Martin Vejmelka, David Hartman, Norbert Marwan, Milan Palus, Jürgen Kurths. Nature Communications, 2015
326 citations recorded by the source corpus.
Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of spatio-temporal complex systems such as the Earth’s climate to volcanic eruptions, extreme events or geoengineering. Here a data-driven approach is introduced based on a dimension reduction, causal reconstruction, and novel network measures based on causal effect theory that go beyond standard complex network tools by distinguishing direct from indirect pathways. Applied to a data set of atmospheric dynamics, the method identifies several strongly uplifting regions acting as major gateways of perturbations spreading in the atmosphere. Additionally, the method provides a stricter statistical approach to pathways of atmospheric teleconnections, yielding insights into the Pacific–Indian Ocean interaction relevant for monsoonal dynamics. Also for neuroscience or power grids, the novel causal interaction perspective provides a complementary approach to simulations or experiments for understanding the functioning of complex spatio-temporal systems with potential applications in increasing their resilience to shocks or extreme events. Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of complex systems such as the Earth’s climate. Here the authors introduce a data-driven approach that identifies causal pathways, and apply it to a global atmospheric data set.
Subfields: Cognitive Neuroscience, Global and Planetary Change, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/3469146baafb4c8608f80feb5bc26a42af35ac81
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In Cognitive Neuroscience
In Statistical and Nonlinear Physics
Citations in the corpus
- Causal network reconstruction from time series: From theoretical assumptions to practical estimation. (2018, background): “In Runge et al. (2015a), synergy is analyzed in the context of optimal prediction schemes.”