Bollt Erik M, Sun Jie, Runge Jakob
Clarkson Center for Complex Systems Science (C3S2), Clarkson University, Potsdam, New York 13699, USA.
German Aerospace Center (DLR), Institute of Data Science, Maelzerstrasse 3, 07745 Jena, Germany.
Chaos. 2018 Jul;28(7):075201. doi: 10.1063/1.5046848.
Questions of causation are foundational across science and often relate further to problems of control, policy decisions, and forecasts. In nonlinear dynamics and complex systems science, causation inference and information flow are closely related concepts, whereby "information" or knowledge of certain states can be thought of as coupling influence onto the future states of other processes in a complex system. While causation inference and information flow are by now classical topics, incorporating methods from statistics and time series analysis, information theory, dynamical systems, and statistical mechanics, to name a few, there remain important advancements in continuing to strengthen the theory, and pushing the context of applications, especially with the ever-increasing abundance of data collected across many fields and systems. This Focus Issue considers different aspects of these questions, both in terms of founding theory and several topical applications.
因果关系问题是所有科学的基础，并且常常与控制问题、政策决策和预测进一步相关。在非线性动力学和复杂系统科学中，因果推断和信息流是密切相关的概念，据此，关于某些状态的“信息”或知识可以被视为对复杂系统中其他过程的未来状态的耦合影响。虽然因果推断和信息流目前是经典主题，涉及统计学和时间序列分析、信息论、动力系统以及统计力学等诸多方法，但在继续加强理论以及拓展应用背景方面仍有重要进展，特别是鉴于跨多个领域和系统收集的数据量不断增加。本专题聚焦从基础理论和若干热点应用等不同方面探讨这些问题。
