论文标题
在复杂动力学中统一成对相互作用
Unifying Pairwise Interactions in Complex Dynamics
论文作者
论文摘要
科学家已经开发了数百种技术来测量复杂系统中的一对过程之间的相互作用。但是，从相关系数到因果推断，这些计算方法依赖于在很大程度上断开连接的不同定量理论。在这里，我们介绍了一个由237个成对相互作用的统计数据的库，并评估了其在1053个多元时间序列中的行为，来自各种现实世界和模型生成的系统。我们的分析强调了不同的数学表述之间的新共同点，提供了丰富的跨学科文献的统一图片。然后，我们使用三个现实世界的案例研究表明，同时利用各种科学的不同方法可以揭示最适合解决给定问题的人，从而对促进成功绩效的成对依赖的概念表述产生可解释的理解。我们的框架是在可扩展的开放软件中提供的，可以通过整合数十年的方法学进步来实现全面的数据驱动分析。
Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems. But these computational methods, from correlation coefficients to causal inference, rely on distinct quantitative theories that remain largely disconnected. Here we introduce a library of 237 statistics of pairwise interactions and assess their behavior on 1053 multivariate time series from a wide range of real-world and model-generated systems. Our analysis highlights new commonalities between different mathematical formulations, providing a unified picture of a rich interdisciplinary literature. Using three real-world case studies, we then show that simultaneously leveraging diverse methods from across science can uncover those most suitable for addressing a given problem, yielding interpretable understanding of the conceptual formulations of pairwise dependence that drive successful performance. Our framework is provided in extendable open software, enabling comprehensive data-driven analysis by integrating decades of methodological advances.
