Unifying Pairwise Interactions in Complex Dynamics
This work unifies diverse interdisciplinary methods for analyzing complex dynamics, benefiting scientists and researchers by integrating decades of advances into an open software framework.
The authors tackled the problem of disconnected pairwise interaction measures in complex systems by introducing a library of 237 statistics and assessing them on 1053 multivariate time series, revealing commonalities and enabling data-driven selection of suitable methods for interpretable understanding.
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.