Information Propagation in Multilayer Systems with Higher-Order Interactions across Timescales

Complex systems are characterized by multiple spatial and temporal scales. A natural framework to capture their multiscale nature is that of multilayer networks, where different layers represent distinct physical processes that often regulate each other indirectly. We model these regulatory mechanisms through triadic higher-order interactions between nodes and edges. In this work, we focus on how the different timescales associated with each layer impact their reciprocal effective couplings. First, we rigorously derive a decomposition of the joint probability distribution of any dynamical process acting on such multilayer networks. By inspecting this probabilistic structure, we unravel the general principles governing how information propagates across timescales, elucidating the interplay between mutual information and causality in multiscale systems. In particular, we show that feedback interactions, i.e., those representing regulatory mechanisms from slow to fast variables, generate mutual information between layers. On the contrary, direct interactions, i.e., from fast to slow layers, can propagate this information only under certain conditions that depend solely on the structure of the underlying higher-order couplings. We introduce the mutual information matrix for multiscale observables to capture these emergent functional couplings. We apply our results to study archetypal examples of biological signaling networks and effective environmental dependencies in stochastic processes. Our framework generalizes to any dynamics on multilayer networks, paving the way for a deeper understanding of how the multiscale nature of real-world systems shapes their information content and complexity.