Topology as a Language for Emergent Organization in Complex Systems: Multiscale Structure, Higher-Order Interactions, and Early Warning Signals

TL;DR

This review highlights how topological methods like persistent homology and Mapper provide a powerful language for understanding emergent organization in complex systems across multiple scales and domains, aiding in early warning and regime shift detection.

Contribution

It synthesizes recent advances in topological data analysis applied to complex systems, emphasizing multiscale structure, higher-order interactions, and practical applications.

Findings

Topological methods formalize multiscale stability and collective interactions.

Higher-order models capture interactions beyond pairwise relationships.

Topology-based signals are effective for regime shifts and early warning detection.

Abstract

Complex systems are difficult to study not only because they are nonlinear, multiscale, and often nonstationary, but because their scientifically relevant organization is often invisible at the level of individual components, pairwise interactions, or low-order summary statistics. This review argues that topology has become valuable in complex-systems science because it provides a mathematical language for representing emergent organization when relevant structure is distributed, relational, and robust across scale. We synthesize work on persistent homology, Mapper, simplicial complexes, hypergraphs, and related operators, while distinguishing invariant-based topological methods from broader topology-inspired representations. We show how persistence formalizes multiscale stability, how higher-order models preserve collective interactions erased by pairwise graphs, and how topological approaches complement rather than replace statistics, graph theory, and geometry. We review applications in nonlinear dynamics, neuroscience, finance, ecology, materials science, and anomaly detection, emphasizing a common logic: topology turns reorganizing structure into measurable signals for regime shifts, state transitions, and early warning. Across domains, these methods are most effective when the scientific target is organizational rather than scalar, when threshold ambiguity is intrinsic to the problem, and when topology functions as a structural diagnostic or feature extractor within a broader analytic pipeline. We conclude by identifying key limitations, including representation dependence, inferential challenges, interpretability, computational scaling, and the narrowness of one-parameter workflows, and by outlining a research agenda linking topology more closely to dynamics, causality, streaming decision support, topology-aware AI, and socio-technical resilience.