Multilayered Emergence: Modeling and Detecting Cross-Scale Emergent Patterns in Heterogeneous Multi-Agent Systems

Most current work focuses on either micro-level (agent) or macro-level (global outcome) emergence, often missing the rich tapestry of patterns that emerge at intermediate scales—such as subgroups, roles, or layered coordination (see D’Alfonso & Fedele, 2024, for synchronization at the group level). This idea proposes a novel simulation and analysis toolkit that explicitly tracks, models, and visualizes emergence across multiple organizational levels in heterogeneous MAS. By using multilayer network analysis, information-theoretic metrics, and hierarchical clustering, the framework could identify, for example, when a MAS is exhibiting emergent sub-team formation, or when local role allocation is driving global outcomes. Such an approach would challenge current one-scale-at-a-time frameworks, providing a more nuanced understanding of emergent phenomena and potentially enabling targeted interventions at the right scale.

References:

1. An Emergent Synchronization Property in a Set of Non-Competing Multi-Agent Systems. L. D’Alfonso, G. Fedele (2024). IEEE Control Systems Letters.