Toward an enhanced scalability framework for human-multi-agent teams

Abstract

As autonomous systems become increasingly capable, the deployment of Human-Multi-Agent Teams (HMATs) offers immense potential for executing complex, decentralized tasks. However, realizing this potential is constrained by a critical bottleneck: the cognitive and supervisory capacity of the human operator. While foundational fan-out models established crucial groundwork for predicting this capacity, they are structurally limited to sequential, one-to-one interactions and rely on ambiguous variables that often produce overly optimistic predictions in real-world environments. This article synthesizes a multi-year empirical research program to present a comprehensive, new framework for modeling human-autonomy scalability. First, we deconstruct the variables of legacy models into human and robot elements, demonstrating that true scalability is a relational property driven by human-robot alignment rather than raw machine autonomy. We then validate the necessity of a mathematically bounded architecture to ensure predictive stability under operational uncertainty. Finally, we extend this framework to mathematically capture the paradigm shift toward group-based, one-to-many teaming. By analyzing the results of a complex, multi-agent simulation, we reveal the fundamental, quantifiable trade-off between increasing team scale and maintaining human oversight, providing AI system designers with a validated blueprint for optimizing the next generation of human-autonomy teams.