Reframing the human–robot ratio: Collaborative autonomy and the attention‐demand economy

Abstract The human–robot ratio is a useful design heuristic for supervisory robot control. However, advances in AI, computation, and reliable autonomy enable existing systems to extend beyond hierarchical supervision to more flexible organizations of interdependent team members. The human–robot ratio can be misleading for such organizations, so there is a need for a new way to understand teams of interdependent humans, robots, and AI agents. We propose the attention‐demand economy , a graph‐theoretic abstraction in which agents generate and transform attentional demands subject to bounded capacity. This abstraction exposes nonlinear scaling behavior, cascading supervisory requirements, and coordination bottlenecks that are masked under models that rely solely on the human–robot ratio. We demonstrate how the model can be used to (1) develop new system‐level metrics and (2) discover the source and extent of cascading failures. This paper also shows how a radar chart can be used to understand the overall difficulty of managing a human–robot team. The paper briefly demonstrates how the radar chart can be used to design the attention‐demand model. The primary message of this paper is a call to shift from optimizing human–robot ratios to designing organizations with justified attention flows.