Effects of Swarm Size Variability on Operator Workload

2026-04-23

AI Summary

This study addresses how dynamic changes in swarm size influence operator workload and human-swarm system performance. Grounded in workload history theory, the research employed a simulated unmanned aerial vehicle monitoring task (N = 34), integrating subjective workload ratings with objective performance metrics to systematically examine how the magnitude and direction of swarm size changes affect operators’ cognitive load and task performance. Results indicate that objective performance is relatively insensitive to small-scale changes, whereas subjective workload is significantly modulated by both the direction and magnitude of change. Specifically, minor reductions in swarm size leave residual cognitive load, modest increases are more readily managed, and large changes can trigger a cognitive reset. These findings reveal a novel workload regulation mechanism in dynamic human-swarm interaction and offer actionable strategies for real-time workload management.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: Human-Robot Interaction

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Real-world deployments of human--swarm teams depend on balancing operator workload to leverage human strengths without inducing overload. A key challenge is that swarm size is often dynamic: robots may join or leave the mission due to failures or redeployment, causing abrupt workload fluctuations. Understanding how such changes affect human workload and performance is critical for robust human--swarm interaction design. This paper investigates how the magnitude and direction of changes in swarm size influence operator workload. Drawing on the concept of workload history, we test three hypotheses: (1) workload remains elevated following decreases in swarm size, (2) small increases are more manageable than large jumps, and (3) sufficiently large changes override these effects by inducing a cognitive reset. We conducted two studies (N = 34) using a monitoring task with simulated drone swarms of varying sizes. By varying the swarm size between episodes, we measured perceived workload relative to swarm size changes. Results show that objective performance is largely unaffected by small changes in swarm size, while subjective workload is sensitive to both change direction and magnitude. Small increases preserve lower workload, whereas small decreases leave workload elevated, indicating workload residue; large changes in either direction attenuate these effects, suggesting a reset response. These findings offer actionable guidance for managing swarm-size transitions to support operator workload in dynamic human--swarm systems.

Problem

• swarm size variability
• operator workload
• human-swarm interaction
• workload fluctuation
• dynamic swarms

Innovation

• human-swarm interaction
• operator workload
• swarm size variability
• workload history
• cognitive reset