Split over $n$ resource sharing problem: Are fewer capable agents better than many simpler ones?

arXiv cs.RO / 4/30/2026

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Key Points

  • The paper tackles a multi-agent decision problem: whether limited resources should be concentrated in a few highly capable agents or distributed across many simpler ones.
  • It introduces the “split over n” resource sharing problem, analyzing scenarios where n agents share a common pool and where agent footprints scale as 1/n in a multi-agent disk-coverage case study.
  • The analysis finds that the initial coverage rate can increase with the number of agents, but overall performance depends strongly on how agent speed scales with size or footprint.
  • If agent speed decreases proportionally with their radii, larger groups perform about equally well, while if speed decreases proportionally with their footprints, a single agent outperforms larger groups.
  • Simulations indicate that splitting resources can increase individual agent failure rates, providing guidance for choosing an optimal level of distributiveness in resource-constrained multi-agent system design.

Abstract

In multi-agent systems, should limited resources be concentrated into a few capable agents or distributed among many simpler ones? This work formulates the split over n resource sharing problem where a group of n agents equally shares a common resource (e.g., monetary budget, computational resources, physical size). We present a case study in multi-agent coverage where the area of the disk-shaped footprint of agents scales as 1/n. A formal analysis reveals that the initial coverage rate grows with n. However, if the speed of agents decreases proportionally with their radii, groups of all sizes perform equally well, whereas if it decreases proportionally with their footprints, a single agent performs best. We also present computer simulations in which resource splitting increases the failure rates of individual agents. The models and findings help identify optimal distributiveness levels and inform the design of multi-agent systems under resource constraints.