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

2026-04-29

AI Summary

This study investigates the trade-off in multi-agent systems between concentrating limited resources on a few high-performance agents and distributing them across a larger number of simpler agents. It formally introduces, for the first time, the *n*-partition resource allocation problem and develops a disk-footprint coverage model to analyze this trade-off. Through a combination of theoretical analysis and simulations, the work reveals a nonlinear relationship between resource allocation strategies and system performance. Results show that initial coverage improves with agent count; when agent speed scales inversely with sensing radius, systems of varying scales perform comparably, whereas performance favors a single agent when speed scales inversely with sensing area. Although partitioning resources enhances coverage potential, it concurrently increases vulnerability to individual agent failures.

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.

Problem

• multi-agent systems
• resource sharing
• agent capability
• resource allocation
• system distributiveness

Innovation

• resource sharing
• multi-agent systems
• coverage optimization
• scalability analysis
• agent distributiveness