Predicting Multi-Agent Specialization via Task Parallelizability

• URL: http://arxiv.org/abs/2503.15703v2
• Date: Wed, 17 Sep 2025 19:17:12 GMT
• Title: Predicting Multi-Agent Specialization via Task Parallelizability
• Authors: Elizabeth Mieczkowski, Ruaridh Mon-Williams, Neil Bramley, Christopher G. Lucas, Natalia Velez, Thomas L. Griffiths,
• Abstract summary: We present a closed-form bound that predicts when specialization improves performance depending on task regimes and team size.<n>We validate our model on two standard MARL benchmarks that represent opposite benchmarks.<n>Three follow-up experiments in Overcooked-AI demonstrate that the model works in environments with more complex spatial and resource bottlenecks.
• Score: 8.465921582175426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When should we encourage specialization in multi-agent systems versus train generalists that perform the entire task independently? We propose that specialization largely depends on task parallelizability: the potential for multiple agents to execute task components concurrently. Drawing inspiration from Amdahl's Law in distributed systems, we present a closed-form bound that predicts when specialization improves performance, depending only on task concurrency and team size. We validate our model on two standard MARL benchmarks that represent opposite regimes -- StarCraft Multi-Agent Challenge (SMAC, unlimited concurrency) and Multi-Particle Environment (MPE, unit-capacity bottlenecks) -- and observe close alignment between the bound at each extreme and an empirical measure of specialization. Three follow-up experiments in Overcooked-AI demonstrate that the model works in environments with more complex spatial and resource bottlenecks that allow for a range of strategies. Beyond prediction, the bound also serves as a diagnostic tool, highlighting biases in MARL training algorithms that cause sub-optimal convergence to specialist strategies with larger state spaces.

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