Do human specialization constraints generalize to AI multi-agent systems?

Ascertain whether the constraints that make specialization advantageous in human collaborative teams also apply to AI multi-agent systems trained via reinforcement learning, determining if the same environmental and task-induced limitations govern the optimality of specialization in artificial agents.

Background

The paper challenges the common assumption that specialization is universally desirable in multi-agent systems by contrasting human teams—where specialization can incur training overhead, rigidity, and failure risk—with generalist strategies that maximize adaptability through parallel execution. Building on this, the authors question whether the constraints that make specialization beneficial for humans also hold in AI contexts, motivating a framework grounded in task parallelizability and environmental bottlenecks.

This open question frames the paper’s central investigation: using distributed systems principles (Amdahl’s Law) to predict when generalist versus specialist strategies should emerge in multi-agent reinforcement learning, and whether such predictions align with observed behaviors in Overcooked-AI experiments.