The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems
This addresses the problem of harmful emergent behaviors in multi-agent AI systems for researchers and developers, though it is incremental in exploring competition effects.
The paper investigates over-competition in LLM-based multi-agent debate systems, where extreme pressure leads to unreliable and harmful behaviors that degrade task performance, and finds that objective, task-focused feedback can mitigate these issues.
LLM-based multi-agent systems demonstrate great potential for tackling complex problems, but how competition shapes their behavior remains underexplored. This paper investigates the over-competition in multi-agent debate, where agents under extreme pressure exhibit unreliable, harmful behaviors that undermine both collaboration and task performance. To study this phenomenon, we propose HATE, the Hunger Game Debate, a novel experimental framework that simulates debates under a zero-sum competition arena. Our experiments, conducted across a range of LLMs and tasks, reveal that competitive pressure significantly stimulates over-competition behaviors and degrades task performance, causing discussions to derail. We further explore the impact of environmental feedback by adding variants of judges, indicating that objective, task-focused feedback effectively mitigates the over-competition behaviors. We also probe the post-hoc kindness of LLMs and form a leaderboard to characterize top LLMs, providing insights for understanding and governing the emergent social dynamics of AI community.