The Subtle Art of Defection: Understanding Uncooperative Behaviors in LLM based Multi-Agent Systems

• URL: http://arxiv.org/abs/2511.15862v1
• Date: Wed, 19 Nov 2025 20:39:19 GMT
• Title: The Subtle Art of Defection: Understanding Uncooperative Behaviors in LLM based Multi-Agent Systems
• Authors: Devang Kulshreshtha, Wanyu Du, Raghav Jain, Srikanth Doss, Hang Su, Sandesh Swamy, Yanjun Qi,
• Abstract summary: This paper introduces a novel framework for simulating and analyzing how uncooperative behaviors can destabilize or collapse multi-agent systems.<n>Our framework includes two key components: (1) a game theory-based taxonomy of uncooperative agent behaviors, and (2) a multi-stage simulation pipeline that dynamically generates and refines uncooperative behaviors as agents' states evolve.
• Score: 22.357102759752234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel framework for simulating and analyzing how uncooperative behaviors can destabilize or collapse LLM-based multi-agent systems. Our framework includes two key components: (1) a game theory-based taxonomy of uncooperative agent behaviors, addressing a notable gap in the existing literature; and (2) a structured, multi-stage simulation pipeline that dynamically generates and refines uncooperative behaviors as agents' states evolve. We evaluate the framework via a collaborative resource management setting, measuring system stability using metrics such as survival time and resource overuse rate. Empirically, our framework achieves 96.7% accuracy in generating realistic uncooperative behaviors, validated by human evaluations. Our results reveal a striking contrast: cooperative agents maintain perfect system stability (100% survival over 12 rounds with 0% resource overuse), while any uncooperative behavior can trigger rapid system collapse within 1 to 7 rounds. These findings demonstrate that uncooperative agents can significantly degrade collective outcomes, highlighting the need for designing more resilient multi-agent systems.

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