超越低效：多智能体蒙特卡洛模拟中不文明行为的系统性成本
Beyond Inefficiency: Systemic Costs of Incivility in Multi-Agent Monte Carlo Simulations
AI总结：
研究通过多智能体蒙特卡洛模拟探讨不文明行为对交互效率的影响，发现参数较少的模型收敛延迟更显著，并发现先发优势在不同毒性条件下均显著。
摘要：
无建设性辩论和不文明交流已 documented 对生产力和凝聚力的负面影响，但孤立其对操作效率的影响极具挑战性。本研究通过基于大语言模型的多智能体系统作为受控社会学沙盒，系统操控沟通行为。利用蒙特卡洛模拟框架，生成数千次结构化的1对1对抗辩论，在不同毒性条件下测量收敛时间作为交互效率代理。基于先前研究，扩展至两种参数规模不同的LLM代理，评估毒性行为对辩论动态影响的泛化性。先前研究报告的25%延迟得到确认，发现参数较少的模型延迟更显著。进一步发现先发优势显著，发起讨论的代理在不同毒性条件下均显著胜出。
英文摘要：
Unconstructive debate and uncivil communication carry well-documented costs for productivity and cohesion, yet isolating their effect on operational efficiency has proven difficult. Human subject research in this domain is constrained by ethical oversight, limited reproducibility, and the inherent unpredictability of naturalistic settings. We address this gap by leveraging Large Language Model (LLM) based Multi-Agent Systems as a controlled sociological sandbox, enabling systematic manipulation of communicative behavior at scale. Using a Monte Carlo simulation framework, we generate thousands of structured 1-on-1 adversarial debates across varying toxicity conditions, measuring convergence time, defined as the number of rounds required to reach a conclusion, as a proxy for interactional efficiency. Building on a prior study, we replicate and extend its findings across two additional LLM agents of varying parameter size, allowing us to assess whether the effects of toxic behavior on debate dynamics generalize across model scale. The convergence latency of 25% reported in the previous study was confirmed. It was found that this latency is significantly bigger for models with fewer parameters. We further identify a significant first-mover advantage, whereby the agent initiating the discussion wins significantly above chance regardless of toxicity condition.
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