When resources are scarce, will a population of AI agents coordinate in harmony, or descend into tribal chaos? Diverse decision-making AI from different developers is entering everyday devices -- from phones and medical devices to battlefield drones and cars -- and these AI agents typically compete for finite shared resources such as charging slots, relay bandwidth, and traffic priority. Yet their collective dynamics and hence risks to users and society are poorly understood. Here we study AI-agent populations as the first system of real agents in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity. We show empirically and mathematically that when resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload, though tribe formation lessens this risk. Meanwhile, some individuals profit handsomely. When resources are abundant, the same ingredients drive overload to near zero, though tribe formation makes the overload slightly worse. The crossover is arithmetical: it is where opposing tribes that form spontaneously first fit inside the available capacity. More sophisticated AI-agent populations are not better: whether their sophistication helps or harms depends entirely on a single number -- the capacity-to-population ratio -- that is knowable before any AI-agent ships.

翻译：当资源稀缺时，人工智能体群体会协调共生，还是陷入部落式混乱？来自不同开发者的多样化决策AI正进入日常设备——从手机、医疗设备到战场无人机和汽车——这些智能体通常需要竞争有限的共享资源，如充电桩位、中继带宽和交通优先权。然而，其集体动力学特性以及对用户和社会的风险尚未得到充分理解。本研究首次将人工智能体群体作为真实智能体系统，对其集体行为的四个关键调控变量进行独立操控：先天特性（大语言模型固有多样性）、后天培育（个体强化学习）、文化形成（自组织部落涌现）以及资源稀缺性。我们通过实证与数学模型证明：当资源稀缺时，AI模型多样性与强化学习会加剧系统过载风险，而部落形成能适度缓解此风险，同时部分个体获得超额收益。当资源充裕时，相同要素却能将系统过载率趋近于零，尽管部落形成会轻微加剧过载。其临界转换遵循算术规律：当自发形成的对立部落首次适配可用容量时发生。更复杂的人工智能体群体未必更优：其复杂化究竟产生助益还是损害，完全取决于一个可预知的单一参数——容量人口比——该参数可在任何AI智能体部署前确定。