AI agents increasingly operate in multi-agent environments where outcomes depend on coordination. We distinguish primary algorithmic monoculture -- baseline action similarity -- from strategic algorithmic monoculture, whereby agents adjust similarity in response to incentives. We implement a simple experimental design that cleanly separates these forces, and deploy it on human and large language model (LLM) subjects. LLMs exhibit high levels of baseline similarity (primary monoculture) and, like humans, they regulate it in response to coordination incentives (strategic monoculture). While LLMs coordinate extremely well on similar actions, they lag behind humans in sustaining heterogeneity when divergence is rewarded.

翻译：人工智能体越来越多地运行在结果依赖于协调的多智能体环境中。我们将基础性算法单文化（基线行为相似性）与战略性算法单文化（智能体根据激励调整相似性）区分开来。我们实施了一个简洁的实验设计，清晰地分离了这两种作用力，并将其应用于人类和大型语言模型受试者。大型语言模型表现出高水平的基线相似性（基础单文化），并且与人类一样，它们会根据协调激励调节这种相似性（战略性单文化）。尽管大型语言模型在相似行动上协调得极好，但在奖励分歧时，它们在维持异质性方面落后于人类。