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：人工智能代理越来越多地在多代理环境中运行，其中结果依赖于协调。我们将主要算法单一文化（即基线的行动相似性）与战略算法单一文化区分开来，后者中代理会根据激励调整相似性。我们实施了一个简单的实验设计，清晰地将这些因素分离，并将其应用于人类和大型语言模型主体。大型语言模型表现出高水平的基线相似性（主要单一文化），并且与人类一样，它们会根据协调激励对其进行调节（战略单一文化）。虽然大型语言模型在相似行动上的协调非常好，但当差异获得奖励时，它们在维持异质性方面落后于人类。