How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of decentralized coordination in markets, we study this question through an agent economy in which agents compete via auctions for the right to act, exchange payments, and accumulate wealth from environmental rewards. These simple economic signals induce decentralized credit assignment, driving planning without global orchestration or explicit communication protocols. The population evolves through economic selection: effective agents accumulate wealth and are mutated via exploitation, while ineffective ones go bankrupt and are replaced via exploration. We show that, initialized with weak agents, the economy produces emergent multi-step reasoning strategies and outperforms stronger monolithic baselines across five agentic tasks, including mathematical reasoning, financial research, scientific research, accelerator design, and distributed-system optimization. We further provide theoretical insights into how economic dynamics shape agent behaviors, linking local incentives to long-term global performance. Our results suggest a new path to multi-agent intelligence: rather than engineering coordination, we can design decentralized incentive structures under which it automatically emerges.

翻译：一群智能体如何在无集中控制下自我编排并自适应为更强的集体智能？受弗里德里希·哈耶克关于市场分散协调的经济学理论启发，我们通过一个智能体经济体研究该问题——其中智能体通过拍卖竞争行为权、交换支付，并从环境奖励中积累财富。这些简单的经济信号诱导出分散式信用分配，无需全局编排或显式通信协议即可驱动规划。该群体通过经济选择演化：高效智能体积累财富并通过利用机制发生变异，而低效智能体则破产并通过探索机制被取代。我们证明，从弱智能体初始化出发，该经济体可涌现出多步推理策略，并在五项智能体任务（包括数学推理、金融研究、科学研究、加速器设计及分布式系统优化）中超越强大的单一基线模型。我们进一步提供关于经济动力学如何塑造智能体行为的理论洞见，将局部激励与长期全局表现相联结。研究结果表明了一条通往多智能体智能的新路径：与其设计协调机制，不如构建分散式激励结构——智能体智能将在其中自动涌现。