用经济机制让智能体自组织，无需中央控制。
Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions
- 智能体通过拍卖竞争行动权，靠收益积累财富
- 弱智能体被淘汰，强智能体进化，产生多步推理策略
- 适合研究去中心化AI系统或自动协作机制的人
如何让一群智能体在无中央控制下自发形成更强的集体智能？受弗里德里希·哈耶克市场去中心化协调理论启发，我们构建了一个智能体经济体系：智能体通过拍卖竞争行动权，进行支付交换，并从环境奖励中积累财富。这些简单的经济信号实现去中心化的信用分配，推动规划而无需全局调度或显式通信协议。群体通过经济选择演化：高效智能体积累财富并经由利用进行突变，低效者破产并由探索替代。实验显示，初始为弱智能体时，该经济体系能涌现出多步推理策略，在五项智能任务（数学推理、金融研究、科研、加速器设计、分布式系统优化）上超越更强的单体基线模型。我们进一步提供理论分析，揭示经济动态如何塑造智能体行为，将局部激励与长期全局性能联系起来。结果表明，通往多智能体智能的新路径是：不需人工设计协调，只需构建能自然催生协作的去中心化激励结构。
原文摘要 · Abstract (English)
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.
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