心智经济：通过经济交互涌现的多智能体智能

摘要

在没有集中控制的情况下，一个智能体种群如何通过自组织和自适应形成更强的集体智能？受弗里德里希·哈耶克关于市场去中心化协调的经济学理论启发，我们通过一个智能体经济系统研究此问题——智能体通过竞拍争夺行动权、交换支付并积累环境奖励带来的财富。这些简单的经济信号实现了去中心化的信用分配，在无需全局编排或显式通信协议的情况下驱动规划行为。种群通过经济选择演化：高效智能体积累财富并通过开发机制产生变异，低效者则破产并通过探索机制被替换。研究表明，从弱智能体初始化开始，该经济系统能涌现出多步推理策略，并在数学推理、金融研究、科学研究、加速器设计及分布式系统优化五项智能体任务中超越强大的单体基线模型。我们进一步提供了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联系起来。我们的研究结果为多智能体智能开辟了新路径：无需设计协调机制，只需构建去中心化激励结构，协作行为即可自动涌现。

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.