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[ARTICLE · art-19930] src=arxiv.org pub= topic=artificial-intelligence verified=true sentiment=↑ positive

Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

Researchers have developed a multi-agent AI system that uses economic principles—specifically market-based auctions and wealth accumulation—to enable decentralized coordination among agents without centralized control. The system, detailed in a new preprint, outperformed stronger monolithic AI models across five complex tasks including mathematical reasoning and scientific research by allowing effective agents to accumulate wealth and evolve while bankrupt agents are replaced. The findings suggest that designing incentive structures, rather than engineering explicit coordination protocols, can lead to emergent collective intelligence in multi-agent systems.

read1 min publishedJun 3, 2026

arXiv:2606.02859v1 Announce Type: new Abstract: 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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