这篇论文用GPT和DeepSeek跑了24个六智能体世界，不看角色标签，只看机制本身能不能催生经济行为，结论挺反直觉。
该论文提出AI Agent经济学概念，研究在仅提供工作、转移、选举和分配机制但无预设策略时，智能体能否涌现经济关系。实验构建无生产边界测试和24个六智能体世界，覆盖GPT和DeepSeek模型。结果发现无生产任务时智能体间无实质转移，而引入已验证工作和稀缺任务后，转账、借贷、投票换访问权等策略出现。研究显示组织的形成取决于可执行权利和资源后果，而非角色标签或提示语言。
AI Agent Economics: Can Autonomous Economic Behavior Emerge among AI Agents under Minimal External Conditions?
Multi-agent studies commonly place AI agents in predefined games, markets, or roles, making it difficult to distinguish endogenous economic organization from behavior inherited from the scenario. We ask whether economic relations emerge when agents receive executable mechanisms for work, transfer, elections, and allocation but no prescribed social or economic strategy. We define AI Agent Economics as systems of production, allocation, consumption, exchange, and institutions that alter agents' future feasible actions. We develop a two-stage framework comprising a no-production boundary test and 24 independent six-agent worlds across GPT and DeepSeek. Without productive tasks, agents communicate and govern resource provision but show no substantive inter-agent transfer activity. With verified work and scarce task access, transfers, loans, access promises, vote-for-access exchanges, and allocation strategies emerge. Holding the election interface fixed, executable allocation authority increases differentiation while reducing failed allocation and prolonged exclusion. When energy becomes symbolic, continuation support disappears, yet competition over task access persists. These findings show that organization follows executable rights and resource consequences rather than role labels or prompt language, and motivate governance audits of the mechanisms that actually constrain agents' future actions.