经济模拟的六个台阶：AI 能自己进化出市场吗？
我们总让 AI 预测经济，但这篇想的是：让 AI 直接活在经济里，自己当买家、卖家、银行，从内部把经济“长”出来。研究者给这种“经济世界模型”画了张六层能力图：从固定规则的玩具世界，到用大模型当智能体、智能体自己会进化、制度也会跟着变，最后到和真实数据对齐的“经济孪生”。翻遍现有文献，绝大多数还停在最底下两层——能跑、能玩，但离“自己进化出制度”差得远。它不是你明天能用上的东西，但它划出了 AI 经济模拟从“沙盘”到“社会”的路线图。
📄 原文摘要(英文)
Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within by modeling heterogeneous agents, their beliefs and actions, and the market and institutional mechanisms through which their interactions produce aggregate outcomes. This paper develops an implementation roadmap for building economic world models as generative engines in which heterogeneous agents act, interact, adapt, and co-evolve with markets and institutions, thereby producing economic dynamics from the inside. We organize EWM systems into a six-level capability ladder, from fixed rule-based agent worlds to adaptive and LLM-based agent worlds, self-evolving agents, evolving institutional worlds, and sim-to-real economic twins aligned with real observations. A systematic literature survey across these levels reveals that existing work remains concentrated in lower-level agent and simulation environments, while systems with self-evolving agents, endogenous institutions, persistent empirical alignment, and validated economic mechanisms remain rare. By translating the EWM agenda into an implementation blueprint, this paper aims to accelerate the development of the next generation of economic simulation environments that can serve as high-fidelity sandboxes for human decision-makers and as training, planning, evaluation, and safety substrates for AI agents. We release a curated paper list and related resources to support future research.