从经济主体到主体性经济：经济世界模型的系统蓝图

摘要

经济世界模型（EWMs）是一类生成式经济模型，通过对异质性智能体及其信念与行动，以及它们相互作用产生总体结果所依托的市场与制度机制进行建模，模拟经济如何从内部演化。本文制定了一条实施路线图，旨在将经济世界模型构建为生成引擎，使异质性智能体在其中行动、交互、适应，并与市场和制度共同演化，从而从内部产生经济动态。我们将EWM系统组织为六级能力阶梯：从基于固定规则的智能体世界，到自适应和基于大语言模型的智能体世界，再到自我演化智能体、演化制度世界，以及与现实观测对齐的仿真到现实经济孪生。对这些层级的系统性文献综述表明，现有工作仍集中在较低层级的智能体和仿真环境中，而具备自我演化智能体、内生制度、持续实证对齐以及经过验证的经济机制的系统仍然罕见。通过将EWM议程转化为实施蓝图，本文旨在加速下一代经济仿真环境的发展，使其能够作为人类决策者的高保真沙盒，并作为AI智能体的训练、规划、评估和安全基础。我们发布了一份精选论文列表及相关资源，以支持未来研究。

English

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.