從經濟主體到主體性經濟：經濟世界模型的系統藍圖

摘要

經濟世界模型（Economic World Models, EWMs）是生成式經濟模型，透過建模異質主體、其信念與行動，以及其互動藉以產生總體結果的市場與制度機制，來模擬經濟如何從內部演化。本文制定了一份實施路線圖，旨在將經濟世界模型建構為生成引擎，在其中異質主體行動、互動、適應，並與市場和制度共同演化，從而由內而外地產生經濟動態。我們將 EWM 系統劃分為六級能力階梯，從固定規則基礎的主體世界，到自適應與基於大型語言模型（LLM）的主體世界、自我演化主體、演化制度世界，以及與真實觀測對齊的從模擬到真實（sim-to-real）經濟孿生。橫跨這些層級的系統性文獻回顧顯示，現有研究仍集中在較低層級的主體與模擬環境，而具備自我演化主體、內生制度、持續實證對齊以及經過驗證的經濟機制的系統仍然罕見。透過將 EWM 議程轉化為實施藍圖，本文旨在加速下一代經濟模擬環境的發展，使其既可作為人類決策者的高保真沙盒，也可作為 AI 智能體的訓練、規劃、評估與安全基底。我們發布了一份精選論文清單及相關資源，以支持未來研究。

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.