From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models
This paper provides a blueprint for developing advanced economic simulation environments for human decision-makers and AI agents, addressing the current lack of high-fidelity models.
This paper proposes a six-level capability ladder for Economic World Models (EWMs), which are generative economic models simulating economic evolution through heterogeneous agents and market mechanisms. The authors find that current research is concentrated in lower-level agent and simulation environments, with higher-level systems like self-evolving agents and endogenous institutions being rare.
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.