A long-standing challenge in economics lies not in the lack of intuition, but in the difficulty of translating intuitive insights into verifiable research. To address this challenge, we introduce AgentEconomist, an end-to-end interactive system designed to translate abstract intuitions into executable computational experiments. Grounded in a domain-specific knowledge base covering over 13,000 high-quality academic papers, the system employs a modular multi-stage architecture. Specifically, the Idea Development Stage generates literature-grounded hypotheses, the Experimental Design Stage configures simulator-aligned experimental parameters and protocols, and the Experimental Execution Stage runs experiments and returns structured analyses. Together, these stages form a human-in-the-loop, iterative workflow that translates economic intuitions into executable computational experiments. Through extensive experiments involving human expert evaluation and large language models (LLMs) as judges, we show that the system generates research ideas with stronger literature grounding and higher novelty and insight than state-of-the-art generic LLMs. Overall, AgentEconomist adopts a human-AI collaboration paradigm that enables researchers to focus on high-level intuitions, while delegating the labor-intensive processes of translation and computational execution to agents.

翻译：经济学中一个长期存在的挑战并非缺乏直觉，而是难以将直觉洞见转化为可验证的研究。为应对这一挑战，我们提出AgentEconomist——一个端到端交互式系统，旨在将抽象直觉转化为可执行的计算实验。该系统建立在覆盖13,000余篇高质量学术论文的领域知识库基础上，采用模块化多阶段架构。具体而言，思路开发阶段生成基于文献支撑的假设，实验设计阶段配置与仿真器对齐的实验参数与协议，实验执行阶段运行实验并返回结构化分析结果。这些阶段共同构成一个人在回路中的迭代工作流，将经济直觉转化为可执行的计算实验。通过包含人类专家评估与大型语言模型（LLM）评判的广泛实验，我们证明该系统生成的科研思路在文献支撑度、新颖性和洞察力方面均优于最先进的通用LLM。总体而言，AgentEconomist采用人机协作范式，使研究者得以聚焦高层次直觉，同时将翻译与计算执行等劳动密集型流程委托给智能体。