The 57th Frontier Forum on Digital Technology and Economic Finance

Abstract: Large language models reason fluently about economics, but their generative reasoning is bounded by what they memorised at training time and by how a query routes their attention. We augment that reasoning with structured retrieval over the field’s own literature. Each paper in a curated corpus is extracted against PromptEcon, a fixed vocabulary of 9 categories and 51 elements linked by typed edges, and the per-paper instances merge by canonical concept into a single graph. HippoRAG-style retrieval over that graph augments the model’s context before it generates — re-ranking its parametric memory toward the relevant region and supplying post-cutoff work it never trained on. We run the method as a pilot study on the overlapping-generations literature and evaluate it with a blind expert-panel exam and an algorithm- and code-generation test. The approach is not specific to OLG: it is a reproducible recipe for turning any structured economics literature into a retrieval substrate for AI-augmented research.

Speaker Introduction：

Zhigang Feng is a Professor in the Department of Economics at the University of Nebraska at Omaha, USA, and a Chair Professor at the School of Economics, Zhejiang University. He serves as an Executive Committee Member and the Mentor Program Director of the Chinese Economists Society (CES), and is the founder of the interview column "Jingbang Lunce" (Economic Policy and Strategy). His research focuses primarily on macroeconomics, artificial intelligence and machine learning, and computational economics. Some of his research outcomes have received multiple grants from the Swiss National Science Foundation, the Swiss National Supercomputing Centre, and the U.S. National Science Foundation, and have been published in international journals such as the International Economic Review, Quantitative Economics, Review of Economic Dynamics, and Economic Theory. He founded the Bilibili channel "Zhongnan Macro", which teaches advanced macroeconomics and quantitative macroeconomics. His textbook, Machine Learning and Quantitative Macroeconomics: A Practical Guide with PyTorch, was published by Peking University Press in 2025.