人工智能时代农业经济学研究范式的演化与重构

江西财经大学学报 ›› 2026, Vol. 0 ›› Issue (3): 103-114.

• “三农”研究·人工智能专题 • 上一篇下一篇

人工智能时代农业经济学研究范式的演化与重构

司伟, 吴琦

中国农业大学经济管理学院,北京 100083

收稿日期:2026-03-26 修回日期:2026-04-09 出版日期:2026-05-25 发布日期:2026-05-29
通讯作者: 吴琦,中国农业大学讲师,经济学博士,主要从事农食系统微观主体行为经济学研究,联系方式qiwu.ecm@cau.edu.cn。
作者简介:司伟,中国农业大学教授,管理学博士,博士生导师,主要从事农业与食物政策研究。
基金资助:
教育部哲学社会科学研究重大专项“习近平总书记关于农业农村现代化的重要论述研究”（2024JZDZ059）; 国家自然科学基金面上项目“转变居民膳食结构以实现大豆需求减量：路径与影响研究”（72573169）; 中国博士后科学基金面上项目“人工智能种植决策技术提升农业生产效率的内在机理与实现路径研究”（2024M763598）

The Evolution and Reconstruction of Research Paradigms in Agricultural Economics in the Era of Artificial Intelligence

Si Wei, Wu Qi

China Agricultural University, Beijing 100083, China

Received:2026-03-26 Revised:2026-04-09 Online:2026-05-25 Published:2026-05-29

摘要/Abstract

摘要： 人工智能正在重构科研范式,但在农业经济学中的应用尚不充分。因此,需要系统讨论其在农业经济学中的应用路径、面临的冲突及解决思路。人工智能已从自然科学向社会科学及经济学领域演进,并在微观行为分析与政策模拟中得到应用。然而,人工智能所侧重的预测能力与农业经济学中的因果识别导向存在冲突,这是当前农业经济学科应用人工智能的主要障碍。考虑到农业自然再生产的不确定性与中国小农户为主的生产制度的复杂性,盲目套用通用模型可能带来决策偏见与风险。一种可行的方案是将人工智能嵌入因果推断框架,使其在高维数据结构下辅助变量选择、工具变量构造及反事实预测。此外,研究者在使用人工智能工具时应从单纯的数据处理转向知识可靠性评价,利用理论功底和实地调研经验纠偏算法局限。上述思路为智能时代的农业经济学提供了一个兼顾预测优势与因果推断需求的方法论框架。

关键词: 人工智能, 农业经济学, 研究范式, 预测能力, 因果识别

Abstract: Artificial intelligence is reshaping the paradigm of scientific research, yet its application in agricultural economics remains insufficient. Therefore, a systematic discussion is needed on its implementation pathways, conflicts faced, and resolution strategies within agricultural economics. Artificial intelligence has evolved from the natural sciences into the fields of social sciences and economics, finding applications in microbehavioral analysis and policy simulation. However, the predictive ability emphasized by artificial intelligence conflicts with the causal identification orientation in agricultural economics, which is currently the main obstacle to the application of artificial intelligence in agricultural economics. Considering the uncertainty of agricultural natural reproduction and the complexity of China’s production system dominated by small-scale farmers, blindly applying generic models may lead to decision-making biases and risks. A feasible solution is to integrate artificial intelligence into causal inference frameworks, enabling it to assist in variable selection, instrument variable construction, and counterfactual prediction under high-dimensional data structures. In addition, researchers should shift from simple data processing to knowledge reliability evaluation when using artificial intelligence tools, and use theoretical knowledge and field research experience to correct algorithm limitations. The above ideas provide a methodological framework for agricultural economics in the intelligent era that balances predictive advantages and causal inference needs.

Key words: artificial intelligence, agricultural economics, research paradigm, predictive power, causal identification

中图分类号:

F320

司伟, 吴琦. 人工智能时代农业经济学研究范式的演化与重构[J]. 江西财经大学学报, 2026, 0(3): 103-114.

Si Wei, Wu Qi. The Evolution and Reconstruction of Research Paradigms in Agricultural Economics in the Era of Artificial Intelligence[J]. Journal of Jiangxi University of Finance and Economics, 2026, 0(3): 103-114.

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人工智能时代农业经济学研究范式的演化与重构

The Evolution and Reconstruction of Research Paradigms in Agricultural Economics in the Era of Artificial Intelligence

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