大数据驱动的政府决策智能化：理论演进、实践路径与未来展望

Big Data-Driven Intelligent Government Decision-Making: Theoretical Evolution, Practical Pathways, and Future Prospects

摘要: 大数据与人工智能技术的融合，正在推动政府决策从主要依靠经验转向更多依赖数据。文章对近十年来该领域的重要研究进行了梳理。文章首先回顾了理论发展过程，指出研究焦点从早期的循证决策，逐步扩展到数字治理，并进一步深入到对算法决策的批判性审视。其次，分析了由数据、智能分析和实际应用构成的技术体系如何支持决策，并探讨了其在城市管理、公共服务等场景中带来的效率提升与相关风险。文章特别总结了我国在该领域的实践，其特点是注重战略统筹与制度协同，并将增进民众福祉作为价值旨归。最后，文章指出了当前研究存在的不足，并建议未来应更关注技术应用的微观过程、构建有效的风险治理机制，以及发展符合本土实际的理论解释。

Abstract: The convergence of big data and artificial intelligence is driving a shift in government decision-making from primarily relying on experience to depending more on data. This paper reviews significant research in this field over the past decade. It begins by tracing the theoretical development, noting that the research focus has expanded from early evidence-based policymaking to digital governance, and further to a critical examination of algorithmic decision-making. Secondly, it analyzes how the technical system, comprising data, intelligent analysis, and practical applications, supports decision-making, and explores the efficiency gains and associated risks in scenarios such as urban management and public service. The paper particularly summarizes China’s practices in this area, which are characterized by a focus on strategic planning and institutional coordination, with the ultimate goal of enhancing public well-being. Finally, it identifies shortcomings in current research and suggests that future work should pay more attention to the micro-processes of technological application, construct effective risk governance mechanisms, and develop theoretical explanations tailored to local contexts.