大数据驱动政府决策智能化的协同演进逻辑——基于技术驱动与治理调适的分析框架

Collaborative Evolution Logic of Big Data-Driven Intelligent Government Decision-Making—A Framework of Technological Drive and Governance Adaptation

摘要: 大数据与人工智能正驱动政府决策向智能化转型，成为全球治理现代化的重要议题。当前研究显示，该领域在国内外均呈现出从“技术赋能”到“风险反思”再到“制度构建”的阶段性演进特征，但因制度环境差异，其研究重心有所不同。国外研究较早聚焦于算法问责、伦理框架与法律适配，注重个体权利与程序正义；国内研究则更强调国家战略引领下的整体智治、场景创新与治理效能，体现出技术应用与治理体系深度融合的本土路径。基于此，研究进一步提出“技术迭代–治理适应”协同演进框架，指出政府决策智能化并非单纯的技术引入过程，而是技术能力与治理体系在持续互动中动态调适的进程。未来研究可在深化“技术–制度”互动机制、开展过程追踪与情境化分析、构建兼顾多价值的综合评估体系、以及探索资源差异条件下的包容性实施路径等方面持续推进，以推动形成更稳健、可信且适应性强的智能决策生态。

Abstract: Big data and artificial intelligence are driving the transformation of government decision-making toward intelligence, making it a critical issue in global governance modernization. Current research reveals that this field exhibits a stage-based evolution from “technology empowerment” to “risk reflection” and then to “institutional construction” both domestically and internationally. However, due to differences in institutional environments, research priorities vary. International studies have long focused on algorithmic accountability, ethical frameworks, and legal adaptation, emphasizing individual rights and procedural justice. In contrast, domestic research prioritizes holistic smart governance, scenario-based innovation, and governance efficiency under national strategic guidance, reflecting a local pathway characterized by the deep integration of technological application and governance systems. Building on this, the study further proposes a “technological iteration—governance adaptation” collaborative evolution framework, arguing that intelligent government decision-making is not merely a process of technology introduction but a dynamic adjustment process in which technological capacity and governance systems continuously interact. Future research should deepen the understanding of “technology-institution” interaction mechanisms, employ process-tracing and contextualized analysis, develop comprehensive evaluation systems that balance multiple values, and explore inclusive implementation pathways under varying resource conditions, thereby fostering a more robust, trustworthy, and adaptive intelligent decision-making ecosystem.