政策试验、迭代学习与治理转型——基于北京市街道管理改革的案例分析

Policy Experimentation,Iterative Learning and Governance Transformation——A Case Study of Sub-District Management Reform in Beijing

政策试验是中国改革经常采用的一项治理安排,它通过构建议题驱动的强化学习机制,为稳步推进治理转型提供了有效路径。已有研究大多聚焦于政策试验的组织模式,而对试验驱动的政策学习研究较少。从议题学习的视角,构建“迭代学习—治理转型”分析框架来考察,可以看到,政策试验并非一次性的测试过程,而是伴随着连续的迭代学习与知识生产。政策试验中的议题学习可分解为四个阶段:即议题驱动的概念创造、概念驱动的局部试验、绩效驱动的政策更新、议题再建构驱动的迭代学习。成功试验产生自我强化效应,从失败试验中也可汲取教训,推动政策试验走向迭代创新,实现知识的持续获取和应用。在案例研究中,政策试验的迭代学习得到印证:北京经历了多轮政策试验,顺利实现城市基层治理转型。

Policy experimentation is a governance arrangement usually adopted in China s reform,which provides an effective path to steadily promote governance transformation by constructing an enhanced learning mechanism that is driven by issues.Most of the existing studies focus on the organizational model of policy experimentation,whereas little research has been conducted on the policy learning driven by experimentation.From the perspective of issue learning,we construct an analytical framework of“iterative learning and governance transformation”.It is shown that policy experimentation is not a one-shot testing process but is accompanied by continuous iterative learning and knowledge production.Issue learning in policy experimentation can be divided into four stages:the conceptual creation driven by issues,the local experimentation driven by concepts,the policy renewal driven by performance,and the iterative learning driven by issue reconstruction.Successful experiments cause an effect of self-enhancement,and lessons can also be drawn from failed experiments,which will propel policy experimentation towards iterative innovation and the realization of the continuous acquisition and application of knowledge.In the case study,the iterative learning of policy experimentation has been confirmed:after several rounds of policy experimentation,Beijing has smoothly realized the urban grassroots governance transformation.