The Practice and Frontiers of Digital Public Governance

Digital public governance refers to public governance conducted in an era marked by digital technology. It includes both “governance with technology,” i.e., using digital technology as a tool to manage public affairs, and “governance of technology,” i.e., governing digital technology itself and its derivative problems, risks, and issues, as well as governing the tension between technological logic and public value logic, seeking institutional reconciliation and order reconstruction. What are the current development directions and trends of digital public governance? What limits are constricting its outward expansion? How should we shape the future of digital public governance? Against the backdrop of deepening the construction of Digital China, answering these questions constitutes both a forward-looking, normative reflection on the theory of digital public governance and a practical outlook on the future of digital development in China and the world.

数字公共治理是指在以数字技术为标志的时代开展的公共治理。它既包括“用技术治理”，即以数字技术为工具开展公共事务治理；也包括“对技术治理”，即针对数字技术本身及其衍生的问题、风险和议题进行治理；以及对技术逻辑与公共价值逻辑的张力进行治理，寻求制度性调和与秩序重建。数字公共治理当下的发展方向与趋势是什么？哪些限度正在收缩其外推的边界？我们应如何塑造数字公共治理的未来？在深入推进数字中国建设的时代背景下，回答上述问题既是对数字公共治理理论的前瞻性、规范性思考，也是对中国与世界数字化发展未来的实践展望。

Directions and Trends of Digital Public Governance

数字公共治理的方向与趋势

“Direction” addresses where governance might head amid the technological wave, while “trends” reveal the driving forces and inertia already evident behind these trajectories. The reason for sorting out the directions and trends of digital public governance is not to presuppose a fixed path, but to capture the clues of governance evolution amidst uncertainty, thereby understanding how digital public governance continuously breaks through old boundaries and generates new orders.

“方向”回答的是治理在技术浪潮中可能走向何处，“趋势”揭示的是这些走向背后已经显现的动力与惯性。之所以需要对数字公共治理的方向与趋势进行梳理，并不是为了预设一个固定的路径，而是为了在不确定中捕捉治理演化的线索，从而理解数字公共治理如何不断突破旧有边界、生成新的秩序。

1. Knowledge Base: From Empirical Induction to Data-Driven

1.知识基础：从经验归纳到数据驱动

Traditional decision-making models primarily rely on two types of knowledge sources: empirical judgments accumulated by operational departments through long-term practice, and theoretical generalizations formed by experts and scholars based on historical materials and existing research. In the context of a relatively stable industrial social governance environment and limited information scale, experience and theory can constitute a feasible basis for decision-making and support policy formulation and adjustment. However, this decision-making logic also has obvious limitations, such as information time lags, narrow data sources, and a lack of complex situation prediction.

传统决策模式主要依赖两类知识来源：实务部门在长期实践中积累的经验性判断，专家学者基于历史资料和既有研究形成的理论性归纳。在工业社会治理环境相对稳定、信息规模有限的背景下，经验与理论能够构成决策的可行依据，并支撑政策的生成与调整，但这种决策逻辑也存在信息时滞、数据来源狭窄、缺乏复杂情势预判等明显局限。

With the widespread application of digital technologies such as big data and artificial intelligence, the knowledge base of public governance is undergoing a profound transformation. An increasing amount of social operational information can be collected, processed, and analyzed in real time, making data an indispensable foundational component of public governance. This cognitive approach, starting from data, is driving public governance to gradually form a new knowledge paradigm—a data-driven cognitive and decision-making logic. The State Council’s “Guiding Opinions on Strengthening the Construction of Digital Government” proposes to “fully leverage the foundational resource role and innovative engine role of data, improve the scientific level of government decision-making and the efficiency of management and services,” which precisely indicates that data-driven has become an important direction for current governance transformation and is gradually being elevated to an institutional arrangement.

随着大数据、人工智能等数字技术的广泛应用，公共治理的知识基础经历深刻转型。越来越多的社会运行信息可以被实时采集、处理和分析，数据成为公共治理不可或缺的基础构件。这种以数据为出发点的认知方式，推动公共治理逐步形成一种新的知识范式——数据驱动的认知和决策逻辑。国务院《关于加强数字政府建设的指导意见》提出，“充分发挥数据的基础资源作用和创新引擎作用，提高政府决策科学化水平和管理服务效率”，正表明数据驱动已成为当下治理转型的重要方向，并逐步上升为制度安排。

In the process of governance operation, this shift in the knowledge base can be summarized as the cognitive chain of “data—modeling—simulation.” On the one hand, the scope and frequency of data collection are continuously expanding, with many aspects of social life being digitally recorded, providing rich and real-time knowledge materials for governance. On the other hand, algorithmic modeling is gradually embedded in institutionalized operations. Through the structured analysis of massive data, models not only assume the function of problem identification but also become important cognitive tools for assessing risks and predicting trends. Looking further, simulation and emulation technologies are beginning to be used in the policy process, enabling governments to conduct scenario analyses of different options in a virtual environment, thereby accumulating knowledge and experience before policies are implemented. Consequently, the generation mode of governance knowledge will exhibit new characteristics: shifting from reliance on limited experience and static induction to a greater reliance on data-driven dynamic iteration; shifting from a focus on post-hoc summarization to a greater emphasis on pre-hoc prediction.

在治理运行过程中，知识基础的这种转向可以概括为“数据—建模—模拟”的认知链条。一方面，数据采集范围和频率不断拓展，社会生活诸多环节被数字化记录，为治理提供丰富和实时的知识素材。另一方面，算法建模逐渐嵌入制度化运作，通过对海量数据的结构化分析，模型不仅承担了问题识别功能，也成为评估风险和预测趋势的重要认知工具。进一步看，模拟与仿真技术开始用于政策过程，使政府能够在虚拟环境下对不同方案进行推演，从而在政策落地前就积累知识经验。由此，治理知识的生成方式将呈现新的特点：由依赖有限经验与静态归纳，转向更多依托数据驱动的动态迭代；由侧重事后总结，转向更加注重事前预判。

2. Methodology: From Linear Reasoning to Prediction and Experimentation

2.方法论：从线性推理到预测与实验

In existing public governance research, policy analysis and formulation have largely relied on linear reasoning and empirical judgment. This research path mostly contains two important assumptions: first, that social processes have relative stability and causal relationships can be grasped through post-hoc induction; second, that governance objects possess a certain homogeneity, where “common denominators” or “averages” can represent overall patterns. However, the real world is often complex and changeable, and these assumptions face severe challenges: policy outcomes are usually accompanied by feedback effects and path dependence, and causal relationships are not singularly linear; the differentiation of governance objects is continuously expanding, and averages obscure the true situation of some groups.

在已有公共治理研究中，政策分析与制定多依赖线性推理和经验判断。这种研究路径大多存有两个重要假设：一是社会过程具有相对稳定性，因果关系可以通过事后归纳来把握；二是治理对象具有一定的同质性，“公约数”“均值”能够代表整体规律。但真实世界却往往复杂、多变，这些假设面临严峻挑战：政策结果通常伴随反馈效应和路径依赖，因果关系并非单一线性；治理对象的差异化不断扩大，平均值掩盖了一些群体的真实处境。

The digital era brings tools and methods to overcome these limitations. Big data analysis can identify patterns and trends in massive amounts of information, compensating for the shortcomings of traditional statistical methods in scale and real-time capability; machine learning algorithms optimize prediction and classification capabilities through continuous training, enabling governance to dynamically respond to complex environments; simulation and emulation technologies allow policies to be tested in multiple scenarios in a virtual environment in advance, without relying solely on single-point piloting.

数字时代带来了突破这些局限的工具和方法。大数据分析能够在海量信息中识别模式和趋势，弥补传统统计方法在规模和实时性上的不足；机器学习算法在不断训练中优化预测与分类能力，使治理能够动态应对复杂环境；仿真与模拟技术允许政策在虚拟环境中提前测试多种方案，而不必完全依赖单点试行。

At the same time, these new tools and methods are not piecemeal and mechanical additions; they act on the entire governance process simultaneously, forming a multi-dimensional shift: in the temporal dimension, governance is gradually moving away from “post-hoc summarization” towards “pre-hoc prediction” based on real-time data and predictive models; in the logical dimension, policy generation no longer relies on static causal chains but is continuously revised through dynamic iteration; in the object dimension, policies no longer only take the “majority” as a reference but respond to differences between groups and regions through finer-grained data analysis; in the spatial dimension, policy experimentation breaks free from the limitations of single-point piloting, enabling parallel simulation and comparison across multiple scenarios with the help of virtual simulation and digital twins. The overall reshaping of methodology by digital tools makes public governance more forward-looking, flexible, sensitive to differences, and spatially extensible.

同时，新的工具和方法不是单点和机械的增补，它们同时作用于治理全过程，形成了一种多维转向：在时间维度上，治理逐步告别“事后总结”，转向依托实时数据和预测模型的“事前预判”；在逻辑维度上，政策生成不再依赖静态因果链条，而是通过动态迭代不断修正；在对象维度上，政策不再仅以“大多数”为参照，而是通过更细颗粒度的数据分析，回应群体和区域间的差异性；在空间维度上，政策试验摆脱了单点试行的限制，借助虚拟仿真和数字孪生，能够在多场景中并行演练和比较。数字工具对方法论的整体重塑，使公共治理更具前瞻性、灵活性、差异敏感性与空间延展性。

3. Subject Relations: From Single-Center to Multi-Dimensional

3.主体关系：从单一中心到多元多维

Subject relations are a classic topic in public governance discussions. Government-centrism is a common feature of traditional public administration theory and practice. With social development and the diversification of public needs, the single-center structure has gradually revealed the limitations of governance. Since the end of the 20th century, academic understanding of subject relations has gradually shifted towards multi-actor interaction, and the focus of governance has also moved from control to coordination.

主体关系是公共治理讨论的经典议题。政府中心主义是传统公共管理理论与实践的普遍特征。随着社会发展和公共需求的多样化，单一中心结构逐渐显露出治理局限性。20世纪末以来，学界对主体关系的理解逐渐转向多元互动，治理的重心也由控制走向协同。

Entering the digital era, the evolution of subject relations has escalated from “multi-actor” to “multi-dimensional.” The subjects participating in governance have not only surged in number and expanded in type, but their modes of existence and logics of agency have also become heterogeneous. The “subjects” within the governance system are no longer limited to human individuals and organizations but have expanded to include technology itself, forming complex interactions across physical, social, and digital three-dimensional spaces, constituting multi-dimensional subject relations.

进入数字时代，主体关系的演进从“多元”迭升为“多维”。参与治理的主体不仅数量激增、类型扩展，其存在方式与能动逻辑也呈现出异质化。治理体系中的“主体”不再局限于人类个体与组织，而扩展至技术本身，并在物理、社会与数字三维空间中形成复杂互动，构成多元多维的主体关系。

The emergence of this type of subject relation is the result of the mutual reconstruction between digital technology and governance structures, and manifests in practice as trends towards collaboration, platformization, and networking. For example, facing public issues such as climate change and platform economy regulation, a single subject finds it difficult to cope alone. The effectiveness of governance depends on the complementarity of capabilities and resources among different subjects, and governance methods have shifted from past departmental segmentation to cross-boundary collaboration. This collaboration, in turn, relies on platform-based technological and institutional conditions. In this process, the functional boundaries of digital platforms are being reshaped, transforming from information channels into intermediaries and operating systems for multi-subject interaction, connecting government, enterprises, society, citizens, and technological subjects. Against the backdrop of the evolution of public governance subject relations towards a multi-dimensional direction, state authority has not dissipated but is re-embedded into the network through institutional design, data regulation, and algorithmic governance, becoming a key force for maintaining overall coordination and public value. This shift indicates that public governance in the digital era is moving from centralization to distribution, and from a logic of control to a logic of coordination.

这种主体关系的出现是数字技术与治理结构之间相互重构的结果，并在实践中表现为协同化、平台化与网络化的趋势。例如，面对气候变化、平台经济监管等公共问题，单一主体难以独自应对，治理的有效性取决于不同主体之间的能力、资源互补，治理方式也由过去的部门分割走向跨界协作，而这种协同则依托平台化的技术与制度条件。在此过程中，数字平台的功能边界正在被重塑，从信息通道转变为多元主体交互的中介与操作系统，连接着政府、企业、社会、公民以及技术主体。在公共治理主体关系向多维方向演化的背景下，国家权威并未消解，而是通过制度设计、数据规制与算法治理重新嵌入网络之中，成为维护整体协调和公共价值的关键力量。这种转向表明，数字时代公共治理正由中心化走向分布式，由控制逻辑走向协同逻辑。

4. Governance Ecology: From Network Logic to Ecological Logic

4.治理生态：从网络逻辑到生态逻辑

The traditional bureaucratic system maintains order through hierarchy and rules, emphasizing advantages in resource mobilization and unified action. The subsequent governance network theory attempted to explain the logic of coordination under multi-actor interaction, but the applicability of this assumption is limited in highly dynamic and uncertain contexts. In response, academia has introduced the metaphor and explanatory framework of “ecology,” emphasizing that public governance does not operate in isolation but is embedded within a whole constituted by the interaction of multiple subjects, institutional arrangements, and the technology-society environment.

传统科层体制以层级与规则维系秩序，强调在资源动员与一致行动上的优势。其后兴起的治理网络理论则试图解释多主体互动下的协调逻辑，但这一假定在高度动态与不确定的情境中适用性受限。对此，学界引入“生态”的比喻与解释框架，强调公共治理并非孤立运行，而是嵌入由多元主体、制度安排与技术—社会环境相互作用构成的整体之中。

The digital age has further amplified the limitations of traditional governance while simultaneously giving rise to new conditions for response. The exponential growth in data scale and velocity means that governance processes can no longer rely on a single center for collection and processing; they must instead form distributed response networks. The widespread application of digital platforms and algorithms has reshaped the structures of resources and power, with government, market, and societal actors nesting and competing in new ways on platforms. Governance scenarios are constantly expanding, with virtual and physical spaces interwoven, making governance objects and boundaries more fluid. Solving complex problems requires policies that can be continuously iterated and adjusted in real time, rather than being formulated and implemented once. Compared with traditional governance logic, “ecological logic,” because it emphasizes system self-adaptation, continuous learning, and trust mechanisms, demonstrates stronger explanatory power and practical adaptability.

数字时代进一步放大了传统治理的局限，同时催生出新的应对条件。数据规模和流速呈指数级增长，使治理过程无法再依赖单一中心的收集与处理，而必须形成分布式的响应网络；数字平台和算法的广泛应用重塑了资源与权力结构，政府、市场与社会主体在平台上以新的方式嵌套与博弈；治理场景不断扩展，虚拟与物理空间交织，使治理对象和边界更加流动；复杂问题的解决要求政策能够持续迭代和实时调整，而不再是一次性的制定与执行。相较于传统治理逻辑，“生态逻辑”因其强调系统的自适应、持续学习与信任机制，而展现出更强的解释力与实践适应性。

Transforming from network logic to ecological logic does not negate hierarchy or networks; rather, it places both within a larger ecosystem for understanding: the resilience and effectiveness of governance depend more on the quality of multi-actor interaction and on whether trust and learning mechanisms can be institutionalized, contextualized, and continuously operated, thereby supporting the ongoing generation of publicness. In terms of trends, the governance ecology is gradually evolving from localized, problem-oriented collaborative practices into an institutionalized systemic logic. Future digital public governance may increasingly manifest as an open system that crosses organizations, fields, and levels, emphasizing adaptive adjustment and resilience. This means that the governance ecology is not merely a passive response to complexity but becomes an active framework for reshaping governance order.

从网络逻辑转化为生态逻辑，并非否定层级或网络，而是将二者置于更大的生态系统之中加以理解：治理的韧性与效能，更取决于多主体互动的质量，以及信任与学习机制能否被制度化、场景化并持续运作，从而支撑公共性的持续生成。从趋势上看，治理生态正逐步从局部性、问题导向的协作实践，演变为制度化的系统逻辑。未来的数字公共治理或将更加表现为跨组织、跨领域、跨层级的开放系统，强调自适应调整和韧性。这意味着治理生态不仅是对复杂性的被动回应，而更成为重塑治理秩序的主动框架。

The Limits of Digital Public Governance

数字公共治理的限度

Currently, digital technology seems to offer unprecedented possibilities for public governance, but possibility does not equal limitlessness. Digital public governance has its inherent limits. These limits are neither purely technical defects nor isolated institutional obstacles; they arise from structural boundaries revealed in the interaction between technology and society.

当前，数字技术似乎为公共治理提供了前所未有的可能性，但可能性不等于无限性。数字公共治理有其内在限度，这些限度既不是单纯的技术缺陷，也不是单独的制度障碍，而是源于技术与社会在互动中所显现的结构性边界。

1. The Limits of Technology Itself

1.技术自身的限度

Public governance concerns the public interest, and its objects are complex, changeable social systems and human beings with agency. When digital technology is introduced into the field of public governance, its potential technical defects can touch upon citizens’ fundamental rights, social equity, and even national security, and may even evolve into unbearable social risks and crises of trust.

公共治理关乎公共利益，且其对象是复杂多变的社会系统和具有能动性的人。当数字技术被引入公共治理领域，其可能存在的技术缺陷将会触及公民基本权利、社会公平乃至国家安全，甚至可能演变为不可承受的社会风险和信任危机。

First, the authenticity limit constituted by data distortion. Transforming complex social reality into processable data inevitably entails information loss and selection bias. Therefore, when any governance object is represented in data form to enter the governance process, it will unavoidably bring measurement error, selection bias, and loss of context.

其一，数据失真构成的真实性限度。将复杂社会现实转化为可处理的数据，必然伴随信息损耗和选择偏差。因此，任何治理对象在被数据化表征以进入治理流程时，都将不可避免地带来测量误差、选择偏差与语境丢失。

Second, the stability challenge brought by algorithmic bias. Social inequality factors embedded in the training data of algorithms are learned and solidified by algorithms, thereby forming technical discrimination. At the same time, digital systems operate in highly non-stationary social environments. As time, context, and institutions change, models and algorithms often experience “distribution drift,” causing their predictions and judgments to lose stability, thereby affecting the fairness and reliability of governance decisions.

其二，算法偏见带来的稳定性挑战。算法的训练数据中蕴含的社会不平等因素，会被算法学习并固化，由此形成技术性歧视。同时，数字系统运行于高度非平稳的社会环境之中，随着时间、情境与制度的变化，模型和算法常常出现“分布漂移”，导致其预测与判断失去稳定性，从而影响治理决策的公平性和可靠性。

Third, the controllability dilemma caused by the amplification of systemic risk. The scale, connectivity, and automation speed of technology mean that a single error can instantly evolve into a systemic risk. For example, if a circuit breaker mechanism in financial markets fails due to an algorithmic error, it may trigger a chain reaction, causing enormous losses.

其三，系统风险放大导致的可控性困境。技术的规模化、连通性和自动化速率，使得单个错误瞬间演变为系统性风险。例如，金融市场的熔断机制若因算法错误而失效，可能引发连锁反应，造成巨大损失。

Fourth, the blurring of responsibility attribution weakens accountability. The cross-level “cloud-edge-end” architecture, closed-source models, and transnational supply chains make the decision-making paths and responsibility attribution of digital public governance complex and difficult to trace. At the same time, the black-box nature of algorithms makes it difficult for the public to understand the decision-making process, let alone to file effective appeals and corrections.

其四，责任归属模糊削弱了可问责性。跨层级的“云—边—端”架构、闭源模型和跨国供应链，使得数字公共治理的决策路径和责任归属变得复杂且难以追溯。同时，算法黑箱特性使得公众难以理解决策过程，更难以进行有效的申诉与纠正。

2. The Limits of Instrumental Rationality

2.工具理性的限度

The dominance of instrumental rationality was a malady already present in the industrial age, and it is even more so in the digital age. When instrumental rational tendencies such as efficiency supremacy, scale priority, and optimal solution orientation are applied to public governance, they may come into serious conflict with values such as fairness, diversity, and trade-offs.

工具理性的炽盛是工业时代就已经产生的弊病，在数字时代更是如此。当效率至上、规模优先、最优解导向等工具理性倾向运用于公共治理时，可能与公平、多元和权衡等价值产生严重冲突。

First, the efficiency paradox. In algorithmic optimization and automated systems, efficiency is usually set as the primary goal. But an increase in administrative efficiency does not necessarily bring an enhancement of governance effectiveness. Overemphasizing efficiency logic erodes the publicness foundation on which governance depends. For example, algorithmic credit scoring can increase lending speed, but it may solidify discrimination against certain groups due to model bias.

一是效率悖论。在算法优化与自动化系统中，效率通常被设置为首要目标。但行政效率的提升并不必然带来治理效能的增强。过度强调效率逻辑，会侵蚀治理赖以存在的公共性基础。例如，算法信贷虽能提升放贷速度，却可能因模型偏差而固化对某些人群的歧视。

Second, scale heterogeneity. The development of digital technology relies on data and computing power, and its inherent tendency is toward standardization and scaling. But governance objects are highly heterogeneous, with enormous differences between groups and regional conditions. Standardized platforms often find it difficult to respond to this diversity, which may lead to new forms of digital exclusion. For example, a unified government service system may leave the elderly or those with insufficient digital skills at a loss.

二是规模异质。数字技术的发展依托数据和算力，其内在倾向是标准化与规模化。但治理对象高度异质，群体和地区条件差异极大，标准化平台往往难以回应这种差异性，可能会导致新的数字排斥。例如，统一的政务系统可能让老年人或数字技能不足者无所适从。

Third, the optimal solution illusion. Technical systems rely on optimization models, assuming that problems have a single optimal solution. But in the governance context, public problems are mostly “wicked problems” with no single optimal solution; public governance faces multiple value tensions that require balancing and reconciliation. Clearly, the algorithm’s “optimum” cannot replace the negotiation, compromise, and institutional balance required for governance.

三是最优解幻觉。技术系统依赖优化模型，假设问题存在唯一最优。但在治理语境中，公共问题多是“棘手问题”，不存在单一最优解；公共治理面临多重价值张力，需要进行权衡与调和。可见，算法的“最优”无法替代治理所需的协商、妥协与制度平衡。

3. The Limits of Institutional Adaptation

3.制度适应的限度

Institutions are composed of three basic elements: the regulative, the normative, and the cultural-cognitive. These elements respectively require institutions to provide predictability over time, define acceptability in terms of values, and clarify accountability in terms of cognition. But digital technology brings structural tensions, forming three limits of institutional adaptation.

制度由规制性、规范性与文化—认知性等三大基础要素构成，这些要素分别要求制度在时间上保持可预期、在价值上界定可接受、在认知上澄清可追责。但数字技术带来结构性张力，形成了制度适应的三重限度。

First, the speed mismatch in the regulative dimension. Institutions provide stable expectations for public governance over time through written rules, procedures, and processes; the characteristic of digital technology is high-frequency iteration and continuous deployment. There is an inherent mismatch between the two in terms of speed.

一是规制性维度的速率失配。制度通过成文规则、流程和程序，在时间上为公共治理提供稳定预期；而数字技术的特征是高频迭代和持续部署，二者之间天然存在快慢错配。

Second, the boundary breach in the normative dimension. If institutions set boundaries too broadly, they may suppress innovation and cause resource waste; if set too narrowly, they may leave risk vacuums. In practice, methods such as principle-based legislation and ex post facto refinement are often used for dynamic adjustment, but this means that institutional boundaries are always in a state of catching up, unable to keep pace with technological development, thereby bringing risks such as institutional value disorder and boundary confusion.

二是规范性维度的边界失守。制度若划定边界过宽，可能抑制创新并造成资源浪费；若划定过窄，则可能留下风险真空。现实中常采用原则立法、事中细化的方式来动态调试，但这导致制度边界始终处于追赶状态，无法与技术发展保持同步，从而带来制度价值失序、边界混乱等风险。

Third, the responsibility ambiguity in the cultural-cognitive dimension. Institutions should define “who decides, who executes, who is responsible” through a shared cognitive framework, but under the cross-platform, cross-departmental architecture of digital public governance, this framework is weakened. Especially in scenarios involving multiple actors, it is often difficult to clarify whether the ultimate responsibility lies with the developer, the platform operator, the user, or the regulatory authority.

三是文化—认知性维度的责任模糊。制度本应通过共享的认知框架来界定“谁决策、谁执行、谁负责”，但在数字公共治理的跨平台、跨部门架构下，这一框架被削弱。尤其在多元主体共同参与的场景中，究竟应由开发者、平台方、使用者还是监管部门承担最终责任，往往难以澄清。

4. The Limits of Governance Logic

4.治理逻辑的限度

“Governance logic” is the cognitive framework of public governance, aiming to transform complex social phenomena into actionable plans through problem definition, causal inference, and value prioritization. The widespread application of digital technology may generate complex problems that transcend traditional governance classifications and cognitive frameworks.

“治理逻辑”是公共治理的认知框架，旨在通过问题界定、因果归纳与价值排序，将复杂社会现象转化为可供行动的方案。数字技术的广泛应用可能导致超越传统治理分类和认知框架的复杂问题。

First, logic mismatch. At the international level, technology governance emphasizes coordination and sharing, while geopolitical logic prioritizes relative gains; at the domestic level, technological logic pursues universalization and scalability, while local governance must respond to regional differences and group diversity.

一是逻辑失配。在国际层面，技术治理强调协同与共享，而地缘政治逻辑则以相对收益优先；在国内层面，技术逻辑追求普遍化和可扩展性，而地方治理必须回应区域差异和群体多样性。

Second, causal chain rupture. The development of digital technology has broken through the boundaries of traditional linear causal models. For example, artificial intelligence systems have learning capabilities; their behavior is not entirely predetermined externally but evolves continuously in response to environmental feedback. Smart contracts on the blockchain, once deployed, execute automatically, leaving no room for mid-course correction. This makes it difficult for governance actors, even when they identify a problem, to trace its causes or predict its subsequent evolution.

二是因果链断裂。数字技术的发展突破了传统线性因果模型的边界。例如，人工智能系统具备学习能力，其行为并非完全由外部预设，而是随环境反馈不断演化；区块链上的智能合约一旦部署便自动执行，缺乏中途修正余地。这使得治理者即便识别出问题，也难以追踪成因、预测后续演化。

Third, difficulty in value prioritization. Digital issues often embed conflicting value objectives. For example, platform recommendation systems pursue both user stickiness and commercial revenue, while also relating to information diversity and the health of public opinion. Emphasizing the former exacerbates information cocoons; emphasizing the latter may weaken the sustainability of the business model. The widespread existence of such multiple value tensions means that traditional single-objective prioritization (such as efficiency first, stability first) is no longer effective.

三是价值排序困难。数字议题往往嵌套相互冲突的价值目标。例如，平台推荐系统既追求用户黏性与商业收益，又关系到信息多样性与舆论健康。强调前者会加剧信息茧房，强调后者则可能削弱商业模式可持续性。这种多重价值张力的普遍存在，使得传统的单一目标优先排序（如效率优先、稳定第一）已难以奏效。

The aforementioned limits of digital public governance ultimately lead to the decline of human centrality. The “public” in public governance originates from “human beings,” and its core value orientation is to respond to human needs, safeguard human dignity, and realize human development. However, from the perspective of algorithms and models, individuals are described as calculable variables. While this enhances the operability of governance, it weakens the response to human diversity and subjectivity. At the same time, excessive reliance on technical systems may lead to the degradation of critical thinking and deliberative capacity among human decision-makers and the public. The accumulation of these factors may cause the governance process to slide from “taking humans as ends” to “taking technology as an end.”

数字公共治理的上述限度，归根结底会导致人的中心性式微。公共治理之“公共”，本源在于“人”，其核心价值取向是回应人的需要、保障人的尊严与实现人的发展。但在算法与模型的视角中，个体被描述为可计算的变量，这虽提升了治理的操作性，却削弱了对人的差异性与主体性的回应。同时，过度依赖技术系统，可能导致人类决策者和民众批判性思维、协商能力的退化。种种因素叠加，使治理过程可能从“以人为目的”滑向“以技术为目的”。

Placing humans at the center of digital public governance

把人置于数字公共治理的中心

On the axis of practical development, technological evolution is inevitably unstoppable, and in many fields it is driven by market forces, making its path full of uncertainty. However, the legitimacy and direction of institutional evolution can be more clearly shaped around “human beings.” Looking ahead, digital public governance must place humans at the center in both institutional design and technological application.

在现实发展象限上，技术的演进定然不可避免，而且在许多领域由市场力量牵引，其路径充满不确定性；但制度演进的正当性与方向性却可以更为明确地围绕“人”来塑造。面向未来，数字公共治理要在制度设计与技术应用中把人置于中心位置。

1. Acknowledging human diversity

1.承认人的多样性

The objects of digital public governance are highly heterogeneous. Differences in gender, age, class, region, and cultural background give rise to a complex and diverse pattern of public needs in both form and intensity. If the logic of governance relies excessively on the majority or averages to set standards, it will inevitably create new forms of inequality. Therefore, diversity must be incorporated into institutional design, so that policies can reflect sensitivity to differences during implementation. That is, the needs of different groups should be identified and responded to, and brought into the governance process through the rational allocation of public resources, thereby establishing a more flexible and inclusive social foundation for digital public governance.

数字公共治理的对象具有高度异质性。性别、年龄、阶层、区域与文化背景的差异，使得公共需求在形式和强度上呈现出复杂多样格局。如果治理逻辑过度依赖多数人或平均值来设定标准，势必会形成新的不平等现象。因而，需要将多样性纳入制度化设计，使政策在执行过程中能够体现差异敏感性，即不同群体的需求应当被识别、回应，并通过公共资源的合理配置进入治理过程，从而为数字公共治理建立起更具弹性和包容性的社会基础。

2. Taking humans as ends

2.以人为目的

This must be translated into operable arrangements at the institutional and policy levels. First, in setting policy objectives, improving the conditions for human survival and development should be placed first, rather than taking technical feasibility or economic efficiency as the sole criterion. For example, in the digital transformation of employment services, one should not only look at the overall job matching rate but also pay attention to the accessibility and experiential differences of disadvantaged groups. Second, in institutional design, value boundaries should be established to clarify which areas of publicness cannot be replaced by instrumental logic. For example, data use must go through informed consent procedures, and social assistance must retain channels for human intervention. Third, in the evaluation system, indicators oriented toward “human outcomes” should be introduced, rather than using only efficiency and coverage rates as performance standards. For example, in evaluating the effectiveness of a public health platform, one should not only count vaccination rates or consultation rates but also examine the public’s affordability, sense of gain, and long-term health improvement. At the same time, “taking humans as ends” is not only a requirement on the governance side but should also run through the entire process of technological research and development. Humanities and social science education for digital technology designers and developers can be strengthened, integrating concepts such as fairness, responsibility, ethics, and inclusiveness into the early stages of technology research and development, achieving a combination of engineering and humanities, and avoiding a disconnect between technology and public values.

这需要在制度和政策层面转化为可操作的安排。第一，在政策目标设定中，将改善人的生存与发展条件置于首位，而非以技术可行性或经济效率为唯一标准。例如，在就业服务的数字化转型中，不能只看整体岗位匹配率，还应关注困难群体的可及性与体验差异。第二，在制度设计上，应建立价值边界，明确哪些领域的公共性不能被工具逻辑取代。例如，数据使用必须经过知情同意程序，社会救助必须保留人工介入渠道。第三，在评估体系中，应引入“人的结果”导向的指标，而不是仅以效率和覆盖率作为绩效标准。例如，公共卫生平台的成效评估，不仅要统计接种率或就诊率，更要考察民众的可负担性、获得感与长期健康改善。同时，“以人为目的”不仅是治理端的要求，也应贯穿技术研发全过程。可加强针对数字技术设计者和研发者的人文与社会科学教育，将公平、责任、伦理、包容等理念融入技术研发的早期环节，实现工程与人文结合，避免技术与公共价值脱节。

3. Cultivating the “public person”

3.培育“公共人”

The “public person” refers to an individual who, in a digital society, can understand public affairs, fulfill public responsibilities, and participate in governance as an active subject on institutional and technological platforms. First, the “public person” needs to possess a consciousness of publicness. In a digital governance environment, individuals are often shaped by algorithmic push and personalized recommendations. If they lack public consciousness, they can easily fall into information cocoons or become part of a “silent majority.” [1]1 “Information cocoons” (信息茧房) refers to the phenomenon where individuals, due to algorithmic personalization, are exposed only to information that reinforces their existing beliefs, leading to intellectual isolation. “Silent majority” (沉默的多数) here denotes a large group of people who refrain from expressing their views publicly, often due to perceived social pressure or lack of effective channels. To this end, the education system, public media, and community organizations should guide individuals to transcend the limitations of private interests and recognize the intrinsic connection between public affairs and personal well-being. Second, the “public person” needs to possess the capacity for governance participation. Digital platforms are not only service carriers but also arenas for policy communication, public deliberation, and joint decision-making. The “public person” should be able to express opinions, supervise policies, and participate in co-governance on these platforms. This requires institutions to provide citizens with accessible channels for participation, such as open data platforms, digital hearing systems, and community co-governance applications, enabling individuals to form a stable subject role in public governance. Third, the “public person” should also possess digital ethical literacy. Whether individuals can respect the rights of others and adhere to the principles of fairness and responsibility in data sharing, information dissemination, and online interaction determines whether the public sphere can maintain basic trust and order.

“公共人”是指个体能够在数字社会中理解公共事务、履行公共责任，并在制度与技术平台上作为积极的主体参与治理。其一，“公共人”需要具备公共性意识。数字化治理环境中，个体往往受算法推送和个性化推荐塑造，如果缺乏公共意识，极易陷入信息茧房或“沉默的多数”。为此，应通过教育体系、公共媒体和社区组织，引导个体超越私人利益的局限，认识到公共事务与个人福祉之间的内在联系。其二，“公共人”需要具备治理参与能力。数字平台不仅是服务载体，更是政策沟通、公共协商与共同决策的场域。“公共人”应能够在这些平台中表达意见、监督政策并参与共治。这要求制度为公民提供可及的参与渠道，如开放数据平台、数字化听证系统、社区共治应用等，使个体在公共治理中形成稳定的主体角色。其三，“公共人”还应具备数字伦理素养。个人在数据共享、信息传播、网络交往中能否尊重他人权利、遵循公平与责任原则，决定了公共空间能否维持基本的信任与秩序。

4. Enhancing digital governance capacity

4.提升数字治理能力

The effective operation of digital public governance depends not only on institutional design and technological supply but also on whether the governance subjects possess governance capacity that matches them. This capacity is different from general technical skills and is not limited to common knowledge of public affairs; it is a composite capacity that spans technology and governance. Its content includes at least three aspects: First, the capacity to understand the structure of digital governance. Digital governance systems typically present a complex network spanning departments, levels, and platforms, and linear causal logic is difficult to reveal the interactive relationships within them. This requires governance subjects to understand the coupling mechanisms between institutional rules, technological platforms, and social behavior, and to grasp the holistic nature of governance operations. Second, the capacity to apply and supervise technology. Against the backdrop of the widespread embedding of big data and artificial intelligence in governance, individuals and organizations must not only learn to use these tools but also be able to identify the risks and biases within them. For example, understanding the basic logic of algorithmic decision-making and being able to discern potential biases in information push. Third, the capacity for cross-boundary collaboration and institutional innovation. Many problems in digital governance, such as data sharing, personal privacy, and cybersecurity, transcend the boundaries of a single department or profession. This requires governance participants not only to possess the literacy for negotiation and cooperation but also to have the creativity to propose improvement plans within the institutional framework.

数字公共治理的有效运行，不仅依赖制度设计和技术供给，还取决于治理主体能否具备与之相匹配的治理能力。这种能力既不同于一般的技术技能，也不限于公共事务常识，而是一种横跨技术与治理的复合型能力。其内容至少包括三方面：其一，理解数字治理结构的能力。数字治理体系通常呈现跨部门、跨层级、跨平台的复杂网络，而线性因果逻辑难以揭示其中的互动关系。这需要治理主体能够理解制度规则、技术平台与社会行为之间的耦合机制，把握治理运作的整体性。其二，应用与监督技术的能力。在大数据、人工智能广泛嵌入治理的背景下，个体和组织不仅要学会使用这些工具，还要能够识别其中的风险与偏差。如理解算法决策的基本逻辑，能够辨别信息推送中的潜在偏见。其三，跨界协同与制度创新的能力。数字治理中的许多问题，如数据共享、个人隐私、网络安全，都超越了单一部门或专业的边界。这不仅要求治理参与者具备协商与合作的素养，也要求他们具备在制度框架内提出改进方案的创造力。

Placing humans at the center of digital public governance, preventing risks, or maintaining order is certainly important, but what is even more important is to stimulate human creativity through institutional design and technological application. This requires a dual effort: on the one hand, institutions should lower the threshold for using public data, computing power, and technological tools, enabling more social subjects to enter the innovation process on an equal footing; on the other hand, the cultural and educational systems should cultivate a spirit of exploration and a sense of collaboration, enabling individuals to respond creatively to uncertainty through digital tools. Through this process, human agency and technological potential are combined, thereby providing impetus for the generation of new public orders and institutional forms in the digital society.

把人置于数字公共治理的中心，防范风险或维持秩序固然重要，但更重要的是通过制度设计和技术应用激发人的创造性。这需要进行双重努力：一方面，制度应降低公共数据、算力和技术工具的使用门槛，使更多社会主体能够平等进入创新过程；另一方面，文化与教育体系应当培育探索精神与协作意识，使个体在面对不确定性时能够通过数字工具创造性地予以回应。通过这一过程，人的能动性与技术潜力得以结合，从而为数字社会生成新的公共秩序与制度形态提供动力。

In summary, if we define the frontier as the field that is most challenging, most urgently in need of breakthroughs, and will determine the future landscape, then there is no doubt that the frontier of contemporary public governance is shifting toward digital public governance. This process is full of possibilities but also faces numerous risks and challenges. How to reshape order between technology and value, find breakthroughs between boundaries and possibilities, and carve out paths between risk and creation will be an endless frontier.

综上所述，如果我们将前沿定义为最富挑战性、最亟待突破、将决定未来格局的领域，那么毋庸置疑，当代公共治理的前沿正在转向数字公共治理。这一过程充满可能性，也面临诸多风险挑战。如何在技术与价值之间重塑秩序、在边界与可能之间寻找突破、在风险与创造之间开辟路径，将是一个无尽的前沿。

(Author: Yu Jianxing, is the Party Secretary of Zhejiang Gongshang University and the Dean of the Institute for Social Governance of Zhejiang University)

（作者：郁建兴，系浙江工商大学党委书记、浙江大学社会治理研究院院长）

Online Editor: Huihui

网络编辑：慧慧