Abstract: The rapid development of information technology and intelligent systems has led to the emergence of smart communities as a new direction for modernizing grassroots governance in China. These communities provide intelligent public services to residents, primarily in the areas of public safety, security, and administrative affairs, by collecting and utilizing residents’ personal information data. Currently, smart communities are in the initial stages of development, primarily encountering challenges related to intergovernmental collaboration and residents’ privacy concerns. Throughout the construction process, local governments and community residents will continually adjust their strategies by learning and imitating, given the risks associated with interdepartmental collaboration and information privacy. When the public security department leads the construction, the smart community will focus on providing law and order services to residents. On the other hand, when the civil affairs department leads the construction, the smart community will focus on handling public affairs for residents. Residents can choose whether or not to share personal information with the government in exchange for smart community services. Alternatively, they can refuse to protect their personal information. The aim of this paper is to summarise the decision-making laws of the government and residents during the construction of smart communities. Theconclusions of this paper provide optimisation suggestions for policy design and guide practice with theory.
The article presents a Stackelberg model to illustrate the level-of-effort decisions made by the dominant and auxiliary sectors at a specific point in time. Additionally, an evolutionary game model is used to depict the strategy evolution of local government and community residents over time. By solving and analyzing the game model, evolutionary stable strategies (ESS) can be obtained under four scenarios. The theoretical results indicate that the level of collaboration between government departments and the level of information privacy concerns among residents are key factors influencing the evolutionary stabilisation strategies.After a comprehensive analysis of the conditions corresponding to the evolutionary stabilization strategy, the following conclusions can be drawn: Firstly, the completion of a smart community is contingent on the level of residents’ perception of public service and the department’s ability to collaborate, and the level of residents’ information privacy concern must be below a specific threshold. Secondly, the government tends to select departments with high synergistic abilities rather than those with high working levels to lead the development of smart communities in the long term. This is because the advantages of efficient and cost-effective auxiliary departments can be integrated and amplified by the synergistic abilities of dominant departments. Thirdly, government departments with higher coefficients of return, that is, those with higher administrative benefits or lower administrative costs will exert greater effort. This, in turn, will provide greater utility to local governments and community residents.
To verify the theoretical results, the article combines numerical experiments to analyse two types of typical practice cases. Case I considers the different average levels of information privacy concerns among residents in communities where different occupational groups are the main residents. The experiments demonstrate the results and speed changes of evolutionary convergence under different levels of information privacy concerns. Case II focuses on residents’ perception of services before and after the implementation of a smart community. The study aims to determine the impact of different perception levels on the experiment’s evolutionary speed. The numerical results indicate that higher levels of information privacy concerns result in slower convergence speeds of residents’ information sharing, and may even lead to the adoption of a “no-sharing” strategy. When residents’ perception of public services improves, the speed of convergence of their shared information will increase. This means that a smart community can collect residents’ information in a shorter period of time.
The conclusions of this paper are not perfect and can be improved in the future. Two possible research directions are: including the central government or third-party enterprises as participants in the discussion, and considering changes in construction costs.

Key words: smart community, government departments’ collaboration, information privacy concern, evolutionary game

摘要： 智慧社区在建设的过程中存在跨部门协同和居民信息隐私顾虑的问题。为研究建设的推进路径,本文分别构建地方政府部门间主从博弈模型和地方政府-社区居民间演化博弈模型,给出不同协同模式下政府部门的最优努力水平以及建设的演化规律。研究表明:居民信息隐私关注水平低于特定阈值是建成智慧社区的必要条件,该阈值与居民对公共服务、部门协同的感知有关;协同能力强弱是影响政府模式选择的关键因素;高回报系数不但促使政府部门付出更多努力,还有利于提高政府和居民效用。最后,结合计算机仿真开展案例分析,验证了居民信息隐私关注水平和公共服务感知水平对演化收敛的影响。论文的研究结论对优化智慧社区建设机制有一定启示。

关键词: 智慧社区, 政府部门协同, 信息隐私关注, 演化博弈