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Governance Studies ›› 2023, Vol. 39 ›› Issue (6): 84-99.

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Evolutionary Game Analysis of Multi agent Behavior Strategies in the Digital Transformation of Government Services

Xia Zhiqiang, Zhao Yuxin   

  • Received:2023-10-03 Online:2023-11-15 Published:2023-12-12

Abstract:

The digital transformation of government services is an important method for local governments to promote the modernization of governance. When promoting the digital transformation of government affairs, there are different goals, positions, and interests between the central government, local governments, digital technology enterprises, and the public which result in ineffective cooperation. The article focuses on the multi-agent collaboration mechanism in the digital transformation process of government services and constructs a game model between the central government, local governments, digital technology enterprises, and the public. By conducting simulation analysis on the model, the article reveals the evolutionary game process, ideal equilibrium state, and influencing factors of strategy selection of the four participants and explores the best collaborative strategies for multiple entities. Research has found that the central government attaches great importance to reputation and smooth governance. Local governments and digital enterprises are more concerned about additional costs. And the quality of government service supply affects the trust of the public and forms a positive cycle with public participation and supervision. Therefore, the development focus should be on the digital government responsibility of local governments, while also reducing their burden to supply services. The central government should strengthen the supervision of digital government services, safeguard the public interest, and enhance government credibility. Digital technology enterprises need to enhance their sense of social responsibility and help the public integrate into digital life.

Key words: government services, digital transformation, digital government, evolutionary game

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