Research on Collaborative Governance Strategies of AIGC False Information Based on a Tripartite Evolutionary Game

Qiu Junping; Zhang Tingyong; Xu Zhongyang

doi:10.13266/j.issn.0252-3116.2026.02.006

Library and Information Service >

2026 , Vol. 70 >Issue 2: 71 - 85

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.02.006

RESEARCH PAPERS

Research on Collaborative Governance Strategies of AIGC False Information Based on a Tripartite Evolutionary Game

Qiu Junping ,
Zhang Tingyong ,
Xu Zhongyang

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1 School of Management, Hangzhou Dianzi University, Hangzhou 310018;
2 Chinese Academy of Science and Education Evaluation, Hangzhou Dianzi University, Hangzhou 310018;
3 Academy of Data Science and Informetrics, Hangzhou Dianzi University, Hangzhou 310018;
4 School of Information Management, Nanjing University, Nanjing 210023;
5 Laboratory of Data Intelligence and Interdisciplinary Innovation, Nanjing University, Nanjing 210023

Qiu Junping, Dean, Professor, Doctoral Supervisor;Zhang Tingyong, Doctoral Candidate;Xu Zhongyang, PhD, Postdoctoral Researcher, Corresponding Author, E-mail: hzhduxzy@126.com.

Received date: 2024-12-27

Revised date: 2025-02-19

Online published: 2026-01-21

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Abstract

[Purpose/Significance] It is of great significance to explore the factors influencing the collaborative governance of artificial intelligence-generated content (AIGC) false information and its evolution law, in order to promote the sustainable and healthy development of false information governance system. [Method/Process] This article constructed an evolutionary game model of “user-social media-government”, solved the evolutionarily stable strategies using the Jacobi matrix and Lyapunov’s first method, and finally conducted numerical simulation and sensitivity analysis. [Result/Conclusion] The study shows that: (1) The strategies of users, social media, and government are influenced by their initial willingness. (2) The control willingness, the punishment, and the control effectiveness of social media and the government all affect the user’s strategies. (3) The government’s willingness to regulate, regulatory efficacy, and punishment, the governance costs and benefits of social media, and the incremental governance success rate brought about by users’ participation in collaborative governance all affect the social media’s governance strategies. (4) The government’s regulatory costs, additional credibility gains, and the incremental success rate of governance brought about by user participation in collaborative governance all affect the government’s regulatory strategies. (5) The phenomenon of “free-riding” exists between the government and social media. (6) Users are more sensitive to the punishment of social media than the government and more sensitive to the control effectiveness of the government than social media. Based on this, the research proposes optimization strategies to improve the collaborative governance system of AIGC false information.

Key words： artificial intelligence generated content; false information; collaborative governance; evolutionary game model; evolutionary stable strategies

Cite this article

Qiu Junping , Zhang Tingyong , Xu Zhongyang . Research on Collaborative Governance Strategies of AIGC False Information Based on a Tripartite Evolutionary Game[J]. Library and Information Service, 2026 , 70(2) : 71 -85 . DOI: 10.13266/j.issn.0252-3116.2026.02.006

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