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社交平台推荐内容治理的多方演化博弈与仿真研究
Multi-Party Evolutionary Game and Simulation Research on Social Platform Recommendation Content Governance

DOI: 10.12677/ORF.2024.141098, PP. 1057-1070

Keywords: 社交平台,推荐内容,多方共治,演化博弈,仿真分析
Social Platforms
, Recommended Content, Multi-Party Governance, Evolutionary Games, Simulation Analysis

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Abstract:

[目的/意义]:社交平台推荐内容生态治理关系着用户的算法权力与体验、内容消费经济的发展以及社会稳定,运用演化博弈理论探索多主体参与社交平台推荐内容共治的互动机制有助于为此提出针对性建议。[方法/过程]:基于用户参与反馈治理建立“社交平台企业–政府监管部门–第三方评估机构”三方博弈模型,通过模型推导分析了博弈三方稳定策略的均衡条件及最优均衡点,并采用数值模拟仿真了博弈系统的演化过程以及各个参数变化对博弈主体行为策略的影响。[结果/结论]:鼓励用户参与共治有助于弥补由于第三方评估机构责任缺失带来的损失,从而维持内容推荐生态体系的平衡;扩大对于提供高质量推荐服务与内容的社交平台的补贴扶持力度相较于惩罚措施对于提升推荐内容生态的作用更有效;帮助第三方机构实施分类监管有助于降低第三方机构的监管成本从而促进社交平台提供高质量的推荐服务与内容。最后,提出了搭建一体化共治信息平台的理论思路和相关建议。
[Purpose/Significance]: The governance of the content ecology of social platform recommenda-tion is related to the algorithmic power and experience of users, the development of content consumption economy and social stability, and the use of evolutionary game theory to explore the interactive mechanism of multi-subject participation in the co-governance of social platform recommended content is helpful to put forward targeted suggestions for this purpose. [Method/Process]: Based on user participation feedback governance, a three-way game model of “social platform enterprise-government regulatory department-third-party evaluation agency” was established, and the equilibrium conditions and optimal equilibrium points of the three-way stability strategy of the game were deduced and analyzed through the model, and the evolution process of the game system and the impact of various parameter changes on the behavior strategy of the game subject were simulated by numerical simulation. [Results/Conclusion]: Encour-aging users to participate in co-governance can help compensate for the loss caused by the lack of responsibility of third-party evaluation agencies, thereby maintaining the balance of the content recommendation ecosystem, expanding subsidies and support for social platforms that provide high-quality recommendation services and content is more effective than punitive measures in improving the recommended content ecology, helping third-party institutions im-plement categorical supervision helps reduce the regulatory costs of third-party institutions, thereby promoting social platforms to provide high-quality recommendation services and content. Finally, the theoretical ideas and suggestions for building an integrated co-governance information platform are put forward.

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