研究智能算法推对塑造大学生价值观和协作治理道路的双边效应
1School of Control and Computer Engineering, North China Electric Power University, Beijing, China.
PloS one
|December 17, 2025
概括
这项研究引入了一种新的AI模型,用于打击有害的在线内容,并在大学生中促进积极的价值观. 强化学习框架增强了价值相似性,并有效地减少了对负面材料的接触.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
- 人工智能的人工智能
背景情况:
- 算法推系统创建信息,影响大学生价值观.
- 现有的内容调节方法,如关键字过,对于复杂的在线影响是不够的.
- 对有害内容的高暴露率,如历史虚无主义,需要先进的解决方案.
研究的目的:
- 开发一种用于价值感知的随机游戏模型,使用多代理强化学习.
- 解决推系统对学生价值观的双重影响.
- 为在线内容监管创建一个动态的治理范式.
主要方法:
- 构建了一个128维的状态空间,包括用户人格,政策强度和价值相似性.
- 定义了一个受约束的行动空间来调节推权重.
- 实施了一种双路径进化机制,包括社会实践传播和制度威.
主要成果:
- 在0.4.4的威因子下,实现了与值相似性≥0.8的纳什平衡.
- 经验验证显示,价值相似性增加了107%,有害内容暴露减少了91%.
- 减少了 3.3 倍的共识收时间.
结论:
- 拟议的模型对于动态的在线内容治理是有效的和可通用的.
- 该方法与算法监管的国家战略保持一致.
- 为促进数字时代的积极价值观提供了一个强大的框架.
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