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网络治理的机器学习和游戏理论:增强公众论和区域可持续发展
Podong Song1,2, Wiseong Jin3,4, Shaowei Chen5
1Kunming University, Kunming City, China.
PloS one
|December 5, 2024
概括
本研究引入了一个使用机器学习和游戏理论来理解和管理互联网公众论的新框架,以促进可持续发展. 它提供了对网络空间治理和有效影响在线行为的见解.
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
- 信息科学 信息科学 信息科学
背景情况:
- 网络空间是一个影响人类可持续发展的关键环境.
- 互联网公众论对于网络空间治理和表达人民意志至关重要.
- 由于其复杂性,感知公众论是具有挑战性的.
研究的目的:
- 提出一种新的框架来感知大众意愿,管理论和影响行为.
- 为有效的网络空间治理利用机器学习和游戏理论.
- 为公众论分析提供一个具有成本效益和适用的解决方案.
主要方法:
- 利用深度学习进行论分析.
- 采用积极学习方法来降低运营成本.
- 应用游戏理论,以获得最佳的管理决策.
- 用来自中国Y和G省的经验数据验证了框架.
主要成果:
- 证明了框架在分析和管理论方面的有效性.
- 为公众论和行为之间的相互关系提供理论支持.
- 建立了网络空间治理研究的成本效益方法.
结论:
- 拟议的框架为网络空间治理和论管理提供了有价值的见解.
- 该方法可以适应在不同的区域环境中应用.
- 对互联网公众论的有效管理对于人类可持续发展至关重要.
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