论文标题
大流行控制，游戏理论和机器学习
Pandemic Control, Game Theory and Machine Learning
论文作者
论文摘要
游戏理论一直是控制疾病传播以及提出个人和地区层面最佳政策的有效工具。在此AMS通知文章中，我们关注Covid-19的干预的决策发展，旨在提供数学模型和有效的机器学习方法，以及对过去实施的相关政策的理由，并解释当局的决策如何影响其邻近的游戏理论观点。
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this AMS Notices article, we focus on the decision-making development for the intervention of COVID-19, aiming to provide mathematical models and efficient machine learning methods, and justifications for related policies that have been implemented in the past and explain how the authorities' decisions affect their neighboring regions from a game theory viewpoint.
