Complex Systems Research Group, Faculty of Engineering and IT, The University of Sydney, Sydney, Australia.
Charles Perkins Centre, The University of Sydney, John Hopkins Drive, Sydney, Australia.
J Biol Dyn. 2020 Dec;14(1):57-89. doi: 10.1080/17513758.2020.1720322. Epub 2020 Jan 29.
We review research studies which use game theory to model the decision-making of individuals during an epidemic, attempting to classify the literature and identify the emerging trends in this field. The literature is classified based on (i) type of population modelling (classical or network-based), (ii) frequency of the game (non-repeated or repeated), and (iii) type of strategy adoption (self-learning or imitation). The choice of model is shown to depend on many factors such as the immunity to the disease, the strength of immunity conferred by the vaccine, the size of population and the level of mixing therein. We highlight that while early studies used classical compartmental modelling with self-learning games, in recent years, there is a substantial growth of network-based modelling with imitation games. The review indicates that game theory continues to be an effective tool to model decision-making by individuals with respect to intervention (vaccination or social distancing).
我们回顾了使用博弈论来模拟传染病期间个体决策的研究，试图对文献进行分类，并确定该领域的新兴趋势。文献的分类依据是：（i）人口建模类型（基于经典模型或网络模型），（ii）博弈频率（非重复或重复），以及（iii）策略采用类型（自我学习或模仿）。模型的选择取决于许多因素，例如对疾病的免疫力、疫苗赋予的免疫力强度、人口规模及其混合程度。我们强调，尽管早期的研究使用具有自我学习博弈的经典房室模型，但近年来，基于模仿博弈的网络模型的研究有了实质性的增长。综述表明，博弈论仍然是一种有效的工具，可以用来模拟个人对干预（接种疫苗或社交距离）的决策。
