Global problems, such as pandemics and climate change, require rapid international coordination and diffusion of policy. These phenomena are rare however, with one notable example being the international policy response to the COVID-19 pandemic in early 2020. Here we build an agent-based model of this rapid policy diffusion, where countries constitute the agents and with the principal mechanism for diffusion being peer mimicry. Since it is challenging to predict accurately the policy diffusion curve, we utilize data assimilation, that is an ``on-line'' feed of data to constrain the model against observations. The specific data assimilation algorithm we apply is a particle filter because of its convenient implementation, its ability to handle categorical variables and because the model is not overly computationally expensive, hence a more efficient algorithm is not required. We find that the model alone is able to predict the policy diffusion relatively well with an ensemble of at least 100 simulation runs. The particle filter however improves the fit to the data, reliably so from 500 runs upwards, and increasing filtering frequency results in improved prediction.

翻译：全球性问题，如大流行病和气候变化，需要快速国际协调和政策扩散。然而，此类现象较为罕见，2020年初国际社会对新冠疫情的应对政策是其中一个显著例子。本文构建了这一快速政策扩散的智能体模型，其中各国构成智能体，扩散的主要机制是同行模仿。由于准确预测政策扩散曲线具有挑战性，我们采用数据同化方法，即通过“在线”数据流约束模型使其与观测结果一致。我们应用的具体数据同化算法是粒子滤波，原因在于其实现便捷、能处理分类变量，且模型计算成本不过高，因此无需更高效的算法。研究发现，仅凭模型本身，在至少运行100次模拟集成的情况下即可较好地预测政策扩散。而粒子滤波进一步提升了与数据的拟合度，在模拟运行次数达到500次以上时效果可靠，且增加滤波频率可改善预测结果。