论文标题
一个地理空间有限的置信模型，包括大型影响者，并应用于Covid-19疫苗犹豫不决
A geospatial bounded confidence model including mega-influencers with an application to Covid-19 vaccine hesitancy
论文作者
论文摘要
我们在Hegselmann和Krause的启发下，向大型影响者介绍了一个地理空间有限的置信度模型。地理的包含产生了大规模的地理空间模式，这些模式从随机的初始数据中演变出来。也就是说，无论初始化如何，都会出现志趣相投的代理的空间簇。大型影响者和随机性扩大了这种效果，并软化了当地共识。作为应用程序，我们考虑了关于COVID-19-19疫苗的国家观点。对于某些参数，我们的模型产生的结果与2020年底以来疫苗犹豫的实际调查结果相当。
We introduce a geospatial bounded confidence model with mega-influencers, inspired by Hegselmann and Krause. The inclusion of geography gives rise to large-scale geospatial patterns evolving out of random initial data; that is, spatial clusters of like-minded agents emerge regardless of initialization. Mega-influencers and stochasticity amplify this effect, and soften local consensus. As an application, we consider national views on Covid-19 vaccines. For a certain set of parameters, our model yields results comparable to real survey results on vaccine hesitancy from late 2020.
