A Geospatial Bounded Confidence Model Including Mega-Influencers with an Application to Covid-19 Vaccine Hesitancy

Abstract

We introduce a geospatial bounded confidence model with mega-influencers, inspired by Hegselmann and Krause (2002). 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. Megainfluencers and stochasticity amplify this effect, and soften local consensus. As an application, we consider views on Covid-19 vaccines in the United States. For a certain set of parameters, our model yields results comparable to real survey results on vaccine hesitancy from late 2020.