A geospatial bounded confidence model including mega-influencers with an application to Covid-19 vaccine hesitancy

2022/10/14 by Haensch, Anna, Dragovic, Natasa, Börgers, Christoph +1 · 2 citations
#60-08 #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #G.3 #I.6.3 #J.4 #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)

paper · doi:10.48550/arxiv.2210.08012

Abstract

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.

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