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arXiv · 2210.08012

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

Anna Haensch·Natasa Dragovic·Christoph Börgers·Bruce Boghosian
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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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Anna Haensch, Natasa Dragovic, Christoph Börgers, Bruce Boghosian. 2022-10-14. A geospatial bounded confidence model including mega-influencers with an application to Covid-19 vaccine hesitancy. https://arxiv.org/abs/2210.08012

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