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

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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. 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 Borgers & Bruce Boghosian, 2023. "A Geospatial Bounded Confidence Model Including Mega-Influencers with an Application to Covid-19 Vaccine Hesitancy," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(1), pages 1-8.
    • Handle: RePEc:jas:jasssj:2022-45-4
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    1. Jean-Denis Mathias & Sylvie Huet & Guillaume Deffuant, 2016. "Bounded Confidence Model with Fixed Uncertainties and Extremists: The Opinions Can Keep Fluctuating Indefinitely," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-6.
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