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

Anna Haensch (), Natasa Dragovic (), Christoph Borgers () and Bruce Boghosian ()
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Anna Haensch: http://annahaensch.com
Natasa Dragovic: https://natasadragovic.github.io/

Journal of Artificial Societies and Social Simulation, 2023, vol. 26, issue 1, 8

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.

Keywords: Bounded Confidence Model; Opinion Dynamics; Spatial Modeling; Network Dynamics (search for similar items in EconPapers)
Date: 2023-01-31
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2022-45-4

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