arXiv Open Access 2022

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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Abstrak

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.

Topik & Kata Kunci

cs.SI math.DS physics.soc-ph

Penulis (4)

Anna Haensch

Natasa Dragovic

Christoph Börgers

Bruce Boghosian

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APA MLA BibTeX

Haensch, A., Dragovic, N., Börgers, C., Boghosian, B. (2022). 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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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓