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

TL;DR

This paper presents a geospatial bounded confidence model incorporating mega-influencers to simulate the emergence of spatial clusters of vaccine hesitancy, aligning with real survey data from late 2020.

Contribution

It introduces a novel geospatial bounded confidence model with mega-influencers, capturing large-scale spatial patterns in opinion dynamics, specifically applied to Covid-19 vaccine hesitancy.

Findings

Spatial clusters of opinions emerge regardless of initial conditions.

Mega-influencers and randomness influence local consensus.

Model results align with real survey data from late 2020.

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.