Bounded risk disposition explains Turing patterns and tipping points during spatial contagions

Spatial contagions, such as pandemics, opinion polarization, infodemics and civil unrest, exhibit non-trivial spatio-temporal patterns and dynamics driven by complex human behaviours and population mobility. Here, we propose a concise generic framework to model different contagion types within a sui...

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Veröffentlicht in:Royal Society open science Jg. 11; H. 10; S. 240457 - 23
Hauptverfasser: Jamerlan, C. M., Prokopenko, M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England The Royal Society Publishing 01.10.2024
The Royal Society
Schlagworte:
Aversion
Computer Science and Artificial Intelligence
COVID-19
Epidemics
opinion polarization
Pandemics
Parameter identification
Parameter sensitivity
Pattern formation
Population studies
Real estate
Residential areas
Residential buildings
Riots
Risk
Risk aversion
social myths
socio-economic turbulence
susceptibility acquisition
socio-economic turbulence
epidemics
social myths
pattern formation
opinion polarization
susceptibility acquisition
ISSN:2054-5703, 2054-5703
Online-Zugang:Volltext
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Zusammenfassung:Spatial contagions, such as pandemics, opinion polarization, infodemics and civil unrest, exhibit non-trivial spatio-temporal patterns and dynamics driven by complex human behaviours and population mobility. Here, we propose a concise generic framework to model different contagion types within a suitably defined contagion vulnerability space. This space comprises risk disposition, considered in terms of bounded risk aversion and adaptive responsiveness and a generalized susceptibility acquisition. We show that resultant geospatial contagion configurations follow intricate Turing patterns observed in reaction–diffusion systems. Pattern formation is shown to be highly sensitive to changes in underlying vulnerability parameters. The identified critical regimes (tipping points) imply that slight changes in susceptibility acquisition and perceived local risks can significantly alter the population flow and resultant contagion patterns. We examine several case studies using Australian datasets (COVID-19 pandemic; crime incidence; conflict exposure during COVID-19 protests; real estate businesses and residential building approvals) and demonstrate that these spatial contagions generated Turing patterns in accordance with the proposed model.
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Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.7441741.
ISSN:2054-5703
2054-5703
DOI:10.1098/rsos.240457