Modelling opinion dynamics during crises as complex contagion with feedback
Abstract
Crises and population responses can form coupled dynamical systems, with crisis conditions shaping protective behaviours and collective responses altering the crisis trajectory. Existing models rarely capture both evolving crisis conditions and the reinforcement-dependent spread of competing behaviours. We propose a complex contagion with feedback (CCF) model that couples competing complex contagion with crisis dynamics through bidirectional feedback. Agents stochastically switch between competing states according to social reinforcement and adoption complexity, which is influenced by crisis conditions and information campaigns, while population-level behavioural change feeds back into the crisis. We formulate and analyse the model for a well-mixed population, characterising its equilibria and associated stability conditions. As a case study for model validation, we integrate CCF model with a census-calibrated agent-based model of COVID-19 transmission in Australia to represent social distancing adoption and discontinuation. We compare the CCF model with an existing opinion dynamics model and find that, despite using fewer parameters, it achieves comparable performance in reproducing recurrent infection waves. The resulting framework is parsimonious, analytically tractable, and can be adapted to different types of crises.
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BibTeX
@misc{huang2026modelling,
title = {{Modelling opinion dynamics during crises as complex contagion with feedback}},
author = {Junxiang Huang and Mikhail Prokopenko},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.18684},
eprint = {2609.18684},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.18684},
}
RIS
TY - UNPB
TI - Modelling opinion dynamics during crises as complex contagion with feedback
AU - Junxiang Huang
AU - Mikhail Prokopenko
PY - 2026
DA - 2026/09/16
PB - arXiv
DO - 10.48550/arxiv.2609.18684
UR - https://arxiv.org/abs/2609.18684
AB - Crises and population responses can form coupled dynamical systems, with crisis conditions shaping protective behaviours and collective responses altering the crisis trajectory. Existing models rarely capture both evolving crisis conditions and the reinforcement-dependent spread of competing behaviours. We propose a complex contagion with feedback (CCF) model that couples competing complex contagion with crisis dynamics through bidirectional feedback. Agents stochastically switch between competing states according to social reinforcement and adoption complexity, which is influenced by crisis conditions and information campaigns, while population-level behavioural change feeds back into the crisis. We formulate and analyse the model for a well-mixed population, characterising its equilibria and associated stability conditions. As a case study for model validation, we integrate CCF model with a census-calibrated agent-based model of COVID-19 transmission in Australia to represent social distancing adoption and discontinuation. We compare the CCF model with an existing opinion dynamics model and find that, despite using fewer parameters, it achieves comparable performance in reproducing recurrent infection waves. The resulting framework is parsimonious, analytically tractable, and can be adapted to different types of crises.
ER -