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Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

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  • Shaheen A Abdulkareem
  • Ellen-Wien Augustijn
  • Tatiana Filatova
  • Katarzyna Musial
  • Yaseen T Mustafa

Abstract

Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.

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  • Shaheen A Abdulkareem & Ellen-Wien Augustijn & Tatiana Filatova & Katarzyna Musial & Yaseen T Mustafa, 2020. "Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-22, January.
  • Handle: RePEc:plo:pone00:0226483
    DOI: 10.1371/journal.pone.0226483
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    References listed on IDEAS

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    2. Elizabeth Hunter & Brian Mac Namee & John D. Kelleher, 2017. "A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-2.
    3. Songnian Zhao & John Wu & David Ben-Arieh, 2015. "Modeling infection spread and behavioral change using spatial games," Health Systems, Taylor & Francis Journals, vol. 4(1), pages 41-53, March.
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    1. Gizell Green & Riki Tesler, 2023. "Trust in Health Care Providers, Anxiety, Knowledge, Adherence to Guidelines, and Mental Healthcare Needs Regarding the COVID-19 Pandemic," SAGE Open, , vol. 13(2), pages 21582440231, June.
    2. Sun, Xiaodan & Dong, Shichao & Dai, Chenyang & Wang, Aili, 2024. "A model for coupling the epidemic transmission dynamics and vaccination behavioral dynamics and its application to seasonal influenza," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).

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