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

Autoři: Shaheen A. Abdulkareem aff001;  Ellen-Wien Augustijn aff003;  Tatiana Filatova aff001;  Katarzyna Musial aff005;  Yaseen T. Mustafa aff006
Působiště autorů: Center of Studies of Technology and Sustainability Development (CSTM), Faculty of Behavioral, Management, and Social sciences (BMS), University of Twente, Enschede, The Netherlands aff001;  Department of Computer Science, College of Science, University of Duhok (UoD), Kurdistan region, Iraq aff002;  Department of Geo-Information Processing (GIP), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands aff003;  School of Information, Systems and Modeling, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Sydney, Australia aff004;  Advanced Analytics Institute, School of Software, Faculty of Engineering and IT, University of Technology Sydney (UTS), Sydney, Australia aff005;  Faculty of Science, University of Zakho (UoZ), Kurdistan region, Iraq aff006
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226483


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.

Klíčová slova:

Agent-based modeling – Behavior – Decision making – Human learning – Cholera – Infectious disease epidemiology – Learning – Surface water


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2020 Číslo 1