Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning
Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning
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DOI：
10.1371/journal.pone.0226483
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发表时间：
2020-01-06
期刊：
影响因子：
3.7
通讯作者：
Mustafa, Yaseen T.
中科院分区：
文献类型：
--
作者：
Abdulkareem, Shaheen A.;Augustijn, Ellen-Wien;Mustafa, Yaseen T.
现代社会面临着从疾病到自然灾害和技术中断等各种风险。探索风险意识如何传播以及如何引发应对策略的扩散在各个领域的研究议程中占据突出地位。它需要深入了解个人如何看待风险，如何就保护措施的有效性进行沟通，强调学习和社会互动是推动这一进程的核心机制。从纯粹基于物理的扩散模型到数据驱动的环境方法的方法论方法依赖于基于代理的建模，以适应扩散过程中的上下文相关学习和社会互动。将基于代理的建模与数据驱动的机器学习相结合已经变得越来越流行。然而，很少有人注意到智能学习在风险评估和保护性决策中的作用，无论是在个人还是集体过程中使用。集体学习和个人学习之间的差异还没有得到充分的探讨，在一般的扩散模型，特别是在基于代理的模型的社会环境系统。为了解决这一研究空白，我们使用机器学习增强的基于代理的模型，探索了智能学习对从个人到集体学习的梯度的影响。我们的模拟实验表明，个人的智能判断的风险和选择的应对策略的群体多数票优于领导者为基础的群体，甚至个人单独决定。社会互动对个人学习和团体学习都是必不可少的。如何在基于代理的模型中表示社会学习的选择可以由模型化社会中流行的现有文化和社会规范驱动。
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.