模型不确定性,COVID-19大流行,以及科学与政策的界面
Henrik Thorén1, Philip Gerlee2
1Department of Philosophy, Lund University, Lund 22100, Sweden.
Royal Society open science
|February 15, 2024
概括
用于政策决策的模型,如COVID-19的模型,简化了复杂的不确定性. 这种简化,或者说.
科学领域:
- 流行病学 流行病学
- 科学和政策研究 研究科学和政策研究
- 数学建模的数学建模
背景情况:
- 随着COVID-19大流行,科学发现转化为政策的难度突出显现.
- 在流行病学模型中管理和传达不确定性给决策者带来了重大挑战.
- 流行病学模型不仅代表,而且积极塑造它们旨在传达的不确定性.
研究的目的:
- 在流行病学模型中探索"不确定性化"的概念.
- 分析模型如何简化或结构化政策应用的不确定性.
- 评估基于模型的不确定性化的科学对政策的影响.
主要方法:
- 分析涉及COVID-19流行病学模型的三个案例研究.
- 对这些模型中不确定性如何表示和管理的定性检查.
- 探索模型结构与政策需求之间的关系.
主要成果:
- 模型"化"不确定性,使它们更易于管理,但可能不太代表真正的复杂性.
- 不确定性化的过程可以导致模型输出与政策制定者的实际要求之间的不匹配.
- 来自COVID-19建模的具体例子说明了这种化如何掩盖或过度简化关键的不确定性.
结论:
- 需要更多地关注流行病学模型如何将不确定性化为政策支持.
- 用于简化模型中不确定性的方法可能与政策和规划的细微要求不一致.
- 了解不确定性化的机制对于有效的基于科学的政策制定至关重要.
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