Negative learning
Negative learning
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DOI：
10.1007/s10584-008-9405-1
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发表时间：
2008-07-01
期刊：
影响因子：
4.8
通讯作者：
Webster, Mort
中科院分区：
文献类型：
--
作者：
Oppenheimer, Michael;O'Neill, Brian C.;Webster, Mort
新的技术信息可能会导致科学信念随着时间的推移而偏离后验正确答案。我们把这种在全球变化竞技场中特别成问题的现象称为消极学习。在一些重要情况下，消极的学习可能会影响政策，包括平流层臭氧消耗、南极西部冰盖的动态以及人口和能源预测。我们模拟的背景下，气候变化的负面学习的正式模型，嵌入贝叶斯框架内的概念，说明它可能会导致错误的决定和社会福利的巨大损失。基于这些案例，我们提出了科学评估和决策的方法，可以减轻问题。将科学史工具应用于全球变化中的学习研究，包括对评估过程进行批判性检查，以了解如何做出判断，可以为如何改善决策者的信息流动提供重要的见解。
New technical information may lead to scientific beliefs that diverge over time from the a posteriori right answer. We call this phenomenon, which is particularly problematic in the global change arena, negative learning. Negative learning may have affected policy in important cases, including stratospheric ozone depletion, dynamics of the West Antarctic ice sheet, and population and energy projections. We simulate negative learning in the context of climate change with a formal model that embeds the concept within the Bayesian framework, illustrating that it may lead to errant decisions and large welfare losses to society. Based on these cases, we suggest approaches to scientific assessment and decision making that could mitigate the problem. Application of the tools of science history to the study of learning in global change, including critical examination of the assessment process to understand how judgments are made, could provide important insights on how to improve the flow of information to policy makers.