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首页> 外文期刊>Climatic Change >Negative learning. (Special Issue: Learning and climate change.)
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Negative learning. (Special Issue: Learning and climate change.)

机译：负面学习。 （特刊：学习与气候变化。）

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摘要

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.
机译：新的技术信息可能会导致科学信念与时间的后验正确答案背道而驰。我们称这种现象为负面学习，这种现象在全球变化领域特别有问题。在重要情况下，消极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。我们使用在贝叶斯框架内嵌入该概念的正式模型来模拟气候变化背景下的消极学习，这说明这可能导致错误的决策并给社会带来巨大的福利损失。根据这些案例，我们建议可以减轻问题的科学评估和决策方法。将科学史的工具应用于全球变化的学习研究，包括对评估过程进行批判性检查以了解如何做出判断，可以为如何改善信息流向决策者提供重要的见解。

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