Hutzler Florian, Richlan Fabio, Leitner Michael Christian, Schuster Sarah, Braun Mario, Hawelka Stefan
Department of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria.
R Soc Open Sci. 2021 Apr 28;8(4):201574. doi: 10.1098/rsos.201574.
Humans grossly underestimate exponential growth, but are at the same time overconfident in their (poor) judgement. The so-called 'exponential growth bias' is of new relevance in the context of COVID-19, because it explains why humans have fundamental difficulties to grasp the magnitude of a spreading epidemic. Here, we addressed the question, whether logarithmic scaling and contextual framing of epidemiological data affect the anticipation of exponential growth. Our findings show that underestimations were most pronounced when growth curves were linearly scaled framed in the context of a more advanced epidemic progression. For logarithmic scaling, estimates were much more accurate, on target for growth rates around 31%, and not affected by contextual framing. We conclude that the logarithmic depiction is conducive for detecting exponential growth during an early phase as well as resurgences of exponential growth.
人类严重低估指数增长，但同时又对自己（糟糕的）判断力过于自信。所谓的“指数增长偏差”在新冠疫情背景下具有新的相关性，因为它解释了为什么人类在理解传播性流行病的规模时存在根本困难。在此，我们探讨了流行病学数据的对数缩放和背景框架是否会影响对指数增长的预期这一问题。我们的研究结果表明，当增长曲线按线性缩放并置于更高级别的疫情进展背景下时，低估最为明显。对于对数缩放，估计要准确得多，对于约31%的增长率目标而言是准确的，且不受背景框架的影响。我们得出结论，对数描述有助于在早期阶段检测指数增长以及指数增长的复苏情况。
