Anticipating trajectories of exponential growth
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 spr...
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doaj-1e30847dbbd24b2d87d301a08e3230e62021-06-10T08:57:26ZengThe Royal SocietyRoyal Society Open Science2054-57032021-04-018410.1098/rsos.201574Anticipating trajectories of exponential growthFlorian Hutzler0Fabio Richlan1Michael Christian Leitner2Sarah Schuster3Mario Braun4Stefan Hawelka5Department of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, AustriaDepartment of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, AustriaDepartment of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, AustriaDepartment of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, AustriaDepartment of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, AustriaDepartment of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, AustriaHumans 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 and 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.https://royalsocietypublishing.org/doi/10.1098/rsos.201574pandemicCOVID-19exponential growthlinear scalinglogarithmic scalingcontextual framing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Florian Hutzler Fabio Richlan Michael Christian Leitner Sarah Schuster Mario Braun Stefan Hawelka |
spellingShingle |
Florian Hutzler Fabio Richlan Michael Christian Leitner Sarah Schuster Mario Braun Stefan Hawelka Anticipating trajectories of exponential growth Royal Society Open Science pandemic COVID-19 exponential growth linear scaling logarithmic scaling contextual framing |
author_facet |
Florian Hutzler Fabio Richlan Michael Christian Leitner Sarah Schuster Mario Braun Stefan Hawelka |
author_sort |
Florian Hutzler |
title |
Anticipating trajectories of exponential growth |
title_short |
Anticipating trajectories of exponential growth |
title_full |
Anticipating trajectories of exponential growth |
title_fullStr |
Anticipating trajectories of exponential growth |
title_full_unstemmed |
Anticipating trajectories of exponential growth |
title_sort |
anticipating trajectories of exponential growth |
publisher |
The Royal Society |
series |
Royal Society Open Science |
issn |
2054-5703 |
publishDate |
2021-04-01 |
description |
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 and 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. |
topic |
pandemic COVID-19 exponential growth linear scaling logarithmic scaling contextual framing |
url |
https://royalsocietypublishing.org/doi/10.1098/rsos.201574 |
work_keys_str_mv |
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