Systematically false positives in early warning signal analysis.

Many systems in various scientific fields like medicine, ecology, economics or climate science exhibit so-called critical transitions, through which a system abruptly changes from one state to a different state. Typical examples are epileptic seizures, changes in the climate system or catastrophic s...

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Main Authors: Georg Jäger, Manfred Füllsack
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0211072
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author Georg Jäger
Manfred Füllsack
author_facet Georg Jäger
Manfred Füllsack
author_sort Georg Jäger
collection DOAJ
description Many systems in various scientific fields like medicine, ecology, economics or climate science exhibit so-called critical transitions, through which a system abruptly changes from one state to a different state. Typical examples are epileptic seizures, changes in the climate system or catastrophic shifts in ecosystems. In order to predict imminent critical transitions, a mathematical apparatus called early warning signals has been developed and this method is used successfully in many scientific areas. However, not all critical transitions can be detected by this approach (false negative) and the appearance of early warning signals does not necessarily proof that a critical transition is imminent (false positive). Furthermore, there are whole classes of systems that always show early warning signals, even though they do not feature critical transitions. In this study we identify such classes in order to provide a safeguard against a misinterpretation of the results of an early warning signal analysis of such systems. Furthermore, we discuss strategies to avoid such systematic false positives and test our theoretical insights by applying them to real world data.
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spelling doaj.art-4c4e41072b344b47954e3ef5607dfc202022-12-21T18:24:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01142e021107210.1371/journal.pone.0211072Systematically false positives in early warning signal analysis.Georg JägerManfred FüllsackMany systems in various scientific fields like medicine, ecology, economics or climate science exhibit so-called critical transitions, through which a system abruptly changes from one state to a different state. Typical examples are epileptic seizures, changes in the climate system or catastrophic shifts in ecosystems. In order to predict imminent critical transitions, a mathematical apparatus called early warning signals has been developed and this method is used successfully in many scientific areas. However, not all critical transitions can be detected by this approach (false negative) and the appearance of early warning signals does not necessarily proof that a critical transition is imminent (false positive). Furthermore, there are whole classes of systems that always show early warning signals, even though they do not feature critical transitions. In this study we identify such classes in order to provide a safeguard against a misinterpretation of the results of an early warning signal analysis of such systems. Furthermore, we discuss strategies to avoid such systematic false positives and test our theoretical insights by applying them to real world data.https://doi.org/10.1371/journal.pone.0211072
spellingShingle Georg Jäger
Manfred Füllsack
Systematically false positives in early warning signal analysis.
PLoS ONE
title Systematically false positives in early warning signal analysis.
title_full Systematically false positives in early warning signal analysis.
title_fullStr Systematically false positives in early warning signal analysis.
title_full_unstemmed Systematically false positives in early warning signal analysis.
title_short Systematically false positives in early warning signal analysis.
title_sort systematically false positives in early warning signal analysis
url https://doi.org/10.1371/journal.pone.0211072
work_keys_str_mv AT georgjager systematicallyfalsepositivesinearlywarningsignalanalysis
AT manfredfullsack systematicallyfalsepositivesinearlywarningsignalanalysis