Ensembles vs. information theory: supporting science under uncertainty

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dc.contributor.authorNearing, Grey S.
dc.contributor.authorGupta, Hoshin V.
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherUniversity of Arizona
dc.date.accessioned2018-10-11T20:00:37Z
dc.date.available2018-10-11T20:00:37Z
dc.date.issued2018
dc.description.abstractMulti-model ensembles are one of the most common ways to deal with epistemic uncertainty in hydrology. This is a problem because there is no known way to sample models such that the resulting ensemble admits a measure that has any systematic (i.e., asymptotic, bounded, or consistent) relationship with uncertainty. Multi-model ensembles are effectively sensitivity analyses and cannot - even partially - quantify uncertainty. One consequence of this is that multi-model approaches cannot support a consistent scientific method - in particular, multi-model approaches yield unbounded errors in inference. In contrast, information theory supports a coherent hypothesis test that is robust to (i.e., bounded under) arbitrary epistemic uncertainty. This paper may be understood as advocating a procedure for hypothesis testing that does not require quantifying uncertainty, but is coherent and reliable (i.e., bounded) in the presence of arbitrary (unknown and unknowable) uncertainty. We conclude by offering some suggestions about how this proposed philosophy of science suggests new ways to conceptualize and construct simulation models of complex, dynamical systems.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.citationNearing, G., Gupta, H. (2018): Ensembles vs. information theory: supporting science under uncertainty. Frontier Earth Science. DOI: 10.1007/s11707-018-0709-9
dc.identifier.doi10.1007/s11707-018-0709-9
dc.identifier.orcidhttps://orcid.org/0000-0001-9855-2839
dc.identifier.orcidhttps://orcid.org/0000-0001-9855-2839
dc.identifier.urihttp://ir.ua.edu/handle/123456789/3999
dc.languageEnglish
dc.language.isoen_US
dc.publisherSpringer
dc.subjectinformation theory
dc.subjectmulti-model ensembles
dc.subjectBayesian methods
dc.subjectuncertainty quantification
dc.subjecthypothesis testing
dc.subjectGeosciences, Multidisciplinary
dc.subjectGeology
dc.titleEnsembles vs. information theory: supporting science under uncertaintyen_US
dc.typetext
dc.typeReview
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