Ensembles vs. information theory: supporting science under uncertainty

Grey S. Nearing, Hoshin V. Gupta

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations


Multi-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.

Original languageEnglish (US)
Pages (from-to)653-660
Number of pages8
JournalFrontiers of Earth Science
Issue number4
StatePublished - Dec 1 2018


  • Bayesian methods
  • hypothesis testing
  • information theory
  • multi-model ensembles
  • uncertainty quantification

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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