Abstract:
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, multimodel
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.
