Biological systems are often modelled at different levels of abstraction
depending on the particular aims/resources of a study. Such different models
often provide qualitatively concordant predictions over specific
parametrisations, but it is generally unclear whether model predictions are
quantitatively in agreement, and whether such agreement holds for different
parametrisations. Here we present a generally applicable statistical machine
learning methodology to automatically reconcile the predictions of different
models across abstraction levels. Our approach is based on defining a
correction map, a random function which modifies the output of a model in order
to match the statistics of the output of a different model of the same system.
We use two biological examples to give a proof-of-principle demonstration of
the methodology, and discuss its advantages and potential further applications.