Inflation as an Information Bottleneck - A strategy for identifying
  universality classes and making robust predictions
  In this work we propose a statistical approach to handling sources of
theoretical uncertainty in string theory models of inflation. By viewing a
model of inflation as a probabilistic graph, we show that there is an
inevitable information bottleneck between the ultraviolet input of the theory
and observables, as a simple consequence of the data processing theorem. This
information bottleneck can result in strong hierarchies in the sensitivity of
observables to the parameters of the underlying model and hence universal
predictions with respect to at least some microphysical considerations. We also
find other intriguing behaviour, such as sharp transitions in the predictions
when certain hyperparameters cross a critical value. We develop a robust
numerical approach to studying these behaviours by adapting methods often seen
in the context of machine learning. We first test our approach by applying it
to well known examples of universality, sharp transitions, and concentration
phenomena in random matrix theory. We then apply the method to inflation with
axion monodromy. We find universality with respect to a number of model
parameters and that consistency with observational constraints implies that
with very high probability certain perturbative corrections are non-negligible.
