  How much more will we learn about single-field inflationary models in the
future? We address this question in the context of Bayesian design and
information theory. We develop a novel method to compute the expected utility
of deciding between models and apply it to a set of futuristic measurements.
This necessarily requires one to evaluate the Bayesian evidence many thousands
of times over, which is numerically challenging. We show how this can be done
using a number of simplifying assumptions and discuss their validity. We also
modify the form of the expected utility, as previously introduced in the
literature in different contexts, in order to partition each possible future
into either the rejection of models at the level of the maximum likelihood or
the decision between models using Bayesian model comparison. We then quantify
the ability of future experiments to constrain the reheating temperature and
the scalar running. Our approach allows us to discuss possible strategies for
maximising information from future cosmological surveys. In particular, our
conclusions suggest that, in the context of inflationary model selection, a
decrease in the measurement uncertainty of the scalar spectral index would be
more decisive than a decrease in the uncertainty in the tensor-to-scalar ratio.
We have incorporated our approach into a publicly available python class, foxi
(https://sites.google.com/view/foxicode), that can be readily applied to any
survey optimisation problem.
