  Agent-based modelling (ABMing) is a powerful and intuitive approach to
modelling complex systems; however, the intractability of ABMs' likelihood
functions and the non-differentiability of the mathematical operations
comprising these models present a challenge to their use in the real world.
These difficulties have in turn generated research on approximate Bayesian
inference methods for ABMs and on constructing differentiable approximations to
arbitrary ABMs, but little work has been directed towards designing approximate
Bayesian inference techniques for the specific case of differentiable ABMs. In
this work, we aim to address this gap and discuss how generalised variational
inference procedures may be employed to provide misspecification-robust
Bayesian parameter inferences for differentiable ABMs. We demonstrate with
experiments on a differentiable ABM of the COVID-19 pandemic that our approach
can result in accurate inferences, and discuss avenues for future work.
