  Calibrating agent-based models (ABMs) to data is among the most fundamental
requirements to ensure the model fulfils its desired purpose. In recent years,
simulation-based inference methods have emerged as powerful tools for
performing this task when the model likelihood function is intractable, as is
often the case for ABMs. In some real-world use cases of ABMs, both the
observed data and the ABM output consist of the agents' states and their
interactions over time. In such cases, there is a tension between the desire to
make full use of the rich information content of such granular data on the one
hand, and the need to reduce the dimensionality of the data to prevent
difficulties associated with high-dimensional learning tasks on the other. A
possible resolution is to construct lower-dimensional time-series through the
use of summary statistics describing the macrostate of the system at each time
point. However, a poor choice of summary statistics can result in an
unacceptable loss of information from the original dataset, dramatically
reducing the quality of the resulting calibration. In this work, we instead
propose to learn parameter posteriors associated with granular microdata
directly using temporal graph neural networks. We will demonstrate that such an
approach offers highly compelling inductive biases for Bayesian inference using
the raw ABM microstates as output.
