  Agent Based Modelling (ABM) is a computational framework for simulating the
behaviours and interactions of autonomous agents. As Agent Based Models are
usually representative of complex systems, obtaining a likelihood function of
the model parameters is nearly always intractable. There is a necessity to
conduct inference in a likelihood free context in order to understand the model
output. Approximate Bayesian Computation is a suitable approach for this
inference. It can be applied to an Agent Based Model to both validate the
simulation and infer a set of parameters to describe the model. Recent research
in ABC has yielded increasingly efficient algorithms for calculating the
approximate likelihood. These are investigated and compared using a pedestrian
model in the Hamilton CBD.
