Understanding the inner-workings of city-level agent-based models

Agent-based modelling (ABM) is rapidly becoming established as a standard tool for the social
scientist. Its ability to represent individuals, their unique characteristics and behaviour provide
a template for allowing simulations of human behaviour to be created. Typically, the
application systems that ABMs are used to replicate, for example cities, are heavily imbued
with complexity and non-linear dynamics. However, we lack the tools to understand the
interplay of these dynamics and the part they play in the final model output. This is particularly
important if we are to create robust models that can be used within the policy arena. This is
also timely; the growth in new forms of individual micro-data (big data) will only increase the
potential for ABM to give new insights into how individuals use and interact with systems.
Using the outputs for several scenarios such as health or retail related, from a city level
ABM, this PhD will evaluate and apply a range of techniques from Machine Learning (ML)
for uncovering the inner-workings of ABM. This will give insight into which processes the
model is, and is not replicating well. Specifically, the aims will be:
1 Critical evaluation of different techniques from ML - which offers the greatest
potential for understanding the interplay of ABM rules? This will include both
methods for visualisation of the system as well as statistical appraisal.
2 Adaption of these approaches for application to ABM outputs
3 Application of ML approaches to ABM outputs and critical evaluation.
The outputs of this PhD will be the application of one or more ML inspired approaches that
will shed light on the black box nature of ABM.