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

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Agent-based modelling (ABM) is rapidly becoming established as a standard tool for the socialscientist. Its ability to represent individuals, their unique characteristics and behaviour providea template for allowing simulations of human behaviour to be created. Typically, theapplication systems that ABMs are used to replicate, for example cities, are heavily imbuedwith complexity and non-linear dynamics. However, we lack the tools to understand theinterplay of these dynamics and the part they play in the final model output. This is particularlyimportant if we are to create robust models that can be used within the policy arena. This isalso timely; the growth in new forms of individual micro-data (big data) will only increase thepotential 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 levelABM, 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 themodel is, and is not replicating well. Specifically, the aims will be:1 Critical evaluation of different techniques from ML - which offers the greatestpotential for understanding the interplay of ABM rules? This will include bothmethods for visualisation of the system as well as statistical appraisal.2 Adaption of these approaches for application to ABM outputs3 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 thatwill shed light on the black box nature of ABM.