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

基于主体的建模（ABM）正在迅速成为社会科学家的标准工具。它能够代表个体、其独特的特征和行为，为创建人类行为的模拟提供了一个模板。通常，ABM 用于复制的应用系统（例如城市）非常复杂且非线性动态。然而，我们缺乏工具来理解这些动态的相互作用以及它们在最终模型输出中所扮演的角色。如果我们要创建可在政策领域使用的稳健模型，这一点尤其重要。这也是及时的；新形式的个人微观数据（大数据）的增长只会增加 ABM 的潜力，为个人如何使用系统和与系统交互提供新的见解。使用来自城市级别 ABM 的健康或零售相关等多种场景的输出，该博士将评估和应用机器学习 (ML) 的一系列技术，以揭示 ABM 的内部运作。这将让我们深入了解模型正在哪些过程以及复制得不好。具体来说，目标是：1 对 ML 的不同技术进行批判性评估 - 哪种技术为理解 ABM 规则的相互作用提供了最大的潜力？这将包括系统可视化和统计评估的两种方法。2 将这些方法应用于 ABM 输出3 将 ML 方法应用于 ABM 输出和关键评估。本博士的输出将是一种或多种 ML 启发方法的应用，这些方法将揭示 ABM 的黑匣子本质。

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.