权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

了解城市级基于代理的模型的内部运作

基本信息

批准号：
2106407
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2106407
关键词：
Understanding inner workings city level

项目摘要

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.

基于主体的建模（ABM）正迅速成为社会科学家的标准工具。它能够代表个人，他们独特的特点和行为提供了一个模板，允许模拟人类行为被创建。通常，ABM用于复制的应用系统（例如城市）充满了复杂性和非线性动态。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起的作用。如果我们要创建可用于政策竞技场的稳健模型，这一点尤为重要。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，使个人如何使用和与系统交互的新见解。使用几个场景的输出，如健康或零售相关，从城市层面ABM，这个博士将评估和应用一系列机器学习（ML）技术，以揭示ABM的内部工作。这将使我们深入了解模型的哪些过程是可复制的，哪些过程是不可复制的。具体来说，目标将是：1从ML的不同技术的关键评估-这提供了最大的潜力，了解ABM规则的相互作用？这将包括系统可视化的方法以及统计评估。2适应这些方法应用于ABM输出3 ML方法应用于ABM输出和关键评估。这个博士学位的输出将是一个或多个ML启发的方法的应用，这些方法将揭示ABM的黑盒性质。