Probabilistic Agent-Based Modelling for Predicting School Attendance

用于预测入学率的基于概率代理的建模

• 批准号: 2887257
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887257
• 关键词: Probabilistic; Agent; Based; Modelling; Predicting

In the UK, after finishing their exams at age 16 children must undertake a further two years of education, employment and training. However, large numbers of children do not participate in this final stage in their compulsory education, becoming 'NEET' (not in education, employment, or training). There are a wide range of events that may predispose someone to becoming NEET at 16, and these can often occur at a very young age, so identifying the most important causes is extremely complicated. As such, designing policies that are aimed at identifying and, ideally, preventing these negative influences can be extremely challenging.A method that might hold promise in modelling the individuals and systems that cause NEET outcomes is that of Agent-Based Modelling (ABM). ABM is characterised by models that represent individuals directly, simulating their behaviour and actions over time. Hence such a method might be ideal for exploring the events, people, and systems that all influence a young person over a long time period (from birth to age 16) with the aim of identifying pivotal events or behaviours that may correlate with becoming NEET in later life. Such predictions are inherently extremely uncertain, so this project will also develop, for the first time, a probabilistic agent-based model that represents model elements (variables, functions, outputs, etc) in a probabilistic way that captures the uncertainty in the system directly.The project will be based at the University of Leeds and affiliated with the Wolfson Centre for Applied Health Research. As part of the Centre the student will have access to a world-leading, pseudo-anonymised, secure dataset that contains records for all school children in the region with rich information that will be essential for building and validating the agent-based model. In addition, the project will collaborate with experts in local government to ensure that the project design, implementation and results can be of direct use for developing policies that will have the potential to improve the lives of many young people. Importantly, the project will explore the factors that lead to children becoming NEET in general; no real children will be identified through the research.

在英国，在16岁时完成考试后，必须再接受两年的教育，就业和培训。但是，大量儿童没有参加他们的强制性教育的最后阶段，成为“ NEET”（不是在教育，就业或培训中）。各种各样的事件可能会使某人在16岁时成为NEET，而且这些事件通常可以在很小的时候就发生，因此确定最重要的原因非常复杂。因此，设计旨在识别旨在识别这些负面影响的政策可能是极具挑战性的。 ABM的特征是直接代表个人的模型，随着时间的流逝模拟了他们的行为和行为。因此，这种方法可能非常适合探索长期影响年轻人（从出生到16岁）的事件，人和系统的理想选择，目的是确定可能与成为NEET相关的关键事件或行为以后的生活。此类预测本质上是极其不确定的，因此该项目还将首次开发基于概率代理的模型，该模型代表模型元素（变量，函数，输出等），以概率的方式直接捕获系统中的不确定性该项目将位于利兹大学，并隶属于沃尔夫森应用健康研究中心。作为中心的一部分，学生将可以访问世界领先的伪匿名，安全的数据集，其中包含该地区所有儿童的记录，并提供丰富的信息，这对于构建和验证基于代理的模型至关重要。此外，该项目将与地方政府的专家合作，以确保项目设计，实施和结果可以直接用于制定有可能改善许多年轻人生活的政策。重要的是，该项目将探索导致儿童成为一般情况的因素。通过研究将无法确定真正的孩子。