Probabilistic Agent-Based Modelling for Predicting School Attendance
- Primary Supervisor
- Nick Malleson <N.S.Malleson@leeds.ac.uk>
- University of Leeds
- Academic Supervisors
- Dan Birks <D.Birks@leeds.ac.uk> (University of Leeds), Mark Mon-Williams <M.Mon-Williams@leeds.ac.uk> (University of Leeds)
- Research Themes
- Agent Based Modelling, Education, Social Inequality
- Project Partners
- Bradford Institute of Health Research
Project reference: LE74
Please note the deadline for this opportunity has now passed and we will not be accepting further applications.
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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.