Candidates are required to have:
- An excellent undergraduate degree with Honours in a relevant business, scientific/technological or social science subject
- A Masters degree (or equivalent) will be strongly preferred
- Students may also have other relevant experience or skills which are relevant to this project
- Candidates who are not native English speakers will be required to provide evidence for their English skills (such as by IELTS or similar tests that are approved by UKVI, or a degree completed in an English speaking country).
Candidates should be available to commence studies on 1st October 2019
Data Science can provide significant insights from data. However, much of the research in this field focusses on empirically identifying relationships between attributes within the data. A shortcoming of this empirical model building is that variables are selected on statistical principles to optimise correlation rather than on causality that aligns with decision making.
System dynamics (SD) is a simulation method that seeks to explain dynamic behaviour of an adaptive system through its structure by adopting a high-level system perspective, but has been noted as being theory-rich but data-poor.
The use of data science methods to inform SD modelling provides a number of opportunities. Firstly, rich data can be used to provide rigorous evidence for input parameters to the system level model. Secondly, the data can be used to support insight to plausible theories or key system structures. This project will provide an important opportunity to impact strategic decision-making, enabling directed enquiry of data.
The aim of this project will be to design and evaluate a framework for coherently combining decision science methods with SD modelling. This will provide an understanding of the system level structure that explains dynamic behaviour through empirical findings identified within rich data.
This project will evaluate the most effective way in which data science methods and SD modelling can be combined to support strategic decision-making. There are a number of possible ways in which this may be achieved. For example, a bottom-up approach could be taken by allowing structure for the SD model to emerge from the data, or a top-down approach could be taken whereby the structure of the SD model is used to direct examination of the data. However, a more favourable approach is an iterative one, where expert judgement is used to structure the issue of focus to explore the data and development of the SD model. The decision-makers and experts will be involved throughout each stage of the process to ensure ownership of the model and its outcomes.
In conjunction with the Northern Ireland Department of Health, home care workforce planning has been identified as an area which could benefit from this work.
Demand for adult social care is rising across the UK. Although increased home care workers are needed to meet this demand, continued reduction in public spending is impacting the future of home care providers and leading to calls for the need for fundamental reforms. Policy level models are required to consider the impact of such reforms and SD modelling has proven impact in a social care and healthcare environment.
The successful student will have access to experts in the Department of Health in Northern Ireland and will have the opportunity to test their framework in supporting strategic decision-making in the area of home care workforce planning.
Fee waiver at Home/EU rate and annual stipend of approx. £14,777*
*Whilst open to International candidates, please note that this scholarship covers Home/EU/RUK Fee rate only
Professor Susan Howick and Professor John Quigley, Dept of Management Science
If you have any questions about this project, or would like to discuss it informally, please contact the lead supervisor Professor Susan Howick at firstname.lastname@example.org
Department PGR Administrator: Elaine Monteith (email@example.com)
How to apply
All applications should include:
- a cover letter indicating the candidate's relevant skills/experience and how they can contribute to this research
- a CV and relevant qualification transcripts
- two references (please refer to guidance on references)
When sending the above documents please use the following file-naming convention: fullname_typeofdocument
Apply now by uploading your documents.