Study
Doctoral study
The Centre for Decision Research in Leeds University Business School is keen to encourage applications from students wishing to undertake a PhD in the area of decision making, and would be interested in discussing potential projects with students who would like to study here. Proposals are welcomed in any area of decision research, but at this time the example topic areas listed for potential supervisors in CDR in the list below are of particular interest. To get a broader idea of each supervisors' areas of interest look at our publications page or their profile page (links in the list below).
Details of research degree study at Leeds University Business School can be found here: https://business.leeds.ac.uk/research-degrees. This includes links to details on how to apply, and fees and scholarships (in the PhD tab of "In this Section").
- Effects of Computer Aided Decision Support
- Use of archival sports data to study choice behaviour
- Investigations of (the linkages between) impulsivity, self-control and time preferences
- Predicting decisions using natural language
- Ecological approaches to decision-making
- Mitigating the hypothetical bias in stated preference surveys
- New revealed preference data sources for non-market valuation
- Development and evaluation of new decision support systems for real industrial problems
- Development and utilisation of new analytical techniques to support industry decision-making
- Risk perception and communication in applied contexts (e.g. medicine, natural hazards, food safety, product safety)
- Understanding and communication of weather and climate information
- Effects of source reliability and confidence on judgement and decision-making
- Food-related decision making, sustainability-related decision making
- Developing risk (benefit) communications, and other interventions in relation to food and sustainability
- The impact of risk and uncertainty in environment, health and behaviour using experimental economics methods (primarily lab-in-the-field and field experiments)
- Non-monetary valuation of environmental health hazards (including risk-risk trade-off methods)
- Investigating machine learning and deep learning techniques for predictive modelling in financial and economic systems, using various modalities such as time series, images, and relational data
- Investigating how affective computing can reveal the links between affect and downstream financial behaviour, with applications in financial decision-making, consumer behaviour, and risk perception
- Investigating generative approaches for synthetic data creation to enhance predictive modelling and enable what-if scenario analysis in financial and economic systems
- Modelling time use: applying and developing new quantitative techniques to understand and predict the way people use their time
- Integration of Machine learning and econometrics in business analytics
- Influence of heuristics, mental accounting, and bounded rationality on financial decision-making, and their implications for consumer welfare.
- Consumer behaviour, status-seeking and herd behaviour, income inequality, and their relationship to subjective well-being.
- Topics within the behavioural household finance literature (saving, borrowing and investment decisions)
- Conducting data analysis across different sectors
- Employing machine learning and optimisation techniques (e.g., shallow/deep learning, computer vision, small-sample learning, and generalisable reinforcement learning) to support decision-making and operations management
- Eliciting preferences from decision makers using machine learning and multi-criteria decision analysis
- Mining association rules from large data sets using dominance-based rough analysis
- Investigating ways of structuring decision-making problems
- Influencer marketing and consumer attitudes
- Online information and cryptocurrencies
- Individual decision making, particularly in the areas of finance and health, considering cognitive and/ or emotional perspectives
- The effect of time pressure and other stressors on judgment and decision making
- Risk perception and communication, with particular interest in environmental risks (e.g. weather, climate, geohazards)
- Weather and climate information services for decision support
- The role of emotion in judgement and decision making
- Unstructured multimodal data mining: texts, images...
- Understanding and identifying human traits : faces, expressions, personalities, intelligence, confidence...
- Employing those computational techniques to improve business and management practice
- Individual differences in personality, identity status, decision-making, & risk-taking
- Lifespan perspective on decision-making, including identity status, longitudinal assessment of decision-making skills
- Risk perceptions, with a focus on health behaviors and financial behaviors (e.g., food safety, behavioural addictions such as gambling, problematic gaming)
Find out more about postgraduate research at Leeds University Business School.
MSc Business Analytics and Decision Sciences
The MSc Business Analytics and Decision Sciences programme delivers the knowledge and understanding of how business analytics can provide evidence to support management decision-making.
Full details of the programme and how to apply can be found here.
The MSc Business Analytics and Decision Sciences was ranked in the 2025 QS Master in Business Analytics rankings:
- 7th in the UK
- 21st in Europe
- 51-60 range in the world (up from 61-70)
Welcome to our 2019/20 Cohort for MSc Business Analytics & Decision Sciences (BADS)
We are happy to welcome more students to the programme this year (184) giving them the chance to develop skills valued by employers. This year students have the chance to take our new module on Machine Learning in Practice, giving them experience with this technology to take to the workplace.
Highlights from the 2014/15 cohort
The Premier Farnell Leadership Challenge
Students on CDR's MSc Business Analytics & Decision Sciences took part in a “Leadership Challenge” with Premier Farnell organised by Jo Lumb, Engagement Manager in the Business School’s Management Division.
Saptarshi Ray of the MSc explained “We are taking part in the leaders' challenge with one of the finest e-commerce companies in the world; Premier Farnell. We have got the challenge of demonstrating how big data analytics can help the company to improve their revenue and organisational efficiencies. We will be working as consultants for one month and will provide our suggestions to them.
“With the guidance of our Programme Director, Professor Alan Pearman, we undertook research on the company background and their business policy before going to the company and meeting their top personnel. Premier Farnell gave us a short presentation about their company and also briefed us on their current challenges and what they expect from us. We were given the opportunity to ask key questions to help with the challenge.
“It is a great experience for us to work as real life analytics consultants, which most of the students on our course aspire to be in their future career. We are grateful to Premier Farnell and the University of Leeds for providing us with this opportunity.”
The student group's analysis of the company's position was very well received and they subsequently were invited to make a full presentation to senior UK and US managers.
Presenting at EURO2015
Euro2015 is a major academic conference in the Operations Research area. Two MSc students, Saptarshi Ray and Ryzky Yudha presented papers:
Saptarshi Ray presented a talk titled “Association rules mining and cross correlation using Apriori algorithm to the financial market: The case with respect to UK Stock market and Global cues” and Ryzky Yudha presented a talk titled “Matching Critical Success Factors of Employee Performance to Help the Recruitment Process in Analysing Candidate Profiles by Incorporating Social Media Analytics”
Staff from the Centre for Decision Research also presented papers:
- Simon McNair, Alan Pearman, Ken-Ichi Shimomura and Barbara Summers, An experimental exploration of behaviour patterns in a mixed strategy two-person game
- Michele Lundy, Sajid Siraj and Salvatore Greco, Addressing weaknesses in pairwise comparison based prioritization methods – can the spanning tree approach help?
- Richard Hodgett, ChemDecide – a MCDA software for the chemical-using industries