PhD Scholarship: “Quantitative Models of Realistic Human Decision-Makers for Data Analytics and Optimisation”

The University of Manchester

Alliance Manchester Business School is committed to recruiting the highest calibre Ph.D. students from across the globe. With an excellent international reputation, we strive to produce graduates and researchers of distinction who attain the highest standards of academic excellence, contribute to their disciplines and typify Original Thinking Applied.

This PhD scholarship offers three years’ funding, including tuition fees and annual stipend of approximately £15,000 for candidates commencing their studies in September 2018. The successful candidate will receive a generous research support and conference allowance, and have access to a robust doctoral research training programme, dedicated research resources, training in transferable skills, visiting speaker seminar programme, and associate with existing research centres and groups. Students are encouraged to undertake training and development in teaching and deliver teaching/research assistantship duties on a paid basis to further enhance their experience in preparation for their future careers.

The Project

In many strategic problems in logistics, managements, planning, and manufacturing, a Decision Maker (DM) must find solutions that optimise multiple, conflicting criteria and decide among them, which nowadays often involves the DM interacting with some automated process implemented as a Multi-criteria Decision-Making and Optimisation (MCDMO) algorithm. In reality, decision-making is influenced by human factors (cognitive biases, fatigue, mistakes) that have been thoroughly studied in behavioural economics and psychology. The design of algorithms able to cope with these human factors remains an open challenge.

This project aims to devise realistic, general “simulations” of DMs (machine DMs) that explicitly model these human factors as configurable parameters, independent of specific preferences. Machine DMs will enable the empirical analysis of algorithms with respect to particular human factors. Parameters of machine DMs may be explicitly set to mimic human behaviours (e.g. risk-averse vs. risk-seeking). The ultimate goal is the development of the next generation of data analytics and decision support methods that adapt to the human factors prominent on particular problem scenarios, thus helping humans to make better decisions.

Entry Requirements

Applications are sought from exceptional UK, EU and international students with an outstanding academic background, ideally in Computer Science, Mathematics, Operations Research, Data Science, Business Analytics, Industrial Engineering, Economics, or other discipline within business and operations management. The successful candidate must have a strong programming background (C/C++/Java/R/Python) and good analytical and communication skills. An understanding of multi-criteria decision-making and/or mathematical and heuristic multi-objective optimization techniques is highly desirable.

Applicants must have a First or Upper Second Class Honours degree (or equivalent) and hold or expect to obtain a Masters qualification with Distinction. English Language requirements (where required) are IELTS 7.0, TOEFL 623 (100 ibt), PTE 66.

How to apply

Candidates should submit a PhD application for the PhD Business & Management and indicate that they wish to be considered for this project.

Your application must contain a letter of interest outlining your background, interest and research skills related to the topic, understanding of the project, and how you satisfy the above requirements.

Candidates are strongly advised to submit their full application as early as possible. Candidates who do not apply by the deadline will not be considered.

Enquiries

For further details about the project, please contact Dr. Manuel Lopez-Ibanez at manuel.lopez-ibanez@manchester.ac.uk.

For questions related to your application, contact Lynne Barlow-Cheetham: mbs-pgresearch@manchester.ac.uk

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Type / Role:

PhD

Location(s):

Northern England