The Choice Modelling Centre and the Institute for Transport Studies are looking for a motivated PhD student to work on a timely and ambitious project aimed at advancing our understanding and prediction of how people make choices jointly in different settings, such as within families or work settings. The project supervisors will be Dr Chiara Calastri and Prof. Stephane Hess, in close collaboration with Dr Richard Mann from the School of Mathematics.
Only UK nationals or EU nationals who have been in the UK for the past 3 years can apply. The deadline for application is 31st January 2020.
For further information on the project and enquiries please see: https://phd.leeds.ac.uk/project/480-which-one-should-we-choose-developing-statistical-models-to-explain-and-forecast-joint-decision-making.
Decisions made jointly by multiple agents represent an exciting and challenging behavioural process that has received limited attention in spite of recent rapid developments in various disciplines using mathematical models to interpret and forecast human behaviour. Joint decisions as well as social influences are relevant to most aspects of life, from household management to therapy choices in the case of illness; and while advanced theoretical models have been developed in the field of Game Theory, applicability to real datasets lags behind. In recent years, Machine Learning has emerged as a key analytical tool for representing decision making, and has arguably made greater strides than traditional statistical approaches when it comes to capturing interactions between agents and joint decision making. However, unlike Choice Modelling or Game Theory, Machine Learning lacks an econometric and psychological foundation – the outputs cannot be used for welfare analysis and little is learned about the behavioural processes beyond being able to predict outcomes. The present PhD project aims to address some important research gaps in this area. First of all, the candidate will conduct a review of the studies of mathematical models of joint and collective decision making, a much needed contribution in a sparse field both in terms of methods and applications. The core element of the project will revolve around the development of a new statistical framework which can accommodate joint decision making while ensuring behavioural interpretability. The areas of applications of this framework can range from health to transport decisions, and will depend on the aspirations and background of the candidate and data availability. The forecasting ability and welfare implications of the developed methods will then be compared to other techniques such as machine learning and AI, producing a piece of work that does not only assess strengths and weaknesses of the different techniques but also reflects on how they can interact to analyse the complex process of joint decision making.