Developing statistical models to explain and forecast joint decision making

Lead Research Organisation: University of Leeds
Department Name: Institute for Transport Studies


Project Summary (brief overview - one paragraph):

While the past two decades have seen rapid development in mathematical models to interpret and forecast human behaviour in many disciplines, one of the processes that has received the least attention is that of decisions made jointly by multiple agents. 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.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513258/1 30/09/2018 29/09/2023
2441958 Studentship EP/R513258/1 30/09/2020 29/09/2023 Shuwei Lin