PhD research

PhD research with a choice modelling context is carried out across different schools at the University of Leeds. For initial enquiries, relating to both topics and funding opportunities, please send an e-mail to General Enquiries from where your query will be directed to potential supervisors.

Specific funding opportunities are announced on this site as and when they arise.

Current potential topics include, arranged by school of lead supervisor

Institute for Transport Studies

Which one should we choose? Developing statistical models to explain and forecast joint decision making

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.

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.

More info:

Demand for electric and hybrid cars - modeling vehicle replacement and type choice

Supervisor: Prof. Gerard de Jong

Electric and hybrid cars could contribute substantially to the required reduction in emissions and dependence on fossil fuels. The technology to do this is there. Crucial issues for the market penetration of this new technology are:

  • How fast will consumers replace their current cars?
  • How many of the replacement cars will be electric and hybrid cars?

Behavioural data on these important issues are largely missing. This project will develop a combined revealed/stated preference household survey which includes:

  • Questions on actual attributes of he households, its persons and its cars;
  • Retrospective questions on the car ownership history of the household;
  • Stated choice experiments on car type choice, including attributes that are specially relevant for electric and hybrid cars, such as fuelling range, top speed and luggage space.

As part of this project, the questionnaire will be used to interview several hundreds of UK households. The resulting data will then be used to estimate models for:

  • The timing of vehicle replacement (hazard-based duration models or Markov models) and other changes in the household car ownership status (e.g. moving to more cars or fewer cars);
  • Vehicle type choice (discrete choice models, including mixed logit), focussing on electric and hybrid cars;
  • Vehicle use.

Finally, the estimated models will be used to carry out policy simulations, such as on the effect of measures to accelerate replacement (e.g. scrappage schemes), subsidies on the purchase of electric and hybrid cars, and emission taxes.

Suggested reading:

Jong, G.C. de (1996); A disaggregate model system of vehicle holding duration, type choice and use; Transportation Research B, 30-4, pp 263-276.

Jong. G.C. de, J. Fox, A.J. Daly, M. Pieters and R. Smit (2004); Comparison of car ownership models, Transport Reviews, 24-4, pp 379-408, 2004.

Rashidi, T.H., K. Mohammadian and F. Koppelman (2009); A dynamic hazard-based structural equations model of vehicle ownership with endogenous residential and job location changes incorporating group decision making; Paper presented at the International Choice Modelling Conference 2009, Harrogate.

Are we modelling the wrong thing: differences between the psychologists' and the modellers' view of behaviour

Supervisors: Dr Richard BatleyProfessor Stephane Hess

Mathematical models representing human behaviour are used extensively in the field of transport and beyond. These models are used to analyse existing choices and forecast likely behaviour in a changing environment, e.g. the provision of new transport facilities, the introduction of new electricity pricing structures or the building of a new hospital.

To a large extent, these models are based on a compensatory approach, in which a person is assumed to make choices by trading off different attributes against one another. As an example, one mode of travel may be faster, but an alternative mode is cheaper; one train will get us to work on time, but the slightly later train is considerably less congested. The values of the different attributes of an alternative all affect that alternative’s probability of being chosen, where the negative effect of one attribute may be cancelled out by the more positive effect of another attribute.

A different view of behaviour however exists in various strands of the mathematical psychology literature. Here, evidence suggests that at some people do not in fact engage in compensatory evaluation of alternatives, but make use of various alternative heuristics to arrive at their choices. This could for example involve lexicographic behaviour, the existence of reference points or the presence of thresholds in sensitivities or tolerances.

The aim of this PhD project is to first revisit the limited amount of existing work contrasting and combining the often disparate methodologies from the fields of economics and mathematical psychology. In-depth studies will then be conducted to investigate under which circumstances the assumptions made in traditional approaches may not be justified. Ultimately, the aim is to expand the existing methodological framework to be able to adequately represent decision making processes that are well established in the mathematical psychology literature, but which are largely ignored in the modelling field. By better understanding and representing the underlying behavioural structures, the project will seek to enhance the predictive power of models used to plan the provision and usage of scarce services and resources (such as healthcare, energy and transportation).

While the topic is concerned with the interface between psychology, economics and mathematics, the proposed research will be highly methodological in its nature, and a strong quantitative background will be expected from the student. Some programming skills will be also be desirable.

References – suggested reading

Batley, R. and Daly, A. (2006) On the equivalence between elimination-by-aspects and generalised extreme value models of choice behaviour. Journal of Mathematical Psychology, 50 (5), pp456-467.

Batley, R. and Toner, J. (2003) Elimination-by-aspects and advanced logit models of stated preferences for alternative-fuel vehicles. Proceedings of the European Transport Conference, Strasbourg, October 2003.

Hess, S., Rose, J.M. and Polak, J.W. (2009), Non-trading, lexicographic and inconsistent behaviour in stated preference data, Transportation Research Part B, forthcoming.

Simon, H.A. (1959) Theories of decision-making in economics and behavioral science. American Economic Review, 49 (1), pp253-283.

Train, K.E. (2003) Discrete choice methods with Simulation, Cambridge University Press, Cambridge, MA.

Tversky, A. (1972) Elimination by aspects: a theory of choice. Psychological Review, 79 (4), pp281-299.

Developments in experimental design for stated choice surveys and alternative preference elicitation procedures

SupervisorsProfessor Stephane HessDr Jeremy Toner

The analysis of travel behaviour requires as its main input data on travel decisions (choices) made by individual respondents. However, in many situations, data on real world choices is either not available or is not suitable for the purposes of the proposed analysis. As a result, an increasing number of studies rely on data collected through surveys which present respondents with hypothetical choice scenarios. Data from such stated preference (SP) surveys are used not only in academic work but also form the backbone of many studies advising policy makers in scenarios as wide ranging as the building of new roads, the introduction of road pricing or the investment in new rolling stock.

The majority of work using SP methods now makes use of stated choice (SC) surveys, in which respondents are asked to choose their most preferred option amongst a set of mutually exclusive alternatives. Approaches such as ranking or rating exercises have been largely discredited in a transport context, as have transfer price methods, which aim to directly obtain the willingness by respondents to pay for developments or improvements. However, outside a transport environment, these methods are experiencing a renaissance, and new developments, such as best-worst, a halfway measure between choice and full ranking, are gaining in popularity. At the same time, in transport and elsewhere, researchers are constantly devising new methods to improve the efficiency of the various available survey techniques. The net outcome is that there is substantial confusion at the user end, with practitioners often unsure which approach would be most applicable in their given context.

The aim of this PhD project would be to conduct an in depth comparison of the different available methods, highlighting which approaches are most adequate in what context. Additionally, the work would look at the potential for combining various existing methods. Finally, where appropriate, further methodological developments would be made.

Recommended reading:

Louviere, J.J., Hensher, D.A. and Swait, J.D. (2000) Stated Choice Methods: Analysis and Application. Cambridge University Press.

PTRC (2000) Stated Preference Modelling Techniques. A compilation of major papers from PTRC’s meeting and conference material. Edited by J de D Ortuzar. PTRC, London.

Mode and shipment size choice models on data for individual shipments

Supervisor: Prof. Gerard de Jong

Mode choice in freight transport is usually studied in isolation. However, mode and shipment size are closely linked decisions. Large shipment sizes usually coincide with higher market shares for non-road transport, whereas there is a high correlation between road transport and small shipment sizes. Decisions on shipment size (or delivery frequency) need to be studied taking a logistics approach (e.g. reducing inventories by more frequent, just-in-time deliveries) that encompasses the more limited transport costs approach.

The Swedish 2004-2005 Commodity Flow Survey (CFS) is a unique data source in Europe. It details about more than 2.5 million individual shipments to or from a company in Sweden, with information on origin, destination, modes used, weight and value of the shipment, sector of the sending firm, commodity type, access to rail tracks and quays, etc.. Whilst the US Commodity Flow Survey has been analysed several times, its Swedish counterpart has barely been used for model estimation so far. Using this Swedish CFS, mode and shipment size choice at the individual shipment level can be explained from characteristics of the shipper, the shipment and transport time and cost on the networks.

Earlier work at ITS Leeds used the CFS 2001 to estimate mode and shipment size models. Multinomial land nested logit models were estimated on the CFS 2004-2005 in a Master Thesis project at Delft University of Technology in The Netherlands.

This PhD project will extend the models estimated so far on the CFS 2004-2005 in many ways:

  • estimation of different models for different commodity classes (observed heterogeneity)
  • estimation of models with different transport and logistics costs functions
  • estimation of mixed logit models following the random coefficients specification to account for unobserved heterogeneity
  • estimation of models where shipment size is treated as a continuous variable instead of discrete size classes, simultaneously with (discrete) mode choice.

Furthermore the project will look into the implications of these modelling options for the value of time and freight demand elasticities – the model outputs that are typically used to evaluate transport policies.

Suggested reading:

Jong, G.C. de and M.E. Ben-Akiva (2007) “A micro-simulation model of shipment size and transport chain choice”, Special issue on freight transport ofTransportation Research B, 41, pp. 950-965, 2007.

School of Earth and Environment

Public preferences for invasive versus non-native species: a transnational study

Contact: Martin DallimerStephane Hess
Co-supervisor: Dr. Thomas Lundhede (University of Copenhagen)

Across the European Union, some 12,000 non-native species have become established, some of which can have significant impacts on native biodiversity, agriculture, forestry, fisheries and infrastructure. For example, the economic cost to Europe of invasive alien species has been estimated at €12.5 to 20 billion per year, and governments across the continent spend many billions on prevention, control and eradication programmes.

However, many invasive species, such as the grey squirrel in the UK, or any of the 13 non-native parrot species now found in Europe, can attract strong public support and concern, representing a complex socio-environmental conflict. Policy calls for culling and control are often strongly opposed even though the ecological and economic cases can be compelling. There is therefore a strong case to examine the general public’s preferences for invasive species in order to inform and set policy responses to the species. One common way of quantifying preferences in such complex situations is to design and deliver choice experiments.

The aim of this PhD will be to use choice modelling to quantify public preferences for invasive species across different countries in Europe. In particular, the project would investigate: (1) Across multiple European countries, do the general public prefer native or non-native species? (2) How do these preferences vary according to the characteristics of the species concerned? (3) Invasive species are often localised in occurrence. How might public preferences vary according to people’s individual experiences of the species concerned?

Academic Unit of Health Economics

Patient choice in healthcare - do we really know what is best for us?

Supervisors: David MeadsProfessor Stephane Hess

In order to facilitate shared decision making, patients in England are being offered more information and choice regarding their healthcare. This includes more information on treatments and hospital and health care professional (HPC) performance and more choice over treatments and over which hospitals and HPCs will provide healthcare. In making choices, particularly over treatments, patients often have to read and interpret relevant information, interpret the associated risks and accurately imagine treatment benefits and side effects such that their choices are consistent with their current and future preferences. Whether or not patients make the ‘correct’ or optimal decisions depends on their ability to interpret information and predict the benefits and harms of treatments.

This increased choice has significant implications, not only for healthcare resource management, but patient satisfaction and outcomes and potentially for issues such as treatment adherence. Using stated and/or revealed preference analysis methods, the proposed PhD research will explore the extent to which individuals are able to make optimal choices about healthcare and determine the impact on this ability of factors such as educational level, mood, experience and attitudes to risk. The research will provide important insights into the way information and choices are presented to individuals and potentially has significant policy implications.