Research & Projects
Research & Projects
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Discrete choice modelling is a well-developed and theoretically underpinned modelling framework, which is used in various scientific disciplines for studying human behaviour, including transport, health, economics, and marketing. Discrete choice models are typically used to infer preferences over attributes and alternatives as well as to compute consumers' willingness to pay, e.g. to improve a service or product. As such, discrete choice models are often instrumental to policymaking.
Choice modelling involves mixing formal behavioural theories and statistical methods with subjective judgments of the model builder (a.k.a. the choice modeller). Model building is often considered an art, and involves various stages, such as data collection, descriptive analysis, model specification development, and interpretation of outcomes. During the modelling process, numerous small and large decisions must be made by the choice modeller, such as which analyses to conduct and which model specifications to test. Nowadays there are various textbooks and courses available to learn modelling choice behaviour.
However, the full modelling process leading to the final choice models reported in scientific papers and/or used for policy analysis is only partially codified. Small, but potentially important, modelling steps are underreported or even overlooked, especially those prior to the actual estimation of choice models. This lack of knowledge of the entire modelling process hampers discussion and development of best practices in choice modelling. Moreover, it holds back efforts to automate part of the modelling process in software packages.
The project has two main objectives. Firstly, it aims to provide a comprehensive understanding of the various modelling steps employed by choice modellers during the collection of choice data and the development of choice models. Secondly, it seeks to develop a set of tools that can support choice modellers by offering recommendations and automating various stages of data collection, analysis, model specification development, and outcome interpretation. These tools will streamline the process and enhance the efficiency of choice modelling, enabling researchers to make better-informed modelling decisions and draw meaningful conclusions from their analyses.
At the same time and before starting my PhD, I worked as a research associate at the Institute of Complex Systems Engineering (University of Chile). The two main lines of research were related to:
Complex Engineering Systems Institute - University of Chile
Development of a methodology that, with the use of biosensors (Biomonitor V3.0) and with a contextual application plus a software architecture, allows the capture and storage of information from psychophysiological data of users in different contexts, such as the use of the bicycle, the use of public transport, the driving of vehicles, the analysis of teleworking, the immersion in monitoring and virtual reality, the use of sport (golf) and the analysis of the validation of IAPS.
RUM-DFT
Civil Engineering Department- University of Chile
Formulation and validation of RUM-DFT that considers the complete sequence of intermediate decisions in the attention to the preselection part, given the well-known attention information matrix. Bayesian estimation is investigated to find the order of the attention attributes with only the choice in a choice situation.
Eyetracker example (sequence of attended attributes)
Dynamics of utilities in the decision-makers' information search process. The solid and segmented lines show the values of the utilities of the alternatives under the RUM-DFT and RUM modelling, respectively. A) A choice situation with 4 alternatives and 2 attributes is considered (travel cost and travel time). B) A choice situation with 4 alternatives and 3 attributes is considered (travel cost, travel time and waiting time)
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