Abstract
We propose three procedures based on association rules (AR) learning and random forests (RF) to support the specification of a portfolio choice model applied in data from complex choice experiment data, specifically a Participatory Value Evaluation (PVE) choice experiment. In a PVE choice experiment, respondents choose a combination of alternatives, subject to a resource constraint. We combine a methodological-iterative (MI) procedure with AR learning and RF models to support the specification of parameters of a portfolio choice model. Additionally, we use RF model predictions to contrast the validity of the behavioural assumptions of different specifications of the portfolio choice model. We use data of a PVE choice experiment conducted to elicit the preferences of Dutch citizens for lifting COVID-19 measures. Our results show model fit and interpretation improvements in the portfolio choice model, compared with conventional model specifications. Additionally, we provide guidelines on the use of outcomes from AR learning and RF models from a choice modelling perspective.
Original language | English |
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Article number | 100397 |
Journal | Journal of Choice Modelling |
Volume | 46 |
DOIs | |
Publication status | Published - 2023 |
Keywords
- Association rules
- Choice experiments
- Machine learning
- Participatory value evaluation
- Random forests
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Dive into the research topics of 'Data-driven assisted model specification for complex choice experiments data: Association rules learning and random forests for Participatory Value Evaluation experiments'. Together they form a unique fingerprint.Datasets
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Data of the dissertation "Data-driven methods to study individual choice behaviour: with applications to discrete choice experiments and Participatory Value Evaluation experiments"
Hernández, J. I. (Creator), TU Delft - 4TU.ResearchData, 28 Jun 2023
DOI: 10.4121/E109FDB0-058F-4633-831E-8030C4D03ED4
Dataset/Software: Dataset