Data-driven assisted model specification for complex choice experiments data: Association rules learning and random forests for Participatory Value Evaluation experiments

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 experiment data, specifically Participatory Value Evaluation (PVE) experiment. In PVE experiment, respondents choose combination alternatives, subject resource constraint. combine methodological-iterative (MI) procedure with AR RF models parameters model. Additionally, we use predictions contrast validity behavioural assumptions different specifications conducted elicit preferences Dutch citizens for lifting COVID-19 measures. Our results show fit interpretation improvements model, compared conventional specifications. provide guidelines outcomes modelling perspective. • data-driven methods assist models. obtain goodness-of-fit assisted Additional interpretations are possible methods.