Efficient experimental designs aim to maximise the information obtained from stated choice data to estimate discrete choice models' parameters statistically efficiently. Almost without exception efficient experimental designs assume that decision-makers use a Random Utility Maximisation (RUM)...

This paper investigates the potential of association rules learning and random forests to analyse data from complex choice experiments, and to obtain relevant insights for choice modellers and practitioners. We apply these data-driven methods on data from a so-called Participatory Value...