At the time of creating an experimental design for a stated choice experiment, the analyst often does not precisely know which model, or decision rule, he or she will estimate once the data are collected. This paper presents two new software tools for creating stated choice experimental designs that are simultaneously efficient for regret minimisation and utility maximisation decision rules. The first software tool is a lean, easy-to-use and free-of-charge experimental design tool, which is dedicated to creating designs that incorporate regret minimisation and utility maximisation decision rules. The second tool constitutes a newly developed extension of Ngene – a widely used and richly featured software tool for the generation of experimental designs. To facilitate the use of the new software tools, this paper presents clear worked examples. It focusses on practical issues encountered when generating such decision rule robust designs, such as how to obtain priors and how to deal with alternative specific parameters. Furthermore, we analyse the robustness of the designs that we created using the new software tools. Our results provide evidence that designs optimised for one decision rule can be inefficient for another – highlighting the added value of decision rule robust designs.
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Decision rules
- Efficient design
- Random regret minimisation