Estimating decision rule differences between ‘best’ and ‘worst’ choices in a sequential best worst discrete choice experiment

Since the introduction of Discrete Choice Analysis, countless efforts have been made to enhance the efficiency of data collection through choice experiments and to improve the behavioural realism of choice models. One example development in data collection are best-worst discrete choice experiments (BWDCE), which have the benefit of obtaining...

New software tools for creating stated choice experimental designs efficient for regret minimisation and utility maximisation decision rules

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...

Combining data gathering efficiency with behaviourally realistic modelling: A case of park-and-ride facility choice data gathered with a Sequential Best Worst Discrete Choice Experiment and estimated with a Random Regret Minimisation model

This research combines two relatively new additions to the field of discrete choice modelling: sequential best worst discrete choice experiments (SBWDCE) and random regret minimisation (RRM) modelling, with the hope of developing a more behaviourally realistic choice model. SBWDCEs are able to gather a larger number of stated choice observations...

Consumer surplus for random regret minimisation models

This paper is the first to develop a measure of consumer surplus for the Random Regret Minimisation (RRM) model. Following a not so well-known approach proposed two decades ago, we measure (changes in) consumer surplus by studying (changes in) observed behaviour, i.e. the choice probability, in response to price (changes). We interpret the...