Designing choice experiments by optimizing the complexity level to individual abilities

It has been proven repeatedly in psychology and behavioural decision theory that the complexity of the choice sets affects the consistency of the responses in choice experiments. A handful of studies can be found in the discrete choice literature that take this dependency explicitly into account at the estimation stage. But there is only limited research that investigates how the choice complexity affects the efficiency of the choice design. In this research we propose choice designs in order to estimate the heteroscedastic mixed logit model which is parametrized to model the preference heterogeneity as well as the scale heterogeneity due to the choice complexity. The heteroscedastic model assumes that the scale factor is an exponentiated linear function of some complexity measures. An increase in choice complexity leads to an increase of the error variance, hence of the choice inconsistency. We generate sequential designs, heterogeneous semi-Bayesian designs and homogeneous semi-Bayesian designs considering and ignoring the choice complexity. This way we can examine the advantage of taking the choice complexity into account at the design stage in each design approach. Simulation results show that the proposed sequential design which takes the choice complexity into account outperforms all other designs we considered. It turns out that the sequential approach generates choice sets with a constant, relatively low complexity level. As the respondents can easily cope with these choice sets, they give consistent choices and these choice sets appear to be most informative about the individual preferences.