The degree of regret minimization behaviour (or profundity of
regret) imposed by the RRM models (except for the P-RRM model) is
not constant, but depends on the size of the estimated parameter
as well as on the distribution of the attribute levels in the
data. Therefore, the parameter estimates on their own are not very
informative for the imposed behaviour by the RRM models. To
acquire insight on the behaviour imposed by these models, a formal
measure of the profundity of regret is proposed. With this measure
it is possible to pinpoint the overall degree of regret behaviour
for each attribute.
to go to the Profundity of regret software page.
Accouting for variation in choice set size in RRM models
In many choice situations the choice set size, i.e. the number of
alternatives which are available to the decision-makers, varies
across choice observations. In RRM models such variation in choice
set size is consequential for the model predictions. To account
for variation in the choice set size when estimating RRM models a
simple, but effective correction factor can be used. This
correction factor scales the overall regret.
to go to the page on Variation in choice set size in RRM models.
Robustness of RRM modelling outcomes towards omitted attributes
As discrete choice models may be misspecified, it is crucial for
choice modellers to have knowledge on the robustness of their
modelling outcomes towards misspecification. One type of model
misspecification occurs when not all attributes that are relevant
for the choice are included in the choice model. To investigate
the robustness of RRM modelling outcomes towards the omission of a
relevant attribute several Monte Carlo experiments are conducted.
to go to the page on the Robustness of RRM modelling outcomes
towards omitted attributes.
Decision rule robust experimental designs
Despite compelling evidence that decision-makers use a wide range
of decision rules when making choices, the design of Stated Choice
experiments has exclusively been based on the (often implicit)
assumption that decision-makers make choices using
(linear-additive) Random Utility Maximization (RUM) rules. In a
I show that efficient experimental designs can also be created for
RRM. One particularly important result of this study is that
designs that are efficient for estimating RUM models can be highly
inefficient for estimating RRM models, and vice versa. Therefore,
it is appealing to take multiple decision rules into account when
creating efficient experimental designs.
to go to the page on the Decision rule robust experimental