The
µ
RRM Model
The µRRM model generalizes the RRM2010 model by allowing a
parameter µ to be estimated. This parameter acts as a shape
parameter, despite the fact that it is confounded with the scale.
The µRRM model has three special cases: 1) the RRM2010 model, 2)
the linear-additive RUM model, and 3) the P-RRM model. See
Cranenburgh et al. 2015
for a more extensive description of this model.
By estimating µ we essentially estimate the shape of the attribute
level regret function. The four plots in the figure below show the
different shapes this function can take, depending on the size of µ.
The size of µ is also informative for the degree of regret
minimization behaviour imposed by the µRRM model (i.e. profundity of
regret). Estimating a relatively large µ signals a relatively mild
profundity of regret; while, vice versa, estimating a relatively
small µ signals a relatively strong profundity of regret. Finally,
it is important to note that the size of µ in the µRRM model is not
in any way related to the degree of determinism of the predicted
choice behaviour.
The figures show that the attribute level regret function can take
different shapes. From the left to the right the size of µ
increases. The outer left plot corresponds with a very small µ
(i.e. µ=0,01); the outer right plot corresponds to a large µ (i.e.
µ=5).
To interpret the estimated parameters it is useful to compute profundities of regret for each of the taste parameters, denoted αm. These αm show how much regret behaviour is imposed with regret to attribute m. Click here to go to the 'Profundity of regret' page.
To interpret the estimated parameters it is useful to compute profundities of regret for each of the taste parameters, denoted αm. These αm show how much regret behaviour is imposed with regret to attribute m. Click here to go to the 'Profundity of regret' page.