top of page

A measure of profundity of regret


The degree of regret minimization behaviour imposed by the Classical RRM or the µRRM model (or profundity of regret) 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 Classical RRM or µRRM model.


To acquire insight on the behaviour imposed by these two RRM models, a measure is recently proposed (Cranenburgh et al. 2015). With this measure it is possible to pinpoint the overall degree of regret behaviour for each attribute. The equation below shows how to compute this measure for attribute m, where |Am| denotes the cardinality Am, ßm denotes the estimated taste parameter associated with attribute m, and μ denotes the scale of the error term ε (which equals one for the classical RRM model).











When all attribute differences xjmn – ximn are non-zero, and all choice sets contain the same number of alternatives, denoted J, then we can write the equation as follows, where N denotes the total number of choice observations (given that we have one choice observation per decision makers).











To compute the measure of profundity is easier than it looks at first sight. You can even compute it easily in MS Excel. Below you can find a bit of Matlab code to compute profundity of regret. For those not familiar with Matlab I also included an MS Excel sheet.



  • Click here for a bundle of MATLAB codes, which includes code to compute profundity of regret



  • Click here to download an example Excel sheet to see how to compute profundity of regret



Van Cranenburgh, S., Guevara, C.A. & Chorus, C.G. (2015)  “New Insights on Random Regret Minimization Models”. Transportation Research Part A: Policy and Practice (74) pp 91-109.

Profundity of regret
Profundity of regret
bottom of page