Sensitivity and robustness in selection problems

When using a simple linear multiattribute utility model for the selection of a preferred alternative the result depends in part on the weighting of the attributes. Given the difficulties which may make weight determination problematic it is natural to seek to justify a choice by showing that it is insensitive to weight imprecision. To do this some view must be taken of what constitutes justification. Three such views are defined and tested on a real data set: choose that alternative which maximises the number of plausible scenarios in which it is best (sensitivity); maximises the difference between best and second best (robustness); minimises the difference between a chosen candidate and the best under different weighting schemes (risk aversion). Application of these different criteria identifies a small number of alternatives, one of which is readily chosen. Some quite loose weight restrictions are applied, the clarity of the recommendations indicating that further judgmental input may not be needed.