An Adaptive Algorithm for Pairwise Comparison‐based Preference Measurement

The Pairwise Comparison-based Preference Measurement (PCPM) approach has been proposed for products featuring a large number of attributes. In the PCPM framework, a static two-cyclic design is used to reduce the number of pairwise comparisons. However, adaptive questioning routines that maximize the information gained from pairwise comparisons promise to further increase the efficiency of this approach. This paper introduces a new adaptive algorithm for PCPM, which accounts for several response errors. The suggested approach is compared with an adaptive algorithm that was proposed for the Analytic Hierarchy Process as well as a random selection of pairwise comparisons. By means of Monte Carlo simulations, we quantify the extent to which the adaptive selection of pairwise comparisons increases the efficiency of the respective approach.