An interactive approach to multicriteria sorting for quasiconcave value functions

In this article, we develop an interactive algorithm to place alternatives in ordered preference classes for a decision maker (DM) with an increasing quasiconcave value function. Such value functions are quite general in that they include linear and concave value functions. Our aim is to elicit sorting information from the DM as few times as possible; our algorithm places other alternatives using previous responses from the DM utilizing properties of quasiconcave value functions. As an application, we sort 81 global MBA programs into preference classes using criteria such as alumni career progress, idea generation, and diversity. We study the performance of our proposed algorithm, when we change the number of criteria, number of alternatives, and introduce response errors.