Deriving rankings from incomplete preference information: A comparison of different approaches

Volume-based methods for decision making under incomplete information like the SMAA family of methods provide rich probabilistic information to support decision making. However, they usually do not directly generate a unique ranking of alternatives. Methods to create such a unique ranking from incomplete preference information typically select one parameter vector, either by mathematical programming approaches or by averaging, and then apply a preference model using this parameter vector. In the present paper, we develop several models to infer a complete ranking or a complete preorder of alternatives directly from the probabilistic information provided by volume-based methods without singling out a specific parameter vector. We compare the results obtained by these models to those obtained with a single parameter approach in a computational study. Results indicate small, but significant differences in the performance of methods, as well as in the probability that additional preference information might worsen, rather than improve, the results. (C) 2016 Elsevier B.V. All rights reserved.