A review of partial information in additive multicriteria methods

The relevance of multiple criteria decision-making/aiding is reinforced by the prominence of these methods in a wide range of applications. Whether by solving problems with a single decision-maker (DM) or a group of DMs, additive modelling, based on value or utility functions, is the most traditional. However, applying this kind of method raises a critical issue: the difficulty in eliciting DM’s preferences and recommending a decision. Actually, it is a hard task for the DM to provide complete information regarding their preferences, because the DM may not be able to provide such information in the detailed way required, or even they may not be willing to do so. From this perspective, the emergence and growth of partial (incomplete or imprecise) information-based methods is indicative that these are a useful way of guiding decision-making as they require less cognitive input from a DM. Thus, this paper systematically reviews the literature on multicriteria decision methods that deal with partial information, focusing on the Multi-Attribute Value/Utility Theory context. Strategic research questions guide a bibliometric and content analysis of 105 peer-reviewed papers selected from the Web of Science (Main Collection). An integrated analysis of the results provides scholars, researchers and other professionals with a deeper comprehension of methodological advances and respective contributions, and of the main challenges and trends in this field of knowledge. Our analysis aims to show that when these methods are applied more reliable decision-making can be achieved.