Visual Analytics for Decision-Making

Visual Analytics combines human cognitive processes and abilities with computational models to solve complex problems by using the strengths of computers and humans. Decision-making processes are commonly very complex and involve investigating various factors and indicators. By integrating Visual Analytics into the decision-making process, the human cognitive load can be reduced, and the accuracy of decisions could be higher. This might be why many visual decision support systems exist, and Visual Analytics approaches provide decision support. However, the literature review reveals that there are either structured decision support methods integrating statistical approaches or Visual Analytics methods that are commonly designed for exploration, comparison, and analysis to support decision-making. By investigating the core ideas related to human limitations and programmed decision-making, we propose in this paper a novel Visual Analytics approach to support structured and exploratory approaches by integrating Multi-Criteria Decision Making (MCDM) methods in Visual Analytics. To reduce the common expert judgment in such methods, our approach trains a neural network through the interaction with the system. Our contributions are threefold: providing a systematic view of decision-making processes, discussing the dual paradigms of visual decision support systems, and proposing a Visual Analytics approach that leverages MCDM and neural networks for improved decision support.