Abstract
The use of bar graphics and tables to represent a Multi-Criteria Decision Making/Aiding (MCDM/A) problems are investigated in this study since these visualizations present a holist vision for MCDM/A situations. In this context, these visualizations bring flexibility to the decision-making process conducted in the Decision Support System (DSS) developed for the FITradeoff method, being an important advantage in this method. In order to support this study, the Neuroscience approach is aggregated to MCDM/A and a neuroscience experiment is constructed to investigate how decision-makers (DMs) evaluate bar graphs and tables in order to identify some patterns of behavior. The main task required in this experiment was to evaluate MCDM/A situations and select the alternative which performed best. Based on descriptive and statistical analyses of the results, some suggestions could be made about DMs behaviorĀ“s when the visualizations were evaluated. Therefore, for this study, two main purposes were raised: provide insights for the analyst about the use of graphical and tabular visualization in MCDM/A situations and to improve the FITradeoff Decision Support System. Regarding to the first purpose, a advising rule has been built to support the analyst in the advising process performed with the DMs. Regarding to the second purpose was suggested that tables should be included in the FITradeoff DSS. In total, 51 Management Engineering students took part in the experiment.
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This study was partially sponsored by the Coordination for the Improvements of Higher Education Personnel ā Brazil (CAPES) and the Brazilian Research Council (CNPq) for which the authors are most grateful.
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Reis Peixoto Roselli, L., de Almeida, A.T. (2020). Analysis of Graphical Visualizations for Multi-criteria Decision Making in FITradeoff Method Using a Decision Neuroscience Experiment. In: Moreno-JimƩnez, J., Linden, I., Dargam, F., Jayawickrama, U. (eds) Decision Support Systems X: Cognitive Decision Support Systems and Technologies. ICDSST 2020. Lecture Notes in Business Information Processing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-030-46224-6_3
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