Incorporating user cognitive profile information in intelligent decision support systems

Intelligent decision support systems (iDSS) frequently rely in analytical models to improve problem solving capabilities of decision makers. A large number of intelligent algorithms solely focus on accuracy, but this is seldom the only, or even the most important issue to be considered in decision making processes. This work investigates how to incorporate user cognitive profile preferences in the intelligent decision model. Two situations were considered in this study: (i) how to conceive appropriate models when user preferences and constraints are available and (ii) how to optimize a problem solving structure if these models are already available (i.e. can be posed by its user - the decision maker). Due to their effective application in many classification problems and their high interpretability, decision trees were chosen as the main inference technique and were used in four benchmark databases as our proof of concept for both: conception and optimization of intelligent models. Results suggest that the proposed approach can be useful to better bridge the gap between what the user wants and what can be provided to him, by means of intelligent algorithms. This simple, yet powerful, combination affords high levels of user satisfaction and confidence because they reduce the loss of valuable qualitative information that is readily available in the decision makers' mind. Moreover it is likely to relief the number one plague in DSSs: dismissive attitude by decision makers, leading to quite often systems dismissal.