The main goal of this research group is the acquisition of new results about the procedures of decision making, when the choice is based on binary comparisons between the alternatives and where approximate reasoning is used. The procedures are designed for structures with mixed data, both qualitative and quantitative. One distinct feature of our research is that we allow for the representation of the traits of the alternatives in different scales, not necessarily numerical. 


Since there are several types of uncertainties in a decision making problem, our research follows two directions. On the one hand, we study the concept of statistical preference as an alternative to stochastic dominance, which is the most popular stochastic order. On the other hand, we investigate imprecise methods as a tool for decision making problems where an incomplete knowledge about the probability distribution of the variables is present. Once this is done, we try to apply our results in a number of practical problems, as in the ordering of genetic algorithms. 


Historically, the group was linked to professor Pedro Gil Álvarez, and has also developed other research lines related to data analysis under imprecise or fuzzy information, as is for instance divergence measures, fuzzy random variables, or non-additive measures. 

More information:


You can contact the Research Group on Modeling of the Uncertainty and Imprecision in Decision Theory UNIMODE with the following form.