Abstract argumentation frameworks to promote fairness and rationality in multi-experts multi-criteria decision making

In this work, we focus on multi-criteria decision making and in particular, in the case of multiple experts (ME-MCDM). The problem of making decisions when multiple (possibly conflicting) criteria are involved often boils down to identifying an aggregation function that will combine all appreciations of the multiple dimensions of the problem. In the case of Multiple Experts, decisions even already exist and the goals are to (1) make a decision based on the potentially multiple views of multiple experts and/or (2) use these decisions are informations about what the aggregation function of a “super” expert should be in the aim to make future decisions. Unfortunately, this line of approaches tends to overlook the irrationality and/or lack of fairness of experts, aggregating all available prior information regardless of quality. In this work, we propose to model Multi-Experts Multi-Criteria DecisionMaking (MEMCDM) problems using argumentation frameworks. We specifically design our proposed model so as to emulate fairness and rationality in decisions. For instance, when, of two expert’s decisions, one is unfair, we impose an attack between these two decisions, forcing one of the two decisions out of the argumentation network’s resulting extensions. Similarly, we specifically put irrational decisions in opposition to force one out. In doing so, we aim to enable the prediction of decisions that are themselves fair and rational. Our model is illustrated on two toy examples.