Biased experts and similarity based weights in preferences aggregation

In a group decision making setting, we consider the po-tential impact an expert can have on the overall ranking by providing a biased assessment of the alternatives that differs substantially from the majority opinion. In the framework of similarity based averaging functions, we show that some alternative approaches to weighting the experts ’ inputs during the aggregation process can min-imize the influence the biased expert is able to exert