Two novel methodologies for considering aggregation functions by implicit equations and minimization problems

In this work, we consider the problem of defining aggregation functions by means of two distinct methodologies. In the first one, we obtain an aggregation function in an implicit way, from a function which fulfills appropriate conditions. The second methodology, related to the idea of penalty function, is based on the minimization of a distance or error and can be seen as a generalization of the idea of correlation in statistics. We analyze the aggregation functions obtained with both methodologies and we present an illustrative decision-making example to show their different behaviors.