Ordered Weighted Averaging Operators: A Short Review

Abstract

Aggregation is the process of combining several numerical values into a single representative one, a procedure called an aggregation function. Despite the simplicity of this definition, the size of the field of its applications is incredibly huge. Making decisions (in also artificial intelligence) often leads to aggregating preferences or scores on a given set of alternatives. The concept of the ordered weighted averaging (OWA) operator, a symmetric aggregation function that allocates weights according to the input value and unifies in one operator the conjunctive and disjunctive behavior, was introduced by Yager in 1988. Since then, these functions have been axiomatized and extended in various ways. OWA operators provide a parameterized family of aggregation functions, including many of the wellknown operators. This function has attracted the interest of several researchers, and therefore, a considerable number of articles in which its properties are studied and its applications are investigated have been published. The development of an appropriate methodology for obtaining the weights is still an issue of great interest. This work provides a short review of OWA operators and gives an overview of some of the most significant results.