Induced aggregation operators in the Euclidean distance and its application in financial decision making

Abstract

We develop a new decision making method by using induced aggregation operators in the Euclidean distance. We introduce a new aggregation operator called the induced Euclidean ordered weighted averaging distance (IEOWAD) operator. It is an aggregation operator that parameterizes a wide range of distance measures by using the induced OWA (IOWA) operator such as the maximum distance, the minimum distance, the normalized Euclidean distance (NED) and the weighted Euclidean distance (WED). The main advantage of this operator is that it is able to consider complex attitudinal characters of the decision maker by using order inducing variables in the aggregation of the Euclidean distance. As a result, we get a more general formulation of the Euclidean distance that considers the Euclidean distance as a particular case and a lot of other possible situations depending on the interests of the decision maker. We study some of its main properties giving special attention to the analysis of different particular types of IEOWAD operators. We apply this aggregation operator in a business decision making problem regarding the selection of investments.