Monitoring multidimensional phenomena with a multicriteria composite performance interval approach

TL;DR

This paper introduces a novel multicriteria composite performance interval method for analyzing multidimensional phenomena, offering insights into data aggregation and indicator balance using distance-based metrics.

Contribution

It proposes a new approach that constructs performance intervals based on different aggregation rules, incorporating non-compensability and full-compensability perspectives.

Findings

Provides a performance interval with lower and upper bounds

Uses Minkowski's $L_p$ metrics for aggregation

Interval span indicates indicator balance

Abstract

In the last two decades, composite indicators' construction to measure and compare multidimensional phenomena in a broad spectrum of domains has increased considerably. Different methodological approaches are used to summarize huge data sets of information in a single figure. This paper proposes a new approach that consists of computing a multicriteria composite performance interval based on different aggregation rules. The suggested approach provides an additional layer of information as the performance interval displays a lower bound from a non-compensability perspective, and an upper bound allowing for full-compensability. The outstanding features of this proposal are: (i) a distance-based multicriteria technique is taken as the baseline to construct the multicriteria performance interval (ii) the aggregation of distances/separation measures is made using particular cases of Minkowski's $L_p$ metrics; (iii) the span of the multicriteria performance interval can be considered as a sign of the dimensions or indicators balance.