The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

Libório, Matheus Pereira and Karagiannis, Roxani and Diniz, Alexandre Magno Alvez and Ekel, Petr Iakovlevitch and Vieira, Douglas Alexandre Gomes and Ribeiro, Laura Cozzi (2024) The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators. Entropy, 26 (2). p. 143. ISSN 1099-4300

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Abstract

This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts’ uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon’s theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.

Item Type: Article
Subjects: STM Academic > Multidisciplinary
Depositing User: Unnamed user with email support@stmacademic.com
Date Deposited: 06 Feb 2024 11:54
Last Modified: 06 Feb 2024 11:54
URI: http://article.researchpromo.com/id/eprint/2167

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