Recieved:

16/02/2026

Accepted:

27/08/2026

Page: 

doi:

http://dx.doi.org/10.17515/resm2026-1513dt0216rs

Views:

15

A hybrid multi criteria decision making and clustering approach for evaluating positive peace

Damla Yalçıner Çal1

1Department of Business Administration, Faculty of Economics and Administrative Sciences, Kafkas University, 36000, Kars, Türkiye

Abstract

This study is based on the indicators presented in the Positive Peace Report, which assesses countries’ levels of peace not only through the absence of conflict but also through their institutional and social structures. The positive peace approach measures countries’ capacity for sustainable peace through eight key components: a well-functioning government, low levels of corruption, a robust business environment, equitable distribution of resources, respect for the rights of others, the free flow of information, high levels of human capital, and good relations with neighbors. In this context, the aim of the study is to identify the similarity structures of 163 countries within the framework of these components and to cluster the countries. Given the multidimensional nature of positive peace and the differences in the relative importance of the criteria, it becomes necessary to determine the weights of the criteria objectively and to examine the similarities through multivariate analyses. Accordingly, the study employed the CILOS technique in conjunction with hierarchical clustering analysis. In the first stage, objective weights were determined based on eight criteria; in the second stage, similarities between countries were examined using hierarchical clustering analysis based on this framework. The findings reveal that countries are not homogeneous in terms of positive peace and are grouped into four distinct clusters. Consequently, the study contributes to the comparative analysis of the multidimensional structure of positive peace and demonstrates the effectiveness of the proposed methodological framework.

Keywords

Positive peace; CILOS; MCDM; Hierarchical clustering analysis; Data mining

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