Comparing distance to consensus of collective relations using OWA operators

Abstract

Consensus is understood as a unanimous agreement by all experts in a group. The goal of consensus is not the selection of several options but to develop one decision that suits the interests of the entire group under consideration. In this paper, it is assumed that collective preferences are developed with the help of commonly used ordered weighted averaging operators but resultant relations do not exhibit any property of a consensus type. That is, consensus is not reached at the first attempt of ranking alternatives. Under such circumstances, the measure of distance to consensus can be successfully used to determine how far a group collectively is from consensus. The aim of this paper is to compare, where possible, the distance to consensus of collective relations compiled with the help of most commonly used ordered weighted averaging operators. The innovative part is that this measure helps in defining an upper and lower bound of distance to consensus of the resultant collective relations. With such an analysis at hand, experts can choose a suitable ordered weighting averaging operator to formulate a collective relation that exhibits a lower distance to consensus.

Implementation of Gis-Ahp-Owa for Land Suitability Assessment in Infrastructure Investment Projects: A Case Study in Yucatán, Mexico

“…Thus, with the AHP we were able to consider all relevant elements for conflict resolution in LSA. In contrast to Khalid and Awais (2015), who proposed three methods to attain a "perfect" consensus, our study yielded results that align with Bojórquez-Tapia et al's (2016) proposal for addressing contentious projects, namely Rawlsian's "overlapping consensus." This consensus approach allows for stakeholders with divergent worldviews to support an outcome for different reasons, leading to a resolution that is more robust and sustainable.…”

“…In essence, LSA involves the incorporation of multicriteria decision analysis in a geographic information system (GIS-MCDA) to find optimal locations for infrastructure investment projects (Mazahreh et al, 2019;Latinopoulos & Kechagia, 2015;Joerin et al, 2001). Operationally, LSA entails the elicitation of relative importance weights of a set of geographical attributes, the evaluation of the state of each attribute in spatial units (pixels or polygons), and the implementation of an aggregation operator also known as aggregation function (Jin et al, 2021;Khalid & Awais, 2015), such as the Weighted Linear Combination (WLC) (Bojórquez-Tapia et al, 2003;Malczewski et al, 1997). The importance weights are obtained through MCDA, in which Saaty's (1980) Analytic Hierarchy Process (AHP) has been widely applied (Jafari & Zaredar, 2010).…”