Collective Aggregation Model Based on the Hybridization of the EVAMIX Method and the VMAVA+ Voting Method

In today’s society, collective decision‐making is an essential part of the process. It involves making a choice, finding a consensus between several divergent or even conflicting opinions. Numerous group decision‐making methods have been developed, but some have shortcomings and controversial results. The EVAMIX method is a decision support method for use with a single decision‐maker. It has good properties and is already widely used to solve certain choice problems with a single decision‐maker. In this work, our aim is to contribute to collective decision‐making by proposing a collective choice model combining the EVAMIX method and the VMAVA + voting method, with good properties according to the literature. To do this, we developed the model from these two methods by hybridizing them, presented the desired properties that it verifies, and ended by studying its algorithmic complexity by first developing its algorithm. An application was then made on examples, and a comparison of the results obtained with those of some existing collective aggregation methods in the literature led to the conclusion that it provides satisfactory results.

Authors

Institutions

Publication Details

Journal
International Journal of Mathematics and Mathematical Sciences
Published
2025-01-01
DOI
https://doi.org/10.1155/ijmm/3384629
Citations
1
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
2.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Collective Aggregation Model Based on the Hybridization of the EVAMIX Method and the VMAVA+ Voting Method

Zoïnabo Savadogo, Hadarou Yiogo
1 citations
International Journal of Mathematics and Mathematical Sciences
Multi-Criteria Decision Making
2.11
article

Collective Aggregation Model Based on the Hybridization of the EVAMIX Method and the VMAVA+ Voting Method

Zoïnabo Savadogo, Hadarou Yiogo
article en
1 citations

Abstract

In today’s society, collective decision‐making is an essential part of the process. It involves making a choice, finding a consensus between several divergent or even conflicting opinions. Numerous group decision‐making methods have been developed, but some have shortcomings and controversial results. The EVAMIX method is a decision support method for use with a single decision‐maker. It has good properties and is already widely used to solve certain choice problems with a single decision‐maker. In this work, our aim is to contribute to collective decision‐making by proposing a collective choice model combining the EVAMIX method and the VMAVA + voting method, with good properties according to the literature. To do this, we developed the model from these two methods by hybridizing them, presented the desired properties that it verifies, and ended by studying its algorithmic complexity by first developing its algorithm. An application was then made on examples, and a comparison of the results obtained with those of some existing collective aggregation methods in the literature led to the conclusion that it provides satisfactory results.

International Journal of Mathematics and Mathematical SciencesVol. 2025(1)
Université Joseph Ki-Zerbo (BF)
Openalex Percentile: Top 10%
Multi-Criteria Decision Making
2.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Collective Aggregation Model Based on the Hybridization of the EVAMIX Method and the VMAVA+ Voting Method — Zoïnabo Savadogo, Hadarou Yiogo · International Journal of Mathematics and Mathematical Sciences (2025) | TGRS Research Map | TGRS