Enhancing Personalized Decision-Making with the Balanced SPOTIS Algorithm

Andrii Shekhovtsov, Andrii Shekhovtsov, Jean Dezert, Wojciech Sałabun, Wojciech Sałabun

2025

Abstract

Besides being very useful in solving decision-making problems, classical Multi-Criteria Decision-Making (MCDM) techniques were designed to consider only profit and cost criteria. However, in some cases, it can be necessary to include more complex preferences of decision makers to better fit the problem. In such cases, modern MCDM methods such as Stable Preference Ordering Towards Ideal Solution (SPOTIS) can be used. The SPOTIS method allows for providing the Expected Solution Point (ESP) as input data for the decision problem. However, this approach can lead to unsatisfactory results if provided expert preferences are unreliable. To solve this problem, we propose a novel Balanced SPOTIS method with an ESP confidence parameter, which allows us to obtain a solution that is balanced between objectively ideal solutions and subjective expert preferences. We show how this new approach works in the case study of selecting a used car and provide an in-depth analysis of the problem using the new ESP confidence parameter for sensitivity analysis. Finally, to underline the advantages of the proposed approach, we compare it with the Expected Solution Point - Characteristic Objects Method (ESP-COMET).

Paper Citation

in Harvard Style

Shekhovtsov A., Dezert J. and Sałabun W. (2025). Enhancing Personalized Decision-Making with the Balanced SPOTIS Algorithm. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 264-271. DOI: 10.5220/0013119800003890

in Bibtex Style

@conference{icaart25,
author={Andrii Shekhovtsov and Jean Dezert and Wojciech Sałabun},
title={Enhancing Personalized Decision-Making with the Balanced SPOTIS Algorithm},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={264-271},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013119800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Enhancing Personalized Decision-Making with the Balanced SPOTIS Algorithm
SN - 978-989-758-737-5
AU - Shekhovtsov A.
AU - Dezert J.
AU - Sałabun W.
PY - 2025
SP - 264
EP - 271
DO - 10.5220/0013119800003890
PB - SciTePress