Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Article

Download file Download BibTeX

Title

Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies – part I

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Published in

4OR - A Quarterly Journal of Operations Research

Journal year: 2024 | Journal volume: vol. 22 | Journal number: iss. 2

Article type

scientific article

Publication language

english

Keywords
EN
  • Preference learning
  • Preference modelling
  • Multiple criteria decision aiding
  • Multiple criteria decision making
  • Machine learning
Abstract

EN Multiple criteria decision aiding (MCDA) and preference learning (PL) are established research fields, which have different roots, developed in different communities – the former in the decision sciences and operations research, the latter in AI and machine learning – and have their own agendas in terms of problem setting, assumptions, and criteria of success. In spite of this, they share the major goal of constructing practically useful decision models that either support humans in the task of choosing the best, classifying, or ranking alternatives from a given set, or even automate decision-making by acting autonomously on behalf of the human. Therefore, MCDA and PL can com- plement and mutually benefit from each other, a potential that has been exhausted only to some extent so far. By elaborating on the connection between MCDA and PL in more depth, our goal is to stimulate further research at the junction of these two fields. To this end, we first review both methodologies, MCDA in this part of the paper and PL in the second part, with the intention of highlighting their most common elements. In the second part, we then compare both methodologies in a systematic way and give an overview of existing work on combining PL and MCDA.

Date of online publication

30.01.2024

Pages (from - to)

179 - 209

DOI

10.1007/s10288-023-00560-6

URL

https://link.springer.com/article/10.1007/s10288-023-00560-6

License type

CC BY (attribution alone)

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version

Date of Open Access to the publication

in press

Full text of article

Download file

Access level to full text

public

Ministry points / journal

70

Impact Factor

1,7 [List 2023]

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.