D 2024

A Hybrid Stochastic-Full Enumeration Approach to a Ranking Problem with Insufficient Information

MAZUREK, Jiří and Ryszard JANICKI

Basic information

Original name

A Hybrid Stochastic-Full Enumeration Approach to a Ranking Problem with Insufficient Information

Authors

MAZUREK, Jiří (203 Czech Republic, guarantor, belonging to the institution) and Ryszard JANICKI (124 Canada)

Edition

USA, 2024 58th Annual Conference on Information Sciences and Systems (CISS), p. 1-6, 6 pp. 2024

Publisher

IEEE Xplore

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

RIV identification code

RIV/47813059:19520/24:A0000444

Organization unit

School of Business Administration in Karvina

ISSN

DOI

Keywords in English

insufficient information; Monte Carlo method; pairwise comparisons; ranking

Tags

Links

GA21-03085S, research and development project.
Changed: 20/3/2025 20:11, Miroslava Snopková

Abstract

V originále

When comparing n objects pairwise, at least (n−1) comparisons have to be performed (assuming that a corresponding directed graph is connected) for a derivation of a ranking (a total or partial order) of all objects. The aim of the paper is to introduce a novel algorithm for a case with insufficient information, that is the case when the number of available pairwise comparisons ranges from 1 to (n − 2). It is assumed that the comparisons are performed via the following two non-numerical binary relations: preference relation (≻) and indifference relation(∼). The algorithm provides a probability of each possible ranking (permutation) of all compared objects based on the revealed pairwise comparisons, while missing comparisons are modeled via full enumeration of all feasible cases (for a small number of objects), or via Monte Carlo simulations (for a large number of objects).