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
Edition
USA, 2024 58th Annual Conference on Information Sciences and Systems (CISS), p. 1-6, 6 pp. 2024
Other information
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
RIV identification code
RIV/47813059:19520/24:A0000444
Organization unit
School of Business Administration in Karvina
Keywords in English
insufficient information; Monte Carlo method; pairwise comparisons; ranking
Links
GA21-03085S, research and development project.
In the original language
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).
Displayed: 30/1/2026 02:32