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
Other information
Type of outcome
Stať ve sborníku
Field of Study
10200 1.2 Computer and information sciences
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
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.
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).
Displayed: 26/12/2024 23:42