Name
Abstract | ||
|---|---|---|
This paper examines the extent to which methods used for analyzing judgments from a group of decision makers result in preference rankings which are consistent with the group member’s subjective beliefs about their preferences. In a laboratory group decision setting, the Analytic Hierarchy Process, a newly developed probabilistic extension to the AHP, and two pairwise voting methods are used to develop separate preference rankings based on input from the group members. The probabilistic version of the AHP, in its first independent test, is found to provide more insight into the group decision while requiring fewer a priori assumptions. A commonly used and easily implemented pairwise voting procedure is found to be significantly inferior to the other methods in its ability to capture the group’s preference rankings. This underscores the importance of using appropriate decision models for developing a full understanding of group preferences. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1016/S0377-2217(99)00196-4 | European Journal of Operational Research |
Keywords | Field | DocType |
Analytic hierarchy processes,Decision support systems,Multicriteria analysis | Decision analysis,Decision tree,Potentially all pairwise rankings of all possible alternatives,Artificial intelligence,R-CAST,Management science,Decision rule,Pairwise comparison,Mathematical optimization,Evidential reasoning approach,Machine learning,Decision engineering,Mathematics | Journal |
Volume | Issue | ISSN |
125 | 1 | 0377-2217 |
Citations | PageRank | References |
10 | 1.04 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert F. Easley | 1 | 217 | 13.67 |
| Joseph S. Valacich | 2 | 3136 | 354.00 |
| M. A. Venkataramanan | 3 | 245 | 20.52 |