Approaches to consistency adjustment
Journal of Optimization Theory and Applications
Statistical theory for the ratio model of paired comparisons
Journal of Mathematical Psychology
A statistical look at Saaty's method of estimating pairwise preferences expressed on a ratio scale
Journal of Mathematical Psychology
A Monte Carlo study of pairwise comparison
Information Processing Letters
Interpretation of criteria weights in multicriteria decision making
Computers and Industrial Engineering
Comparison of statistical procedures in analytic hierarchy process using a ranking test
Mathematical and Computer Modelling: An International Journal
Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process
Computers and Operations Research
Post-pruning in decision tree induction using multiple performance measures
Computers and Operations Research
An exact global optimization method for deriving weights from pairwise comparison matrices
Journal of Global Optimization
Post-pruning in regression tree induction: An integrated approach
Expert Systems with Applications: An International Journal
A method for approximating pairwise comparison matrices by consistent matrices
Journal of Global Optimization
Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process
Computers and Operations Research
Estimating ratio scale values when units are unspecified
Computers and Industrial Engineering
A different perspective on a scale for pairwise comparisons
Transactions on computational collective intelligence I
Enumerating all spanning trees for pairwise comparisons
Computers and Operations Research
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mathematical and Computer Modelling: An International Journal
How to combine inconsistent ordinal and cardinal preferences: A satisficing modelling approach
Computers and Industrial Engineering
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Pairwise comparison is commonly used to estimate preference values of finite alternatives with respect to a given criterion. We discuss 18 estimating methods for deriving preference values from pairwise judgment matrices under a common framework of effectiveness: distance minimization and correctness in error free cases. We point out the importance of commensurate scales when aggregating all the columns of a judgment matrix and the desirability of weighting the columns according to the preference values. The common framework is useful in differentiating the strength and weakness of the estimated methods. Some comparison results of these 18 methods on two sets of judgment matrices with small and large errors are presented. We also give insight regarding the underlying mathematical structure of some of the methods.