An action learning approach for assessing the consistency of pairwise comparison data |
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| Institution: | 1. Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy;2. Department of Architectural Engineering and Technology (AE+T), Environmental & Computational Design Section, TU Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands;1. Human–Computer Interaction Center (HCIC), RWTH Aachen University, Campus Boulevard 57, 52074 Aachen, Germany;2. Institute for High Voltage Technology (IFHT), RWTH Aachen University, Schinkelstraße 2, 52056 Aachen, Germany;1. Department of Spatial Planning and Environment, University of Groningen, Groningen, The Netherlands;2. PBL Netherlands Environmental Assessment Agency, The Hague and Bilthoven, The Netherlands;1. Helen Ltd, Kampinkuja 2, 00090 Helen, Finland;2. Aalto University School of Engineering, Department of Mechanical Engineering, Otakaari 4, FIN-02015 Aalto, Finland;3. Aalto University School of Science, Department of Mathematics and Systems Analysis, Otakaari 1, FIN-02015 Aalto, Finland;4. University of Jyväskylä, School of Business and Economics, P.O. Box 35, FIN- 40014 University of Jyväskylä, Finland |
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| Abstract: | Pairwise comparison data are used in various contexts including the generation of weight vectors for multiple criteria decision making problems. If this data is not sufficiently consistent, then the resulting weight vector cannot be considered to be a reliable reflection of the evaluator’s opinion. Hence, it is necessary to measure its level of inconsistency. Different approaches have been proposed to measuring the level of inconsistency, but they are often based on ‘rules of thumb” and/or randomly generated matrices, and are not interpretable. In this paper we present an action learning approach for assessing the consistency of the input pairwise comparison data that offer interpretable consistency measures. |
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