Road to robust prediction of choices in deterministic MCDM

•Linear value function approximation was found suitable for preference prediction.•Estimated and Analytic Hierarchy Process weights were the best prediction methods.•Bayesian model showed a small effect of linear consistency and value difference. We compare five different prediction methods (linear...

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Veröffentlicht in:European journal of operational research Jg. 259; H. 1; S. 229 - 235
Hauptverfasser: Pajala, Tommi, Korhonen, Pekka, Wallenius, Jyrki
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 16.05.2017
Schlagworte:
Bayesian model
Linear value function
Multi objective programming
Multiple criteria decision making
Preference prediction
Multi objective programming
Multiple criteria decision making
Preference prediction
Bayesian model
Linear value function
ISSN:0377-2217, 1872-6860
Online-Zugang:Volltext
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Zusammenfassung:•Linear value function approximation was found suitable for preference prediction.•Estimated and Analytic Hierarchy Process weights were the best prediction methods.•Bayesian model showed a small effect of linear consistency and value difference. We compare five different prediction methods (linear estimated weights, AHP weights, equal weights, logistic regression, and a lexicographic method) in their success rate for predicting preferences in pairwise choices. Students were asked to make pairwise comparisons between student apartments on four criteria: size, rent, travel time to the university and travel time to a (hobby) location of their choice. First ten choices were used to set up the estimation model, and subsequent ten choices are used for prediction. We find that the linear estimation method has the highest prediction success rate. Furthermore, the probability of predicting a choice correctly differs only slightly (by 0.1) between linear consistent and inconsistent subjects, ie. subjects whose preferences were consistent or inconsistent with a linear value function. This shows that in the absence of other preference information, a linear value function is suitable for prediction purposes.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2016.10.001