Road to robust prediction of choices in deterministic MCDM

Tommi Pajala, Pekka Korhonen, Jyrki Wallenius*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)229-235
Number of pages7
JournalEuropean Journal of Operational Research
Volume259
Issue number1
DOIs
Publication statusPublished - May 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Bayesian model
  • Linear value function
  • Multi objective programming
  • Multiple criteria decision making
  • Preference prediction

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