Journal of Business Economics

, Volume 86, Issue 1–2, pp 55–83 | Cite as

Robust ordinal regression for decision under risk and uncertainty

  • Salvatore CorrenteEmail author
  • Salvatore Greco
  • Benedetto Matarazzo
  • Roman Słowiński
Original Paper


We apply the Robust Ordinal Regression (ROR) approach to decision under risk and uncertainty. ROR is a methodology proposed within multiple criteria decision aiding (MCDA) with the aim of taking into account the whole set of instances of a given preference model, for example instances of a value function, which are compatible with preference information supplied by the Decision Maker (DM) in terms of some holistic preference comparisons of alternatives. ROR results in two preference relations, necessary and possible; the necessary weak preference relation holds if an alternative is at least as good as another one for all instances compatible with the DM’s preference information, while the possible weak preference relation holds if an alternative is at least as good as another one for at least one compatible instance. To apply ROR to decision under risk and uncertainty we have to reformulate such a problem in terms of MCDA. This is obtained by considering as criteria a set of quantiles of the outcome distribution, which are meaningful for the DM. We illustrate our approach in a didactic example based on the celebrated newsvendor problem.


Multiple criteria decision aiding Robust ordinal regression Decision under risk and uncertainty Additive value functions Outranking methods 

JEL Classification




The first two authors wish to acknowledge funding by the “Programma Operativo Nazionale” Ricerca & Competitivitá “2007–2013” within the project “PON04a2 E SINERGREEN-RES-NOVAE” and by the FIR of the University of Catania BCAEA3 “New developments in Multiple Criteria Decision Aiding (MCDA) and their application to territorial competitiveness”.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Salvatore Corrente
    • 1
    Email author
  • Salvatore Greco
    • 1
    • 2
  • Benedetto Matarazzo
    • 1
  • Roman Słowiński
    • 3
    • 4
  1. 1.Department of Economics and BusinessUniversity of CataniaCataniaItaly
  2. 2.Centre of Operations Research and Logistics (CORL)University of Portsmouth, Portsmouth Business SchoolPortsmouthUK
  3. 3.Institute of Computing SciencePoznań University of TechnologyPoznanPoland
  4. 4.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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