Dominance-based rough set approach to preference learning from pairwise comparisons in case of decision under uncertainty

  • Authors:

  • Salvatore Greco;Benedetto Matarazzo;Roman Słowiński

  • Affiliations:

  • Faculty of Economics, University of Catania, Catania, Italy;Faculty of Economics, University of Catania, Catania, Italy;Institute of Computing Science, Poznań University of Technology, Poznań, and Institute for Systems Research, Polish Academy of Sciences, Warsaw, Poland

  • Venue:
  • IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
  • Year:
  • 2010

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Abstract

We deal with preference learning from pairwise comparisons, in case of decision under uncertainty, using a new rough set model based on stochastic dominance applied to a pairwise comparison table. For the sake of simplicity we consider the case of traditional additive probability distribution over the set of states of the world; however, the model is rich enough to handle non-additive probability distributions, and even qualitative ordinal distributions. The rough set approach leads to a representation of decision maker's preferences under uncertainty in terms of "if..., then..." decision rules induced from rough approximations of sets of exemplary decisions. An example of such decision rule is "if actais at least strongly preferred to acta′ with probability at least 30%, andais at least weakly preferred to acta′ with probability at least 60%, then actais at least as good as acta′.