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Decision Rule Approach

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Multiple Criteria Decision Analysis: State of the Art Surveys

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 78))

Abstract

We present the methodology of Multiple-Criteria Decision Aiding (MCDA) based on preference modelling in terms of “if. ⋯, then ⋯” decision rules. The basic assumption of the decision rule approach is that the decision maker (DM) accepts to give preferential information in terms of examples of decisions and looks for simple rules justifying her decisions. An important advantage of this approach is the possibility of handling inconsistencies in the preferential information, resulting from hesitations of the DM. The proposed methodology is based on the elementary, natural and rational principle of dominance. It says that if action is at least as good as action on each criterion from a considered family, then is also comprehensively at least as good as The set of decision rules constituting the preference model is induced from the preferential information using a knowledge discovery technique properly modified, so as to handle the dominance principle. The mathematical basis of the decision rule approach to MCDA is the Dominance-based Rough Set Approach (DRSA) developed by the authors. We present some basic applications of this approach, along with didactic examples whose aim is to show in an easy way how DRSA can be used in various contexts of MCDA.

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Authors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco & Benedetto Matarazzo

  2. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań

    Roman Słowinński

  3. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowinński

Authors
  1. Salvatore Greco
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  2. Benedetto Matarazzo
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  3. Roman Słowinński
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Greco, S., Matarazzo, B., Słowinński, R. (2005). Decision Rule Approach. In: Multiple Criteria Decision Analysis: State of the Art Surveys. International Series in Operations Research & Management Science, vol 78. Springer, New York, NY. https://doi.org/10.1007/0-387-23081-5_13

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