Excluded-mean-variance neural decision analyzer for qualitative group decision making

  • Authors:

  • Ki-Young Song;Janusz Kozinski;Gerald T. G. Seniuk;Madan M. Gupta

  • Affiliations:

  • Earth & Space Science & Engineering, York University, Toronto, ON, Canada;Earth & Space Science & Engineering, York University, Toronto, ON, Canada;College of Law, University of Saskatchewan, Saskatoon, SK, Canada;Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada

  • Venue:
  • Advances in Fuzzy Systems - Special issue on Real-Life Applications of Fuzzy Logic
  • Year:
  • 2012

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Abstract

Many qualitative group decisions in professional fields such as law, engineering, economics, psychology, and medicine that appear to be crisp and certain are in reality shrouded in fuzziness as a result of uncertain environments and the nature of human cognition within which the group decisions are made. In this paper we introduce an innovative approach to group decision making in uncertain situations by using amean-variance neural approach. The key idea of this proposed approach is to compute the excluded mean of individual evaluations and weight it by applying a variance influence function (VIF); this process of weighting the excluded mean by VIF provides an improved result in the group decision making. In this paper, a case study with the proposed excludedmean-variance approach is also presented. The results of this case study indicate that this proposed approach can improve the effectiveness of qualitative decision making by providing the decision maker with a new cognitive tool to assist in the reasoning process.