Fuzzy logic-based generalized decision theory with imperfect information

  • Authors:

  • Rafik Aliev;Witold Pedrycz;Bijan Fazlollahi;O. H. Huseynov;A. V. Alizadeh;B. G. Guirimov

  • Affiliations:

  • Department of Computer-Aided Control Systems, Azerbaijan State Oil Academy, Baku, Azerbaijan;Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Canada AB T6R 2G7 and System Research Institute, Polish Academy of Sciences, Warsaw, Poland;Center for Business Development in Transitional Economies, Georgia State University, Atlanta, GA 30302-3989, USA;Department of Computer-Aided Control Systems, Azerbaijan State Oil Academy, Baku, Azerbaijan;Azerbaijan Association of "Zadeh's Legacy", Baku, Azerbaijan;Azerbaijan Association of "Zadeh's Legacy", Baku, Azerbaijan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

The existing decision models have been successfully applied to solving many decision problems in management, business, economics and other fields, but nowadays arises a need to develop more realistic decision models. The main drawback of the existing utility theories starting from von Neumann-Moregnstern expected utility to the advanced non-expected models is that they are designed for laboratory examples with simple, well-defined gambles which do not adequately enough reflect real decision situations. In real-life decision making problems preferences are vague and decision-relevant information is imperfect as described in natural language (NL). Vagueness of preferences and imperfect decision relevant information require using suitable utility model which would be fundamentally different to the existing precise utility models. Precise utility models cannot reflect vagueness of preferences, vagueness of objective conditions and outcomes, imprecise beliefs, etc. The time has come for a new generation of decision theories. In this study, we propose a decision theory, which is capable to deal with vague preferences and imperfect information. The theory discussed here is based on a fuzzy-valued non-expected utility model representing linguistic preference relations and imprecise beliefs.