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Temporalized Structure of Bodies of Evidence in the Multi-Criteria Decision-Making Model

Author

Listed:
  • Gia Sirbiladze

    (Department of Computer Sciences, Iv. Javakhishvili Tbilisi State University, University St. 13, 0186 Tbilisi, Georgia)

  • Koba Gelashvili

    (Department of Computer Sciences, Iv. Javakhishvili Tbilisi State University, University St. 13, 0186 Tbilisi, Georgia)

  • Irina Khutsishvili

    (Department of Computer Sciences, Iv. Javakhishvili Tbilisi State University, University St. 13, 0186 Tbilisi, Georgia)

  • Anna Sikharulidze

    (Department of Computer Sciences, Iv. Javakhishvili Tbilisi State University, University St. 13, 0186 Tbilisi, Georgia)

Abstract

In this paper, we perform the analysis of temporalized structure of bodies of evidence to construct more precise decisions based on the mathematical model of experts’ evaluations. The relation of information precision is defined on a monotone sequence of the bodies of evidence. For determining of a body of evidence the maximum principles of nonspecificity measure, the Shannon and Shapley entropies are applied. Corresponding mathematical programming problems are constructed. A new approach for the numerical solution of these problems is developed. The temporalized structure of bodies of evidence is used for precising the decision in the well-known Kaufmann’s theory of expertons. A measure of increase of decision precision is introduced, which takes into account all steps of temporalization. The temporalized method of expertons is applied to the problem of decision risk management, where the investment fund expert commission provides evaluation of competition results. In our specially created decision-making model, the goal of the expert technology is to aggregate and refine subjective evaluations provided by the expert commission members. The model performs as an adviser that assists the expert commission in selecting of decision with a minimum risks. The results of developed method are then compared with other well-known methods and aggregation operators such as: mean, median, ordered weighed averaging (OWA) and method of expertons.

Suggested Citation

  • Gia Sirbiladze & Koba Gelashvili & Irina Khutsishvili & Anna Sikharulidze, 2015. "Temporalized Structure of Bodies of Evidence in the Multi-Criteria Decision-Making Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 565-596.
    • Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:03:n:s021962201550008x
    DOI: 10.1142/S021962201550008X
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    Citations

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    Cited by:

    1. Gia Sirbiladze & Otar Badagadze, 2017. "Intuitionistic Fuzzy Probabilistic Aggregation Operators Based on the Choquet Integral: Application in Multicriteria Decision-Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 245-279, January.
    2. Gia Sirbiladze & Irina Khutsishvili & Otar Badagadze & Mikheil Kapanadze, 2016. "More Precise Decision-Making Methodology in the Temporalized Body of Evidence. Application in the Information Technology Management," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1469-1502, November.
    3. Gia Sirbiladze, 2016. "New Fuzzy Aggregation Operators Based on the Finite Choquet Integral — Application in the MADM Problem," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 517-551, May.
    4. Gia Sirbiladze & Anna Sikharulidze, 2018. "Extensions of Probability Intuitionistic Fuzzy Aggregation Operators in Fuzzy MCDM/MADM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 621-655, March.

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