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More Precise Decision-Making Methodology in the Temporalized Body of Evidence. Application in the Information Technology Management

Author

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  • Gia Sirbiladze

    (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)

  • Otar Badagadze

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

  • Mikheil Kapanadze

    (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 a body of evidence and possibilistic Extremal Fuzzy Dynamic System (EFDS) for the construction of more precise decisions based on the expert knowledge stream. The process of decision precision consists of two stages. In the first stage the relation of information precision is defined on a monotone sequence of bodies of evidence. The principle of negative imprecision is developed, as the maximum principle of knowledge ignorance measure of a body of evidence. Corresponding mathematical programming problem is constructed. On the output of the first stage we receive the expert knowledge precision stream of the criteria with respect to any decision. In the second stage the constructed stream is an input trajectory for the finite possibilistic model of EFDS. A genetic algorithm approach is developed for identifying of the EFDS finite model. The modelling process gives us the more precise decisions as a prediction of a temporalization procedure. The constructed technology is applied in the non-probabilistic utility theory for the information technology management problem.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:06:n:s0219622016500371
    DOI: 10.1142/S0219622016500371
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    References listed on IDEAS

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    1. Dubois, Didier, 2006. "Possibility theory and statistical reasoning," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 47-69, November.
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    6. 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.
    7. Sirbiladze, Gia & Khutsishvili, Irina & Ghvaberidze, Bezhan, 2014. "Multistage decision-making fuzzy methodology for optimal investments based on experts’ evaluations," European Journal of Operational Research, Elsevier, vol. 232(1), pages 169-177.
    8. 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.
    9. Guangxu Li & Gang Kou & Changsheng Lin & Liang Xu & Yi Liao, 2015. "Multi-attribute decision making with generalized fuzzy numbers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1793-1803, November.
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    Cited by:

    1. Paulo Cesar Schotten & Danielle Costa Morais, 2019. "A group decision model for credit granting in the financial market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-19, December.

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