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Title: SOLUTION IMPROVEMENT IN THE TASK OF MULTI-EXTREMAL STOCHASTIC PROGRAMMING
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SOLUTION IMPROVEMENT IN THE TASK OF MULTI-EXTREMAL STOCHASTIC PROGRAMMING
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1314-2704
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English
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21
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7.2
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• Prof. DSc. Oleksandr Trofymchuk, UKRAINE
• Prof. Dr. hab. oec. Baiba Rivza, LATVIA |
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Decision-making theory has been developing significantly for the last 50 years. It finds its various applications in the very different fields of human activities: engineering, social and life science, and sure economics. The task of substantiating decisions under conditions of uncertainty of all types, except for a priori is reduced to narrowing the initial set of alternatives based on the information available to the decision maker. The quality of recommendations for decision-making under conditions of stochastic uncertainty is enhanced by taking into account such characteristics of the decision maker's personality as the attitude to one's own gains and losses, and the propensity to take risks. Justification of decisions under conditions of a priori uncertainty is possible by constructing adaptive control algorithms. While solving multi-stage tasks, it is necessary to minimize corresponding criteria of the objective function at every stage, which due to disturbing initial conditions of the problem, may take place. As one of the possible approaches to this task, let us take for consideration multi-extremal task of stochastic programming.
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conference
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21st International Multidisciplinary Scientific GeoConference SGEM 2021
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21st International Multidisciplinary Scientific GeoConference SGEM 2021, 16 - 22 August, 2021
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Proceedings Paper
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STEF92 Technology
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SGEM International Multidisciplinary Scientific GeoConference
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SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci & Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts & Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci
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871-876
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16 - 22 August, 2021
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website
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cdrom
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8206
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decision theory; mathematical optimization; mathematical programming; multi-extremal tasks
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