Decision making theory

Course: Informatics

Structural unit: Faculty of Computer Science and Cybernetics

Title
Decision making theory
Code
ННД.21
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
7 Semester
Number of ECTS credits allocated
3
Learning outcomes
- Use modern mathematical apparatus of continuous and discrete analysis, linear algebra, and analytical geometry to solve theoretical and applied problems in the design and implementation of informatization objects. - Demonstrate knowledge about regularities of random phenomena, their properties, and operations on them, about models of random processes and modern software environments for solving some problems of statistical data processing and predictive models construction.
Form of study
Full-time form
Prerequisites and co-requisites
1. Know materials from standard courses in mathematical analysis, linear algebra, discrete mathematics, differential equations, operations research, probability theory, and mathematical statistics. 2. Be able to build and research decision-making models based on decision-making theory, utility theory, and expert procedures in conditions of certainty, risk, uncertainty, and in conditions of conflict. 3. Possess basic skills of building decision-making models.
Course content
Acquaintance and assimilation of the basic principles of decision-making model research, acquisition of practical decision-making skills in various spheres of activity. The basics of the theory of utility, decision-making in conditions of certainty, risk, uncertainty, conflict, and vagueness of data are considered. As a result of studying the academic discipline, the student should know basic concepts and methods of decision-making theory, utility theory, expert procedures in conditions of certainty, risk, uncertainty, conflict, and vagueness of data; expert information processing methods and voting methods; cooperative decision-making methods; be able to: build and research decision-making models based on decision-making theory, utility theory, expert procedures in conditions of certainty, risk, uncertainty, in conditions of conflict and vagueness of data.
Recommended or required reading and other learning resources/tools
1. Voloshyn O.F., Mashchenko S.O. Modeli ta metody pryiniattia rishen. Pidruchnyk. - Kyiv: VPTs „Kyivskyi universytet”, 2010. - 336 p. 2. Voloshyn O.F., Mashchenko S.O. Teoriia pryiniattia rishen. Navchalnyi posibnyk. - Kyiv: VPTs „Kyivskyi universytet”, 2006. - 304 p. 3. Voloshyn O.F., Mashchenko S.O. Metodychni rekomendatsii do vykonannia praktychnykh i laboratornykh robit z teorii pryiniattia rishen. - Kyiv: VPTs „Kyivskyi universytet”, 2001. - 46 p. 4. Diakon V.M., Kovalov L.Ye. Modeli i metody teorii pryiniattia rishen. Pidruchnyk. – Kyiv: ANF HRUP, 2013. – 603 p. 5. Skott Dzh. Konflikti. Kiev: Vneshnetorgizdat, 1991. - 190 p. 6. Volkovich V.L., Voloshin A.F. i dr. Modeli i metodi optimizatsii slozhnikh sistem.- Kiev, Naukova dumka,1993. - 312 p. 7. Voloshyn O.F., Panchenko M.V. Ekspertna systema yakisnoho otsiniuvannia na osnovi bahato parametrychnykh zalezhnostei // «Problemy matematychnykh mashyn i system», 2002, №2. - pp.83-89.
Planned learning activities and teaching methods
Lectures, practical classes, independent work.
Assessment methods and criteria
Semester assessment: Test paper 1 18/10 points. Control work 2 18/10 points. Test 18/10 points. Abstract 6/4 points. Final evaluation in the form of credit: the maximum number of points that can be received by a student: 40 points; form of conduct and types of tasks: written work. Types of tasks: 4 tasks. A student receives an overall positive grade in the discipline if his grade for the exam is at least 24 points. A student is admitted to the exam if during the semester he scored at least 36 points in total; completed and on time submitted at least 2 (two) independent works from the list of proposed works.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Olexii F. Voloshyn
Complex systems modelling
Faculty of Computer Science and Cybernetics
Maryna V. Korobova
Complex systems modelling
Faculty of Computer Science and Cybernetics