Бакалавриат
2023/2024
Избранные главы теории принятия решений
Статус:
Курс по выбору (Прикладная математика и информатика)
Направление:
01.03.02. Прикладная математика и информатика
Кто читает:
Департамент математики
Где читается:
Факультет компьютерных наук
Когда читается:
4-й курс, 3 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
4
Контактные часы:
46
Course Syllabus
Abstract
The course includes several advanced and newest topics in the theory of decision making:
• Decision Making under Deep Uncertainty,
• Data Envelopment Analysis,
• Stability and Similarity in Networks Based on Topology and Nodes Importance,
• Modified versions of Arrow’s Conditions and Axiomatization of Ranked-Choice Rules,
• Arrovian aggregation rules and non-manipulable rules on restricted domains. Single peaked preferences. Median voter theorem. Dictatorial domains.
Learning Objectives
- To familiarize studets with advanced models of Decision Making and their applications in real life problems
Expected Learning Outcomes
- Know the ways to measure uncertainty
- To know the main positive and negative results of the social choice literature: Black's theorem and Arrow's Impossibility Theorem
- Knows how to measure efficiency of units in DEA model
- knows different centrality measures, their advantages and shortages
Course Contents
- Decision Making under Deep Uncertainty
- Data Envelopment Analysis
- Stability and Similarity in Networks Based on Topology and Nodes Importance
- Modified versions of Arrow’s Conditions and Axiomatization of Ranked-Choice Rules
- Arrovian aggregation rules and non-manipulable rules on restricted domains. Single peaked preferences. Median voter theorem. Dictatorial domains
Bibliography
Recommended Core Bibliography
- Aleskerov, F., Meshcheryakova, N., & Shvydun, S. (2016). Centrality measures in networks based on nodes attributes, long-range interactions and group influence.
Recommended Additional Bibliography
- Aleskerov, F., Meshcheryakova, N., Nikitina, A., & Shvydun, S. (2018). Key Borrowers Detection by Long-Range Interactions.