Bachelor
2023/2024
Advanced Chapters in Decision Theory».
Type:
Elective course (Applied Mathematics and Information Science)
Area of studies:
Applied Mathematics and Information Science
Delivered by:
Department of Mathematics
Where:
Faculty of Computer Science
When:
4 year, 3 module
Mode of studies:
offline
Open to:
students of one campus
Language:
English
ECTS credits:
4
Contact hours:
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.