Master 2020/2021

Games and Decisions in Data Analysis and Modelling

Type: Elective course (Data Science)

Area of studies: Applied Mathematics and Informatics

Delivered by: Department of Mathematics

Where: Faculty of Computer Science

When: 1 year, 3, 4 module

Mode of studies: distance learning

Instructors: Fuad T. Aleskerov, Lyudmila Egorova

Master’s programme: Data Science

Language: English

ECTS credits: 4

Contact hours: 64

Course Syllabus

Abstract

The aim of the course "Games and Decisions in Data Analysis and Modeling" is to familiarize students with modern models of game theory and decision theory, their applications in modeling and analyzing socio-economic problems, as well as their use in analytical and decision support systems. The course covers fundamental topics in decision theory: individual preferences modelling using binary relations and choice functions, social choice theory, especially the theory of local voting procedures and the theory of majority rule-based solutions. Also, students will consider the problems of decision-making in the network models of participants interaction and models of strategic behavior of players, taking into account the network structure of connections. Game theory studies the strategic interaction of rational agents and plays a central role in the economics, but it is also widely used in biology, political science, military affairs, etc. In this course, we will study non-cooperative and cooperative games, as well as the mechanism design, which is the reverse task, i.e. the development of rules for the interaction of agents that leads to the desired result. Successful completion of the course contributes to the development of useful strategic thinking in life.

Learning Objectives

• familiarize students with modern models of game theory and decision theory, their applications in modeling and analyzing socio-economic problems, as well as their use in analytical and decision support systems

Expected Learning Outcomes

• Knows the models of individual preferences
• Knows the main voting procedures and voting properties
• Knows the main centrality measures in networks
• Knows how to find Nash equilibtia in normal form games
• Knows the main solution concepts in cooperative games

Course Contents

• Preference modelling
  Classical utility theory, ordinal and cardinal models. Threshold utility. Binary relations of preferences. Rational choice, revelation of preferences. The problem of multi-criteria choice and ranking.
• Social choice theory
  Voting as a way of making decisions in group. Rationality in forming a collective decisions. Various voting procedures and their properties. Properties of voting procedures. The paradoxes of voting.
• Networks
  Network and graphs. Models of network formation. Networks as a way to model constraints on information exchange and interaction. Dissemination of information and influence in networks. Decision-making and strategic behavior of players in network interaction. Centrality indices. Applicaitions in different models.
• Basics of game theory
  Games of models of players interactions. Different solutions concepts. Nash equilibria in pure and mixed strategies.
• Mechanism design
  The aim of mechanism design is to develop the rules of games that lead to a desired result. Examples. The principle of detection. Auctions.
• Cooperative game theory
  Classic cooperative games: problem statement and basic concepts. Core and Shapley value. Limitation of cooperation: examples and methods of modeling. Games with restricted cooperation given in the form of a priori unions.
• Dynamics in games
  Games with consecutive moves. Extensive form of the games. Iterated games with observable actions. Markov strategies and Markov perfect equilibrium in iterative games.

Assessment Elements

• mid-term exam
  The mid-term is a written test in StartExam platform with asynchronous proctoring by Examus. The rules of the mid-term are available at https://elearning.hse.ru/en/student_steps/ The mi-term consists of several questions. In some of them students should provide a short answer, in others they have to do a matching or answer the multiple choice questions. Students are not allowed to use a mobile phone or any other devices and communicate with classmates and any other people during the mid-term.
• Home assignments
  Home assignments should be done by students individually
• Final examination
  The examination shall be held in writing (test) with the use of asynchronous proctoring on the StartExam platform. StartExam is an online platform for conducting test tasks of various levels of complexity. The link to pass the exam task will be available to the student in the RUZ. Asynchronous proctoring means that all the student's actions during the exam will be “watched” by the computer. The exam process is recorded and analyzed by artificial intelligence and a human (proctor). Please be careful and follow the instructions (https://elearning.hse.ru/en/student_steps/) clearly!

Interim Assessment

• Interim assessment (4 module)
  0.5 * Final examination + 0.2 * Home assignments + 0.3 * mid-term exam

Bibliography

Recommended Core Bibliography

• Fuad Aleskerov, Denis Bouyssou, & Bernard Monjardet. (2007). Utility Maximization, Choice and Preference. Post-Print. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.p.hal.journl.halshs.00197186
• Maschler,Michael, Solan,Eilon, & Zamir,Shmuel. (2013). Game Theory. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781107005488

Recommended Additional Bibliography

• Aleskerov, F., Meshcheryakova, N., & Shvydun, S. (2016). Centrality measures in networks based on nodes attributes, long-range interactions and group influence.