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Algorithmic aspects of game theory

General data

Course ID: 1000-2M02AA
Erasmus code / ISCED: 11.303 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title: Algorithmic aspects of game theory
Name in Polish: Algorytmiczne aspekty teorii gier
Organizational unit: Faculty of Mathematics, Informatics, and Mechanics
Course groups: (in Polish) Grupa przedmiotów obieralnych dla informatyki magisterskiej- specjalność Automaty, logika, złożoność
(in Polish) Grupa przedmiotów obieralnych dla informatyki magisterskiej- specjalność Ekonomia algorytmiczna
(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka
Elective courses for Computer Science and Machine Learning
ECTS credit allocation (and other scores): 6.00 Basic information on ECTS credits allocation principles:
  • the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS;
  • the student’s weekly hourly workload is 45 h;
  • 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes;
  • weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS;
  • work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.

view allocation of credits
Language: English
Type of course:

elective monographs

Short description:

Game theory was initiated by von Neumann and Morgenstern as a mathematical theory of rational behaviour. A game comprises description of possible moves and payoffs for each of the players. Typically, each player searches for a strategy maximizing her payoff. The rational behaviour of players is well described by the concept of Nash equilibrium.

Full description:

Game theory was initiated by von Neumann and Morgenstern as a mathematical theory of rational behaviour. A game comprises description of possible moves and payoffs for each of the players. Typically, each player searches for a strategy maximizing her payoff. The rational behaviour of players is well described by the concept of Nash equilibrium.

Many real-life phenomena can be described by games. In computer science games can model,e.g., processes competing for shared resources, or a system interacting with environment. In economy, games model behaviour of market, and in sociology behaviour of social groups.

The course will present the basic concepts of game theory including the Nash equilibrium, games with perfect or imperfect information (like chess or bridge, respectively), zero-sum games, social choice theory (e.g., voting), and auctions.

We will give special attention to infinite games on graphs, which have been proposed for use in computer-aided verification, and are connected to automata theory. Computing the winning or optimal strategies in such games constitutes a challenging open problem in algorithmics.

Bibliography:

Guillermo Owen, Game theory, Emerald Group Publishing Limited; 3rd edition, 1995,

Philip D. Straffin, Game Theory and Strategy, The Mathematical Association of America, 1996,

E. Graedel, W. Thomas, and T. Wilke, Automata, Logics, and Infinite Games, Lecture Notes in Computer Science 2500, Springer 2002.

N.Nisan, T.Roughgarden, E.Tardos, V.Vazirani eds., Algorithmic Game Theory, Cambridge U.Press, 2007.

Learning outcomes:

Knowledge

Student

1. recognizes both extensive-form games and strategic games and understands the concepts of determinacy and equilibria proper to both cases,

2. learns the theorems about the determinacy of some relevant games (e.g. parity games), and the existence of a Nash equilibria, and understands the algorithmic consequences of these results,

3. learns algorithms applicable to extensive games, including infinite games and mean-payoff games; learns the problems related to computational complexity of such games,

4. learns algorithmic difficulty of finding the Nash equilibria, and the Lemke-Howson algorithm.

Skills

Student is able

1. to find a mathematical structure behind social games, and construct algorithms solving such games,

2. to adapt the learned techniques to solve infinite games with various winning conditions,

3. to model some informatics situation in terms of games,

4. to implement at least one method of finding the Nash equilibrium.

Competence

Student

1. understands the increasing role of games in modeling informatics scenarios, especially those related to distribution and interaction, like e.g. Internet and social networks,

Assessment methods and assessment criteria:

The exam consists of solving some number of problems within an indicated term (usually a few days).

In the case of completing the course by a doctoral student, the student will prepare a short report proposing some original research problems related to the course and relevant literature. A chat with a lecturer about the report will be a part of the exam.

Classes in period "Summer semester 2024/25" (past)

Time span: 2025-02-17 - 2025-06-08
Selected timetable range:
Go to timetable
Type of class:
Classes, 30 hours more information
Lecture, 30 hours more information
Coordinators: Damian Niwiński
Group instructors: Damian Niwiński, Marcin Przybyłko
Students list: (inaccessible to you)
Credit: Examination
Course descriptions are protected by copyright.
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