EL3240 Games, Decisions and Information 7.0 credits
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This graduate-level course provides an introduction to cooperative and noncooperative game theory in both static and dynamic settings. The focus will be on the decisions of the players and the role of information in the decisions. The theory spans a wide range of applicaitons in engineering (distributed control, estimation and optimization of/over wireline and wireless communications networks, multi-agent systems), finance (robust portfolio optimization), economics (logistics, price mechanism design, auctions), and biology (systems biology).
Educational levelThird cycle
Academic level (A-D)D
At present this course is not scheduled to be offered.
To give students a basic understanding of game theoretical concepts and the role of information in decision making, and to show possibilities for the use of game theory in systems engineering and social sciences. By the end of the course the student should
- be able to define Nash and Stackelberg equilibrium.
- be able to solve matrix games, quadratic games, and understand the principles of solving general convex-concave games.
- be able to define Ky-Fan convexity and use it in nonconvex games.
- be familiar with solving cooperative and noncooperative games and team problems in simpler settings, such as linear quadratic settings.
- give examples where the role of information and signaling in games affect the costs of the players.
- explain the revelation principle in auction design and its consequences.
- be able to define dynamic games.
- be able to solve linear quadratic games of discrete-time dynamical systems.
- know the principles of solving dynamaic games using Hamilton-Jacobi-Bellman-Isaac´s equation.
- formulate relevant real life problems in a game theoretic framework.
Course main content
Static games, Nash equilibrium, generalized convexity notion in games, team decision theory, price mechanism design, network games, dynamic games, robust control, Hamilton-Jacobi-Bellman-Isaac´s equation, distributed communication and control, network games.
Lectures, exercises, homework, presentation of selected topic.
Basic probability theory and optimization, mathematical maturity.
Requirements for final grade
- Oral and written presentation of a selected topic.
- Oral presentations of homework problems.
- Weekly hand-in assignements.
Course plan valid from: Autumn 11.