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Before choosing course

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). 

Course offering missing for current semester as well as for previous and coming semesters
* Retrieved from Course syllabus FEL3240 (Autumn 2011–)

Content and learning outcomes

Course contents

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.

Intended learning outcomes

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 Disposition

Lectures, exercises, homework, presentation of selected topic.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

Basic probability theory and optimization, mathematical maturity.


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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


    Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

    The examiner may apply another examination format when re-examining individual students.

    Other requirements for final grade

    • Oral and written presentation of a selected topic.
    • Oral presentations of homework problems.
    • Weekly hand-in assignements.

    Opportunity to complete the requirements via supplementary examination

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    Opportunity to raise an approved grade via renewed examination

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    Ethical approach

    • All members of a group are responsible for the group's work.
    • In any assessment, every student shall honestly disclose any help received and sources used.
    • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

    Further information

    Course web

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    Offered by

    EECS/Decision and Control Systems

    Main field of study

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    Education cycle

    Third cycle

    Add-on studies

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    Ather Gattami

    Postgraduate course

    Postgraduate courses at EECS/Decision and Control Systems