Algorithmic game theory (NDMI098) - lecture

Time of the lecture: Thursday 10:40am, in the room S3.

Instructor: Martin Balko. E-mail: balko (AT) kam.mff.cuni.cz

Tutorials:
Information:
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  • 2/2, C+Ex, 5 E-Credits
  • Annotation: An introduction to algorithmic game theory, a relatively new field whose objective is to study formal models of strategic environments and to design effective algorithms for them. This introductory course covers basic concepts and methods that are illustrated with several practical applications. To successfully pass the course, it is recommended to know basics from complexity theory.
  • Exam:
    • The dates are in SIS. The exam is oral with written preparation, the total time is 3 hours.
  • Literature:
    • Noam Nisan, Tim Roughgarden, Éva Tardos, and Vijay V. Vazirani, editors. Algorithmic game theory. Cambridge University Press, Cambridge, 2007.
    • Tim Roughgarden. Twenty lectures on algorithmic game theory. Cambridge University Press, Cambridge, 2016.
    • Kevin Leyton-Brown and Yoav Shoham. Essentials of game theory, volume 3 of Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, Williston, VT, 2008.
    • Jiří Matoušek and Bernd Gärtner. Understanding and Using Linear Programming. Springer-Verlag New York, Inc., 2006.
  • Lecture notes: [PDF] (last update: 20.10.2024)
    • The lecture notes are still under construction. If you notice any mistake or place for improvement, please, let me know by e-mail.
Lectures:
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  • First lecture (4.10.2024):
    • Introduction, information about the course,
    • normal-form games, Nash equilibria (pure and mixed), examples of normal-form games,
    • Nash's theorem and preparation for its proof using Brouwer's fixed point theorem,
    • full presentation [PDF] and short presentation [PDF].
  • Second lecture (11.10.2024):
    • Proof of Nash's theorem,
    • Pareto optimality,
    • the Minimax theorem and remarks,
    • full presentation [PDF] and short presentation [PDF].
  • Third lecture (18.10.2024):
    • Recalling the Minimax theorem,
    • preliminaries from geometry and linear programming,
    • proof of the Minimax theorem based on the duality of linear programming,
    • best response condition,
    • brute-force algorithm to find all Nash equilibria,
    • full presentation [PDF] and short presentation [PDF].
  • Fourth lecture (25.10.2024):
    • Best response polyhedra,
    • best response polytopes,
    • Lemke–Howson algorithm and a proof of its correctness,
    • full presentation [PDF] and short presentation [PDF].
  • Fifth lecture (1.11.2024):
    • Complexity classes FNP and PPAD,
    • NASH being FNP-complete implies NP = coNP (without proof),
    • the END-OF-THE-LINE problem,
    • NASH is PPAD-complete (without proof),
    • epsilon-Nash equilibria,
    • quasi-polynomial time algorithm for finding epsilon-Nash equilibria (without proof),
    • correlated equilibria and their properties,
    • linear program for finding correlated equilibria,
    • full presentation [PDF] and short presentation [PDF].
  • Sixth lecture (8.11.2024):
    • Regret minimization, introduction of the formal model, external regret,
    • large comparison classes cannot yield good bound on external regret,
    • greedy algorithm and its cumulative loss,
    • Randomized greedy algorithm and its cumulative loss,
    • polynomial weights algorithm and its cumulative loss,
    • full presentation [PDF] and short presentation [PDF].
  • Seventh lecture (15.11.2024):
    • No-regret dynamics,
    • proof of the Minimax theorem using regret minimization,
    • coarse correlated equilibria,
    • convergence to coarse correlated equilibria with no-regret dynamics,
    • internal regret and swap regret,
    • full presentation [PDF] and short presentation [PDF].
  • Eighth lecture (22.11.2024):
    • To be added.
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