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Description of the course:
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1st week: Introduction to game theory,
, strategic thinking and rational decision making
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2nd week:
Normal-form games
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3rd week:
Solution concepts ((Approximate) Nash equilibria, Correlated
equilibria)
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4th week:
Examples of games (symmetric bimatrix games)
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5th week:
Zero-sum games (Linear programming formulation, Minimax theorem)
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6th week:
Algorithms for computing solution concepts
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7th week:
Extensive games
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8th week:
Repeated games
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9th week:
Inefficiency of solution concepts (Price of Anarchy, Price of
Stability)
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10th week:
Selfish routing (Atomic/non atomic games, Wardrop equilibrium,
Braess paradox)
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11th week:
Mechanism design (First price auctions, Second price auctions)
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12th week:
Matching theory (Gale-Shapley algorithm)
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13th week:
Applications of game theory
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Assessment:
- 3 sets of exercises (E) (30%) (set1,
set2, set3)
- Midterm (M) (20%)
- 1 presentation/project (P) (Optional)
- Final exam (FE)
- Final grade = max(30%E+20%M+50%FE,
30%E+20%M+20%FE+30%P)
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Books:
- Algorithmic Game theory. Noam Nisan et al.
- A course in game theory. Martin J. Osborne
and Ariel Rubinstein.
- Game theory for applied economists. Robbert
Gibbons.
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Suggested papers:
C. Daskalakis, A. Mehta, and C. Papadimitriou. Progress in
approximate Nash equilibria.
S. Kontogiannis and P. Spirakis. Well supported approximate
equilibria in bimatrix games.
R. Lipton, E. Markakis, and A. Mehta. Playing large games using
simple strategies.
R. Rosenthal. A class of games possessing pure-strategy Nash
equilibria.
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Contact: x at ics.forth.gr, where x =
mfasoul |
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