Name  Lecture  Office hours  Contact  
Instructor  Marcin Pęski  Monday, 1012pm, SS1085  Tuesday 9.1511.10 am (priority 9.1510.10 am), Max Gluskin 207  mpeski@gmail.com 
TA  David WalkerJones  Tutorial: Monday 121pm, SS1085  Thursday 45 pm, GE 313 
Date  Topic  Readings  Theory topics  Important games/Extras 
0601  Lecture 1. Games. Dominant strategies↓  1,2.12.5, 2.9 
Key problem of game theory.
Definition of a game. Examples. Strictly (weakly) dominated strategies (SD). Dominant strategies. 
Plurality voting↓. 
1301  Lecture 2. Iterated elimination and rationalizability↓  12.24* (see comment below) 
Rationality, knowledge of rationality.
Iterated elimination of strictly dominated strategies (IESD). Best responses. Best responses against beliefs. Relation between dominated strategies and never best responses. 

2001  Lecture 3. Nash equilibrium↓  2.62.8, 3.1 
Nash equilibrium. Relation between Nash equilibrium and SD or IESD.
Multiplicity of Nash equilibria. Equilibrium selection. 
Cournot duopoly.

2701  Lecture 4. Nash equilibrium – examples↓  3.2, 3.5 
Cournot oligopoly.
Bertrand duopoly.
Bertrand with differentiated products. 

0302  Lecture 5. Mixed strategies↓  4.14.5, 4.9  Mixed strategies. Nash equilibrium in mixed strategies. 
Penalty shot.
Traffic game. 
1002 
Midterm↓. Location: AH400 (the regular hours 1012pm; AH=Alumni Hall). 

2402  Lecture 6. Extensive form games. Subgame perfection↓  5.15.5, 6.16.2 
Extensiveform game. Strategies.
Nash equilibrium. Subgame Perfect equilibrium. 

203  Lecture 7. Extensive form games – examples↓  7.17.2, 7.67.7 
Ultimatum game.
Alternating offer bargaining. Holdup model. Entry game. 

903  Lecture 8. Repeated games↓  14.114.2, 14.414.6, 14.7.1., 14.10.1  Repeated games. 
Prisoner’s Dilemma followed by Coordinated Investment.
Finitely repeated Prisoner’s Dilemma. 
1603 
Lecture 9. Games with incomplete information↓,
Infinitely repeated games
Lecture notes: Infinitely repeated games
Lecture (voice):Infinitely repeated games, part II
Games with incomplete information I
Lecture notes:Games with incomplete information, part I
Lecture (voice):Games with incomplete information, part I

9.19.3  Inifnitely Repared games. Games with incomplete informarion. Bayesian Nash equilibrium  Infinitely repeated Prisoner’s Dilemma. 
2303 
Lecture 10. Games with incomplete information II↓,
Games with incomplete information II
Lecture notes:Games with incomplete information, part II
Lecture (voice):Games with incomplete information, part II
Games with incomplete information II
Lecture notes:Games with incomplete information, part III
Lecture (voice):Games with incomplete information, part III

9.49.5, 7.6 
Battle of Sexes with uncertain preferences.
Cournot oligopoly with uncertain costs. 

3003** 
Lecture 11. Auctions↓
Games with incomplete information II
Lecture notes:Auctions
Lecture (voice):Auctions

Auctions, 3.5, 9.6  
TBA  Final exam↓  First, secondprice and allpay auctions. 
Kicker \ Goalie  L  C  R 
L  0.6  0.9  0.9 
C  1  0.4  1 
R  0.9  0.9  0.6 
Pl.1\Pl.2  L  C  R 
U  4,5  1,2  3,0 
M  3,1  2,3  3,6 
D  0,4  3,3  4,3 
C  D  
C  ( − 1, − 1)  ( − 10, 0) 
D  (0, − 10)  ( − 5, − 5) 
Nice  Not nice  
Nice  (2, 2)  (0, − 2) 
Not nice  ( − 2, 0)  ( − 1, − 1) 
She\He  Opera  Stadium 
Opera  5,3  0,0 
Stadium  0,0  3,5 
Juliet\Romeo  Casino  Boxing Match 
Casino  1  3 
Boxing Match  1  1 
Juliet\Romeo  Casino  Boxing Match 
Casino  1  1 
Boxing Match  3  1 
Outcome if vote for c  Case  Outcome if vote for a 
A, (n’_{a} = n_{a} > n’_{b} = n_{b}, n’_{c} = n_{c} + 1)  n_{a} > n_{c} + 1, n_{b}  A (n’_{a} = n_{a} + 1 > n’_{b} = n_{b}, n’_{c} = n_{c}) 
AB  n_{a} = n_{b} > n_{c} + 1  A 
ABC  n_{a} = n_{b} = n_{c} + 1  A 
AC  n_{a} = n_{c} + 1 > n_{b}  A 
B  n_{b} > n_{a}, n_{c} + 1  AB or B 
BC  n_{b} = n_{c} + 1 > n_{a}  AB or B 
C  n_{c} + 1 > n_{a}, n_{b}  does not matter 
q_{2} = 0  q_{2} > 0, q_{2} = (1)/(2)(α − c − q_{1})  
q_{1} = 0  Strategies: q_{1} = 0, q_{2} = 0 Pl.1 EC (1)/(4)(α − c − q_{2})^{2} ≤ f⟹(1)/(4)(α − c)^{2} ≤ f Pl.2 EC (1)/(4)(α − c − q_{1})^{2} ≤ f⟹(1)/(4)(α − c)^{2} ≤ f  \strikeout off\uuline off\uwave off Strategies: q_{1} = 0, q_{2} = (1)/(2)(α − c) Pl.1 EC (1)/(4)(α − c − q_{2})^{2} ≤ f⟹(1)/(16)(α − c)^{2} ≤ f Pl.2 EC (1)/(4)(α − c − q_{1})^{2} ≥ f⟹(1)/(4)(α − c)^{2} ≥ f 
q_{1} > 0, q_{1} = (1)/(2)(α − c − q_{2})  Strategies: q_{1} = (1)/(2)(α − c), q_{2} = 0 Pl.1 EC (1)/(4)(α − c − q_{2})^{2} ≥ f⟹(1)/(4)(α − c)^{2} ≥ f Pl.2 EC (1)/(4)(α − c − q_{1})^{2} ≤ f⟹(1)/(16)(α − c)^{2} ≤ f  Strategies: q_{1} = (1)/(3)(α − c), q_{2} = (1)/(3)(α − c) Pl.1 EC (1)/(4)(α − c − q_{2})^{2} ≥ f⟹(1)/(9)(α − c)^{2} ≥ f Pl.2 EC (1)/(4)(α − c − q_{1})^{2} ≥ f⟹(1)/(9)(α − c)^{2} ≥ f 
Types of firm 1\types of firm 2  very high  not so high 
high  probability 1/2, conditional expected value = 875  probability 1/2, conditional expected value = 625 
low  probability 0,  probability 1, conditional expected value = 250 
Types of firm 2\types of firm 1  high  low 
very high  probability 1, conditional expected value = 875  probability 0, 
not so high  probability 1/3, conditional expected value = 625  probability 2/3, conditional expected value = 250 
Action of firm i\action of firm − i  bid_{ − i}  no bid_{ − i} 
bid_{i}  Q_{both}(ω − p_{both})  Q_{single}(ω − p_{single}) 
no bid_{i}  0  0 