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Lecture 1: Introduction
A game
Rules:
1 Players: All of you:
2 Actions: Choose a number between 0 and 1003 Outcome: The player with the number closest to half the average of all
submitted numbers wins.4 Payoffs: He will receive CHF, which I will pay out
right after the game.5 In case of several winners, divide payment by number of winners and
pay all winners.
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Lecture 1: Introduction
A game
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Lecture 1: Introduction
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Soziologisches Institut
“…It is not a case of choosing those [faces] that, to the best of one’s judgment, are really the prettiest, nor even those that average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, fifth and higher degrees.” (John Maynard Keynes, General Theory of Employment, Interest, and Money, 1936, p.156).
Analogy between stock markets and newspaper contest in which people guess what faces others will guess are most beautiful.
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Lecture 1: Introduction
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Lecture 1: Introduction
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Soziologisches Institut
Diekmann, Andreas. "Rational choice, evolution and the “Beauty Contest”." Raymond Boudon. A Life in Sociology. Oxford: Bardwell (2009), p,.8 ff.
K=0K=1K=2K=3
Beliefs and learning
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Lecture 1: Introduction
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Level 0 (“no reasoning”)random guess or simple rules
Level 1 reacts to base strategy at level 0Guesses of 50 = 33Level 2 reacts to level 1Guesses of of 50 = 22Level k reacts to level k-1
Guesses ( ) k 0…
: Cognitive Hierarchy Theory
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Soziologisches Institut
Diekmann, Andreas. "Rational choice, evolution and the “Beauty Contest”." Raymond Boudon. A Life in Sociology. Oxford: Bardwell (2009), p,.8 ff.
K=0K=1K=2K=3
Beliefs and learning
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Soziologisches Institut
Diekmann, Andreas. "Rational choice, evolution and the “Beauty Contest”." Raymond Boudon. A Life in Sociology. Oxford: Bardwell (2009), p,.8 ff.
K=0K=1K=2K=3
Beliefs and learning
![Page 14: INTRODUCTION TO GAME THEORY · 2019-02-25 · Raymond Boudon. A Life in Sociology. Oxford: Bardwell(2009), p,.8 ff. K=1 K=0 K=2 K=3 Beliefs and learning. Lecture 1: Introduction 8IBU](https://reader030.vdocuments.mx/reader030/viewer/2022040821/5e6ac7a7874fb76e6d5d2525/html5/thumbnails/14.jpg)
Soziologisches Institut
Diekmann, Andreas. "Rational choice, evolution and the “Beauty Contest”." Raymond Boudon. A Life in Sociology. Oxford: Bardwell (2009), p,.8 ff.
K=0K=1K=2K=3
Beliefs and learning
![Page 15: INTRODUCTION TO GAME THEORY · 2019-02-25 · Raymond Boudon. A Life in Sociology. Oxford: Bardwell(2009), p,.8 ff. K=1 K=0 K=2 K=3 Beliefs and learning. Lecture 1: Introduction 8IBU](https://reader030.vdocuments.mx/reader030/viewer/2022040821/5e6ac7a7874fb76e6d5d2525/html5/thumbnails/15.jpg)
Soziologisches Institut
Diekmann, Andreas. "Rational choice, evolution and the “Beauty Contest”." Raymond Boudon. A Life in Sociology. Oxford: Bardwell (2009), p,.8 ff.
K=0K=1K=2K=3
Beliefs and learning
![Page 16: INTRODUCTION TO GAME THEORY · 2019-02-25 · Raymond Boudon. A Life in Sociology. Oxford: Bardwell(2009), p,.8 ff. K=1 K=0 K=2 K=3 Beliefs and learning. Lecture 1: Introduction 8IBU](https://reader030.vdocuments.mx/reader030/viewer/2022040821/5e6ac7a7874fb76e6d5d2525/html5/thumbnails/16.jpg)
Soziologisches Institut
Bosch-Domènech et al. (2002, AEA)
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Soziologisches Institut
Bosch-Domènech et al. (2002, AEA)
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Lecture 1: Introduction
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Lecture 1: Introduction
Acknowledgments
Bary Pradelski (ETHZ)
Peyton Young (Oxford, LSE)
Bernhard von Stengel (LSE)
Francoise Forges (Paris Dauphine)
Paul Duetting (LSE)
Jeff Shamma (Georgia Tech, KAUST)
Joergen Weibull (Stockholm, TSE)
Andreas Diekmann (ETHZ)
Dirk Helbing (ETHZ)
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Lecture 1: Introduction
Game theory
A tour of its people, applications and concepts
1 von Neumann
2 Nash
3 Aumann, Schelling, Selten, Shapley
4 Today
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Lecture 1: Introduction
John von Neumann (1903-1957)7 / 47
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Lecture 1: Introduction
What is game theory?
A mathematical language to express models of, as Myerson says:
“conflict and cooperation between intelligent rational decision-makers”
In other words, interactive decision theory (Aumann)
Dates back to von Neumann & Morgenstern (1944)
Most important solution concept: the Nash (1950) equilibrium
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Lecture 1: Introduction
Games and Non-Games
What is a game? And what is not a game?
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Lecture 1: Introduction
Uses of game theory
Prescriptive agenda versus descriptive agenda
“Reverse game theory”/mechanism design:
“in a design problem, the goal function is the main given, while the
mechanism is the unknown.” (Hurwicz)
The mechanism designer is a game designer. He studies
What agents would do in various games
And what game leads to the outcomes that are most desirable
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Lecture 1: Introduction
Game theory revolutionized several disciplines
Biology (evolution, conflict, etc.)
Social sciences (economics, sociology, political science, etc.)
Computer science (algorithms, control, etc.)
game theory is now applied widely (e.g. regulation, online auctions,
distributed control, medical research, etc.)
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Lecture 1: Introduction
Its impact in economics (evaluated by Nobel prizes)
1972: Ken Arrow − general equilibrium
1994: John Nash, Reinhard Selten, John Harsanyi − solution concepts
2005: Tom Schelling and Robert Aumann − evolutionary game theory
and common knowledge
2007: Leonid Hurwicz, Eric Maskin, Roger Myerson − mechanism
design
2009: Lin Ostrom − economic governance, the commons
2012: Al Roth and Lloyd Shapley − market design
2014: Jean Tirole − markets and regulation
2016: Oliver Hart and Bengt Holmström − contract theory
2017: Richard Thaler − limited rationality, social preferences
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Lecture 1: Introduction
Part 1: game theory
“Introduction” / Tour of game theory
Non-cooperative game theory
No binding contracts can be
written
Players are individuals
Main solution concepts:
Nash equilibrium
Strong equilibrium
Cooperative game theory
Binding contract can be written
Players are individuals and
coalitions of individuals
Main solution concepts:
Core
Shapley value
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Lecture 1: Introduction
Noncooperative game theory
John Nash (1928-2015)
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Lecture 1: Introduction
A noncooperative game (normal-form)
players: N = {1, 2, . . . , n} (finite)
actions/strategies: (each player chooses si from his own finite strategyset; Si for each i ∈ N)
resulting strategy combination: s = (s1, . . . , sn) ∈ (Si)i∈N
payoffs: ui = ui(s)payoffs resulting from the outcome of the game determined by s
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Lecture 1: Introduction
Some 2-player examples
Prisoner’s dilemma − social dilemma, tragedy of the commons,free-riding
Conflict between individual and collective incentives
Harmony − aligned incentives
No conflict between individual and collective incentives
Battle of the Sexes − coordination
Conflict and alignment of individual and collective incentives
Hawk dove/Snowdrift − anti-coordination
Conflict and alignment of individual and collective incentives
Matching pennies − zero-sum, rock-paper-scissor
Conflict of individual incentives
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Lecture 1: Introduction
Player 2Heads Tails
Player 1Heads 1,-1 -1,1Tails -1,1 1,-1
Matching pennies
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Lecture 1: Introduction
Confess Stay quietA A
Confess-6 -10
B -6 0
Stay quiet0 -2
B -10 -2
Prisoner’s dilemma
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Lecture 1: Introduction
WOMANBoxing Shopping
MANBoxing 2,1 0,0
Shopping 0,0 1,2
Battle of the sexes
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Lecture 1: Introduction
Player 2Hawk Dove
Player 1Hawk -2,-2 4,0Dove 0,4 2,2
Hawk-Dove game
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Lecture 1: Introduction
Company BCooperate Not Cooperate
Company ACooperate 9,9 4,7
Not Cooperate 7,4 3,3
Harmony game
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Lecture 1: Introduction
Equilibrium
Equilibrium/solution concept:
An equilibrium/solution is a rule that maps the structure of a game into
an equilibrium set of strategies s∗.
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Lecture 1: Introduction
Nash Equilibrium
Definition: Best-response
Player i’s best-response (or, reply) to the strategies s−i played by all
others is the strategy s∗i ∈ Si such that
ui(s∗i , s−i) � ui(s′i, s−i) ∀s′i ∈ Si and s′i �= s∗i
Definition: (Pure-strategy) Nash equilibrium
All strategies are mutual best responses:
ui(s∗i , s−i) � ui(s′i, s−i) ∀s′i ∈ Si and s′i �= s∗i
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Lecture 1: Introduction
Confess Stay quietA A
Confess-6 -10
B -6 0
Stay quiet0 -2
B -10 -2
Prisoner’s dilemma: both players confess (defect)
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Lecture 1: Introduction
WOMANBoxing Shopping
MANBoxing 2,1 0,0
Shopping 0,0 1,2
Battle of the sexes: coordinate on either option
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Lecture 1: Introduction
Player 2Heads Tails
Player 1Heads 1,-1 -1,1Tails -1,1 1,-1
Matching pennies: none (in pure strategies)
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Lecture 1: Introduction
Player 2Hawk Dove
Player 1Hawk -2,-2 4,0Dove 0,4 2,2
Hawk-dove: either of the two hawk-dove outcomes
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Lecture 1: Introduction
Company BCooperate Not Cooperate
Company ACooperate 9,9 4,7
Not Cooperate 7,4 3,3
Harmony: both cooperate
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Lecture 1: Introduction
Pure-strategy N.E. for our 2-player examples
Prisoner’s dilemma − social dilemma
Unique NE − socially undesirable outcome
Harmony − aligned incentives
Unique NE − socially desirable outcome
Battle of the Sexes − coordination
Two NE − both Pareto-optimal
Hawk dove/Snowdrift − anti-coordination
Two NE − Pareto-optimal, but perhaps Dove-Dove “better”
Matching pennies − zero-sum, rock-paper-scissor
No (pure-strategy) NE
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Lecture 1: Introduction
How about our initial game
Remember the rules were:
1 Choose a number between 0 and 100
2 The player with the number closest to half the average of all submitted
numbers wins
What is the Nash Equilibrium?
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Lecture 1: Introduction
0
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Lecture 1: Introduction
Braess’ Paradox
New road worsens congestion!
The story:
60 people travel from S to D
Initially, there is no middle road.
The NE is such that 30 people
travel one way, the others the
other way, and each driver drives
90 mins.
A middle road is build. This
road is super efficient. Now
everyone will use it and drive
the same route, and the NE will
worsen to 119/120 mins.
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Lecture 1: Introduction
ourse admin
Information about the course, and materials/slides of speakers, will be
made available at
Also, please contact directly if you have any questions about thecourse:
Heinrich: [email protected]
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Lecture 1: Introduction
Schedule (preliminary) I1) 19.02. Introduction: a quick tour of game theory Heinrich Nax
2) 26.02. Cooperative game theory Heinrich Nax
•Core and Shapley value
•Matching markets
3) 05.03. Non-cooperative game theory: Normal form Bary Pradelski
•Utilities
•Best replies
4) 12.03. The Nash equilibrium Bary Pradelski
•Proof
•Interpretations and refinements
5) 19.03. Non-cooperative game theory: dynamics Bary Pradelski
•Sub-game perfection and Bayes-Nash equilibrium
•Repeated games
PROBLEM SET 1
6) 26.03. Game theory: evolution Bary Pradelski
•Evolutionary game theory
•Algorithms in computer science (Price of anarchy)
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Lecture 1: Introduction
Finally, let’s play again!
You remember the game:
1 Choose a number between 0 and 100
2 The player with the number that is closest to half the average
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Lecture 1: Introduction
THANKS EVERYBODY
See you next week!
and keep checking the website for new materials as we progress:
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