computational game theory amos fiat modified slides prepared for yishay mansour’s class
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Computational Game Theory Amos Fiat Modified Slides prepared for Yishay Mansour’s class. Lecture 1 - Introduction. Agenda. Introduction to Game Theory Examples Matrix form Games Utility Solution concepts Dominant Strategies Nash Equilibria Complexity - PowerPoint PPT PresentationTRANSCRIPT
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Computational Game TheoryAmos Fiat
Modified Slides prepared for Yishay Mansour’s class
Lecture 1 - Introduction
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Agenda Introduction to Game Theory Examples Matrix form Games Utility Solution concepts
Dominant Strategies Nash Equilibria
Complexity Mechanism Design: reverse game theory
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The study of Game Theory in the context of Computer Science, in order to reason about problems from the perspective of computability and algorithm design.
Computational Game Theory
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Computing involves many different selfish entities. Thus involves game theory.
The Internet, Intranet, etc.◦ Many players (end-users, ISVs, Infrastructure
Providers)◦ Players wish to maximize their own benefit and
act accordingly◦ The trick is to design a system where it’s
beneficial for the player to follow the rules
CGT in Computer Science
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Theory◦ Algorithm design◦ Complexity◦ Quality of game states (Equilibrium states in
particular)◦ Study of dynamics
Industry◦ Sponsored search ◦ Other auctions
CGT in Computer Science
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Rational Player◦ Prioritizes possible actions according to utility or
cost◦ Strives to maximize utility or to minimize cost
Competitive Environment◦ More than one player at the same time
Game Theory analyzes how rational players behave in competitive
environments
Game Theory
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Matrix representation of the game
The Prisoner’s Dilema
Thieves honor
Defect
Thieves honor
3,3 6,2
Defect 2,6 5,5
Row Player Column Player
2 < 3
5 < 6
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It is a dominant strategy to confess A dominant strategy is a “solution concept”
The Prisoner’s Dilema
Thieves honor
Defect
Thieves honor
3,3 6,2
Defect 2,6 5,5
6,10
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Internet Service Providers (ISP) often share their physical networks for free
In some cases an ISP can either choose to route traffic in its own network or via a partner network
ISP Routing
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ISP 1 needs to route traffic from s1 to t1
ISP 2 needs to route traffic from s2 to t2
The cost of routing along each edge is one
ISP Routing
A B
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ISP1 routes via B:◦Cost for ISP1: 1◦Cost for ISP2: 4
ISP Routing
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Cost matrix for the game:
ISP Routing
A B
A 3,3 6,2
B 2,6 5,5ISP 1
ISP 2
B,A: s1 to t1B,A: s2 to t2
Prisoners Dilemma Again
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The game consists of only one ‘turn’
All the players play simultaneously and are unaware of what the other players do
Players are selfish, seek to maximize their own benefit
Strategic Games
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N = {1,…,n} players Player i has actions We will say “action” or “strategy” The space of all possible action vectors is
A joint action is the vector a∈A Player i has a utility function If utility is negative we may call it cost
Strategic Games – Formal Model
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A strategic game:
Strategic Games – Formal Model
Players
Actions of each player
Utility of each player
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Action ai of player i is a weakly dominant strategy if:
Dominant Strategies
Action ai of player i is a strongly dominant strategy if:
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An outcome a of a game is Pareto optimal if for every other outcome b, some player will lose by changing to b
Pareto Optimality
Vilfredo Pareto
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St. Petersburg Paradox:◦ Toss a coin until tails, I pay
you
◦ What will you pay me to play?
Bernulli Utility
“Utility of Money”, “Bernulli Utility”
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Completeness:Transitivity:Continuity:
Independence:
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Von Neumann–Morgenstern Rationality Axioms (1944)Preferences over lotteries
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Rationality Axioms
Utility function overlotteries, real valued,
expected utility maximization
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Gamble A: 100% € 1,000,000Gamble B: 10% € 5,000,000
89% € 1,000,000 1% Nothing
Gamble C: 11% € 1,000,000 89% NothingGamble D: 10% € 5,000,000 90% Nothing
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Allias Paradox (1953)
Gamble A or B?
Gamble C or D?
Experimental ”Fact”:
Experimental “Fact”:
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Gamble A: 100% € 1,000,000Gamble B: 10% € 5,000,000
89% € 1,000,000 1% Nothing
Gamble C: 11% € 1,000,000 89% NothingGamble D: 10% € 5,000,000 90% Nothing
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Allias Paradox
“Fact”:
“Fact”:
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Expected Utility Theory
VNM Axioms Expected Utility MaximizationMixed Nash Equilibrium exists
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Assume there’s a shared resource (network bandwidth) and N players.
Each player “uses” the common resource, by choosing Xi from [0,1].
If
Otherwise,
Tragedy of the commons
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Tragedy of the commons
Given that the otherplayers are fixed, whatIs the best response?
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Tragedy of the commons
This is an equilibriumNo player can improve
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Tragedy of the commons
The case for Privatization or central control of commons
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A Nash Equilibrium is an outcome of the game in which no player can improve its utility alone:
Alternative definition: every player’s action is a best response:
Nash Equilibrium
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The payoff matrix:
Battle of the Sexes
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The payoff matrix:
Battle of the Sexes
Row player has no incentive to
move up
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The payoff matrix:
Battle of the Sexes
Column player has no
incentive to move left
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The payoff matrix:
Battle of the Sexes
So this is an Equilibrium state
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The payoff matrix:
Battle of the Sexes
Same thing here
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2 players need to send a packet from point O to the network.
They can send it via A (costs 1) or B (costs 2)
Routing Game
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The cost matrix:
Routing Game
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The cost matrix:
Routing Game
Equilibrium states
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2 players, each chooses Head or Tail Row player wins if they match the column
player wins if they don’t Utility matrix:
Matching Pennies
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2 players, each chooses Head or Tail Row player wins if they match the column
player wins if they don’t Utility matrix:
Matching Pennies
Row player is fine, but Column player wants to move left
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2 players, each chooses Head or Tail Row player wins if they match the column
player wins if they don’t Utility matrix:
Matching Pennies
Column player is fine, but Row player wants to move up
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2 players, each chooses Head or Tail Row player wins if they match the column
player wins if they don’t Utility matrix:
Matching Pennies
Row player is fine, but Column player
wants to move right
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2 players, each chooses Head or Tail Row player wins if they match the column
player wins if they don’t Utility matrix:
Matching Pennies
Column player is fine, but Row player wants
to move down
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2 players, each chooses Head or Tail Row player wins if they match the column
player wins if they don’t Utility matrix:
No equilibrium state!
Matching Pennies
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Players do not choose a pure strategy (one specific strategy)
Players choose a distribution over their possible pure strategies
For example: with probability p choose Heads, and with probability 1-p choose Tails
Mixed Strategies
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Row player chooses Heads with probability p and Tails with probability 1-p
Column player chooses Heads with probability q and Tails with probability 1-q
Row plays Heads: Row plays Tails:
Matching Pennies
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Each player selects where is the set of all possible distributions over Ai
An outcome of the game is the Joint Mixed Strategy
An outcome of the game is a Mixed Nash Equilibrium if for every player
Mixed Strategy
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2nd definition of Mixed Nash Equilibrium:
Definition:
Definition:
Property of Mixed Nash Equilibrium:
Mixed Strategy
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No pure strategy Nash Equilibrium, only Mixed Nash Equilibrium, for mixed strategy (1/3, 1/3, 1/3) .
Rock Paper Scissors
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N ice cream vendors are spread on the beach
Assume that the beach is the line [0,1] Each vendor chooses a location Xi, which
affects its utility (sales volume). The utility for player i :
X0 = 0, Xn+1 = 1
Location Game
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For N=2 we have a pure Nash Equilibrium:
No player wants to move since it will lose space
For N=3 no pure Nash Equilibrium:
The player in the middle always wants to move to improve its utility
Location Game
0 11/2
0 11/2
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If instead of a line we will assume a circle, we will always have a pure Nash Equilibrium where every player is evenly distanced from each other:
Location Game
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N companies are producing the same product
Company I needs to choose its production volume, xi ≥ 0
The price is determined based on the overall production volume,
Each company has a production cost: The utility of company i is:
Cournot Competition
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Case 1: Linear price, no production cost
◦ Utility:
◦ Pure Nash Equilibrium is reached at:
Cournot Competition
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Case 2: Harmonic price, no production cost
◦ Company i’s utility:
◦ Companies have incentive to produce as much as they can – no pure or mixed Nash Equilibrium
Cournot Competition
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n players wants to buy a single item which is on sale
Each player has a valuation for the product, Assume WLOG that Each player submits its bid, , all players
submit simultaneously.
Auction
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Case 1: First price auction◦ The player with the highest bid wins◦ The price equals the bid◦ 1st Equilibrium is:
The first player needs to know the valuation of the second player – not practical
◦ 2nd Equilibrium is:
Auction
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Case 2: Second price auction: Vickrey Auction◦ The player with the highest bid wins◦ The price equals the second highest bid
No incentive to bid higher than one’s valuation - a player’s utility when it bids its valuation is at least as high than when it bids any other value
This mechanism encourages players to bid truthfully Mechanism Design: reverse game theory –
set up a game so that the equilibria has a desired property
Auction
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Equilibrium Concepts
pureNash
mixed Nash
correlated eq
no regret
best-responsedynamics
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Traffic Flow: the Mathematical Model a directed graph G = (V,E) k source-destination pairs (s1 ,t1), …,
(sk ,tk) a rate (amount) ri of traffic from si to ti
for each edge e, a cost function ce(•)◦ assumed nonnegative, continuous,
nondecreasing
s1 t1
c(x)=x Flow = ½
Flow = ½c(x)=1
Example: (k,r=1)
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Routings of Traffic
Traffic and Flows: fP = amount of traffic routed on si-ti path P flow vector f routing of traffic
Selfish routing: what are the equilibria?
s t
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Nash Flows
Some assumptions: agents small relative to network (nonatomic
game) want to minimize cost of their path
Def: A flow is at Nash equilibrium (or is a Nash flow) if all flow is routed on min-cost paths [given current edge congestion]
xs t
1Flow = .5
Flow = .5
s t1
Flow = 0
Flow = 1x
Example:
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History + Generalizations
model, defn of Nash flows by [Wardrop 52]
Nash flows exist, are (essentially) unique◦ due to [Beckmann et al. 56]◦ general nonatomic games: [Schmeidler 73]
congestion game (payoffs fn of # of players)◦ defined for atomic games by [Rosenthal 73]◦ previous focus: Nash eq in pure strategies exist
potential game (equilibria as optima)◦ defined by [Monderer/Shapley 96]
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The Cost of a Flow
Def: the cost C(f) of flow f = sum of all costs incurred by traffic (avg cost × traffic rate)
s t
x
1
½
½
Cost = ½•½ +½•1 = ¾
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The Cost of a Flow
Def: the cost C(f) of flow f = sum of all costs incurred by traffic (avg cost × traffic rate)
Formally: if cP(f) = sum of costs of edges of P (w.r.t. the flow f), then:
C(f) = P fP • cP(f)
s ts t
x
1
½
½
Cost = ½•½ +½•1 = ¾
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64
Inefficiency of Nash FlowsNote: Nash flows do not minimize the cost observed informally by [Pigou 1920]
Cost of Nash flow = 1•1 + 0•1 = 1 Cost of optimal (min-cost) flow = ½•½ +½•1 = ¾ Price of anarchy := Nash/OPT ratio = 4/3
s t
x
10
1 ½
½
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65
Braess’s Paradox
Initial Network:
s tx 1
½
x1½
½
½
cost = 1.5
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66
Braess’s Paradox
Initial Network: Augmented Network:
s tx 1
½
x1½
½
½
cost = 1.5
s tx 1
½
x1½
½
½0
Now what?
![Page 67: Computational Game Theory Amos Fiat Modified Slides prepared for Yishay Mansour’s class](https://reader036.vdocuments.net/reader036/viewer/2022081603/56813ac5550346895da2d6c8/html5/thumbnails/67.jpg)
67
Braess’s Paradox
Initial Network: Augmented Network:
s tx 1
½
x1½
½
½
cost = 1.5 cost = 2
s t
x 1
x10
![Page 68: Computational Game Theory Amos Fiat Modified Slides prepared for Yishay Mansour’s class](https://reader036.vdocuments.net/reader036/viewer/2022081603/56813ac5550346895da2d6c8/html5/thumbnails/68.jpg)
68
Braess’s Paradox
Initial Network: Augmented Network:
All traffic incurs more cost! [Braess 68]
see also [Cohen/Horowitz 91], [Roughgarden 01]
s tx 1
½
x1½
½
½
cost = 1.5 cost = 2
s t
x 1
x10