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Adapted from: Game Theoretic Approach in Computer Science CS3150, Fall 2002 Introduction to Game Theory Patchrawat Uthaisombut University of Pittsburgh Outline Example: Restaurant Game Formal Definition of Games Goal: Computing outcome of a game Examples: Computing game outcomes

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## Adapted from: Game Theoretic Approach in Computer Science CS3150, Fall 2002 Introduction to Game Theory

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**Adapted from:Game Theoretic Approach in Computer**ScienceCS3150, Fall 2002Introduction to Game Theory Patchrawat Uthaisombut University of Pittsburgh**Outline**• Example: Restaurant Game • Formal Definition of Games • Goal: Computing outcome of a game • Examples: Computing game outcomes • Mixed Strategies • Selfish Routing and Price of Anarchy**Wendy’s**or Dusty’s Wendy’s or Dusty’s Restaurant Game Malcolm Julia**A Play of the Restaurant Game**• The play • Row player chooses Dusty's. • Column player chooses Dusty's. • The Outcome • They meet at Dusty's • The Payoff • Row player gets 1. • Column player gets 2.**Outline**• Example: Restaurant Game • Formal Definition of Games • Goal: Computing outcome of a game • Examples: Computing game outcomes • Mixed Strategies • Selfish Routing and Price of Anarchy**Components of a Strategic Game**• Players • Who is involved? • Rules • Who moves when? • What does a player know when he/she moves? • What moves are available? • Outcomes • For each possible combination of actions by the players, what’s the outcome of the game. • Payoffs • What are the players’ preferences over the possible outcomes?**Key Assumptions**• Common knowledge • Everyone is aware of all player choices and payoff functions • Rationality of Players • Player will move to optimize individual payoff • All utility is expressed in the payoff function**Formal Definition of Strategic Game**• A strategic game is a 3-tuple (n,A,u) • The number of playersn. • For 1<i<n, a set Ai of actions available for player i. • For 1<i<n, apayoff functionui:A1…An R for player i.**Restaurant Game as a Strategic Game**• Players: n = 2 • Player 1 = Malcolm • Player 2 = Julia • Actions: • A1 = {Wendy's, Dusty's } • A2 = {Wendy's, Dusty's } • Payoffs: • u1(Wendy's,Wendy's ) = 2 • u1(Wendy's,Dusty's ) = 0 • u1(Dusty's,Wendy's ) = 0 • u1(Dusty's,Dusty's ) = 1 • u2(Wendy's,Wendy's ) = 1 • u2(Wendy's,Dusty's ) = 0 • u2(Dusty's,Wendy's ) = 0 • u2(Dusty's,Dusty's ) = 2**Outline**• Example: Restaurant Game • Formal Definition of Games • Goal: Computing outcome of a game • Examples: Computing game outcomes • Mixed Strategies • Selfish Routing and Price of Anarchy**Goal: Compute Outcome**• Given a game, compute what the outcome should be • Key assumption: Rationality of players • Ideas • Best response • Nash equilibrium • Dominant action or strategy • Dominated action or strategy**Notations**• x Ak • x is an action or a strategy of player k • Ak is a set of available actions for player k • (a) = (a1, a2,…, an) A1A2…An = A • a profile of actions; one action from each player • (a) = (X,G,H,L,S) • (a-k) = (a) \ ak A1…Ak-1Ak+1…An = A-k • actions of everybody except player k • (a-2) = (X,_,H,L,S) • (a-k,y) = (a-k) y • (a-2,M) = (X,M,H,L,S) • (a-k,ak) = (a)**Best Response Action**• An action x of player k is a best response to an action profile (a-k) if • uk(a-k,x) >uk(a-k,y) for all y in Ak.**Nash Equilibrium (local optimum)**• An action profile (a) is a Nash equilibrium if • for every player k, ak is a best response to (a-k) • that is, for every player k, uk(a-k,ak) >uk(a-k,y) for all y in Ak**Dominant Action or Strategy**• An action x of player k is a dominant action if • x is a best response to all (a-k) in A-k. • That is, uk(a-k,x) >uk(a-k,y) for all y in Ak and any action profile (a-k) in A-k. • That is, no matter what the other players do, x is a strategy for player k that is no worse than any other.**Two Cases**• Dominant actions dictate the resulting Nash Equilibrium • Dominant actions do not exist which means we need other methods**Strictly Dominated Actions**• An action x of player k is a never-best response or a strictly dominated action if • x is not a best response to any action profile (a-k) in A-k • That is, for any action profile (a-k) in A-k there exist an action y in Ak such that uk(a-k,x) <uk(a-k,y) • That is, no matter what the other players do, x is a strategy for player k that she should never use.**Iterated Elimination of Dominated Actions**• Procedure • Successively remove a strictly dominated action of a player from the game table until there are no more strictly dominated actions • Removing a dominated action • Reduce the size of the game • May make another action dominated • May make another action dominant • If there is only 1 outcome remaining, • the game is said to be dominant solvable. • that outcome is the unique Nash equilibrium of the game**Weakly Dominated Actions**• An action x of player k is a weakly dominated action if • for any action profile (a-k) in A-k there exists an action y in Ak such that uk(a-k,x) <uk(a-k,y) and • there exists an action profile (a-k) in A-k and an action y in Ak such that uk(a-k,x) < uk(a-k,y).**Iterated Elimination of Weakly Dominated Actions**• Procedure • Same as before except • Remove weakly dominated actions instead of strictly dominated actions • Undesirable properties • The remaining cells may depend on the order that the actions are removed. • May not yield all Nash equilibria.**Best-Response Function**• A set-valued function Bk • Bk(a-k) = {x Ak | x is a best response to (a-k) } • called the best-response function of player k. • An action profile (ai) is a Nash equilibrium if • ak Bk(a-k) for all players k. • An action x of player k is a dominant action if • x Bk(a-k)for all action profiles (a-k).**Exhaustive Method**• Begin with a game table. • We will incrementally cross out outcomes that are not Nash equilibria as follows: • For each player k = 1..n • For each profile (a-k) in A-k • Compute v = maxxAkuk(a-k, x) • Cross out all outcomes (a-k,x) such that uk(a-k, x) < v • The remaining outcomes are Nash equilibria.**Solution**74 52 37 68 55 34**Best-Response Table**Row player’s best-response table Column player’s best-response table**Outline**• Example: Restaurant Game • Formal Definition of Games • Goal: Computing outcome of a game • Examples: Computing game outcomes • Mixed Strategies • Selfish Routing and Price of Anarchy**The Prisoners’ Dilemma**• The confession of a suspect will be used against the other. • If both confess, get a reduced sentence. • If neither confesses, face only minimum charge.**Movie Game**• Two people go to a movie theatre.**Restaurant Game**• Malcolm and Julia go to a restaurant.**Concert Game**• Suppose both Malcolm and Julia are going to a concert instead of a dinner. • Both like Mozart better than Mahler.**Chicken Game**• Malcolm and Julia dare one another to drive their cars straight into one another.**Outline**• Example: Restaurant Game • Formal Definition of Games • Goal: Computing outcome of a game • Examples: Computing game outcomes • Mixed Strategies • Selfish Routing and Price of Anarchy**2002 US open Final match.**Serena is about to return the ball. She can either hit the ball down the line (DL) or crosscourt (CC) Venus must prepare to cover one side or the other Randomness in Payoff Functions**Mixed Strategies**• What is a mixed strategy? • Suppose Ak is the set of pure strategies for player k. • A mixed strategy for player k is a probability distribution over Ak. • An actual move is chosen randomly according to the probability distribution. • Example: • Ak = { DL, CC } • “DL 60%, CC 40%” is a mixed strategy for k.**Need for Mixed Strategies**• Multiple pure-strategy Nash equilibria • No pure-strategy Nash equilibria • Games where players prefer opposite outcomes • Matching Pennies • Chicken • Sports • Attack and Defense • Each player does very badly if her action is revealed to the other, because the other can respond accordingly. • Want to keep the other guessing. • Mixed strategy Nash equilibrium always exists.**Expectation**• Suppose X is a random variable. • Suppose X = 5 with probability 0.5 • Suppose X = 6 with probability 0.3 • Suppose X = 0 with probability 0.2 • Then E[X] = 5*0.5 + 6*0.3 + 0*0.2 • = 2.5 + 1.8 + 0 = 4.3 • In general, if X = vi with probability pi • Then E[X] = Σvipi**Mixed Strategies in the Chicken Games**• Mixing 2 pure strategies • Swerve with probability p and Straight with probability (1-p) • A continuous range of mixed strategies.**Finding Mixed Strategy Nash Equilibrium**• Compute Row’s payoffs as a function of q. • Find q that make Row’s payoffs indifferent no matter what pure strategy she chooses. • Plot Row’s best-response curve. • Do steps 1-3 for the Column player and p. • Plot Row’s and Column’s best-response curves together. • Points where the 2 curves meet are Nash equilibria.**Why it is an equilibrium?**• It is a Nash equilibrium because • Malcolm can’t change his strategy to do better and • Julia can’t change her strategy to do better • Why can’t Malcolm do better? • Julia chooses a mix such that it doesn’t matter what Malcolm does. • Why can’t Julia do better? • Malcolm chooses a mix such that it doesn’t matter what Julia does.**Exercise**• Find mixed strategy Nash equilibrium in the following game. • Tennis match**Outline**• Example: Restaurant Game • Formal Definition of Games • Goal: Computing outcome of a game • Examples: Computing game outcomes • Mixed Strategies • Selfish Routing and Price of Anarchy**L(x) = 1**s t L(x) = x Selfish Routing • Input • A directed graph G = (V,E) • Set of source-destination pairs {(si,ti)} where ri units of flow must be transmitted from si to ti • Each infinitesimal unit of flow is controlled by a selfish agent seeking to minimize its own latency. • Latency functions L on each edge e • Le(x) is latency of edge e given load x on e • Questions: • Identify the Nash Equilibria of the system • Price of Anarchy: How bad can the total latency of a Nash Equilibrium be compared to that of a socially optimal solution?**Simple Example 1**L(x) = 1 s t L(x) = x (s,t) demand is 1 unit What is optimal flow to minimize total latency? What is Nash equilibrium? Price of Anarchy in this example?**Simple Example 2**L(x) = 1 s t L(x) = xp for some integer p > 0 (s,t) demand is 1 unit What is optimal flow to minimize total latency? What is Nash equilibrium? Price of Anarchy in this example?**0**Braess’ Paradox L(x) = x v L(x) = 1 s t L(x) = x L(x) = 1 w (s,t) demand is 1 unit What is optimal flow to minimize total latency? What is Nash equilibrium? Price of Anarchy in this example?**Price of Anarchy**• Approximation Algorithms • Lack of unbounded computing power leads to loss of optimality • Online Algorithms • Lack of complete information leads to loss of optimality • Noncooperative Games • Lack of coordination leads to loss of optimality

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