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CSC2556 Lecture 9 CSC2556 Lecture 9 Noncooperative Games 1: Nash Equilibria, Price of Anarchy, Cost-Sharing Games CSC2556 - Nisarg Shah 1 Game Theory • How do rational, self-interested agents act in a given environment? • Each agent has a set of possible actions • Rules of the game: ➢ Rewards for the agents as a function of the actions taken by all agents • Noncooperative games ➢ No external trusted agency, no legal agreements CSC2556 - Nisarg Shah 2 Normal Form Games • A set of players N = 1, … , 푛 • Each player 푖 has an action set 푆푖, chooses 푠푖 ∈ 푆푖 • 풮 = 푆1 × ⋯ × 푆푛. • Action profile 푠Ԧ = 푠1, … , 푠푛 ∈ 풮 • Each player 푖 has a utility function 푢푖: 풮 → ℝ ➢ Given the action profile 푠Ԧ = (푠1, … , 푠푛), each player 푖 gets a reward 푢푖 푠1, … , 푠푛 CSC2556 - Nisarg Shah 3 Normal Form Games Prisoner’s dilemma 푆 = {Silent,Betray} John’s Actions Stay Silent Betray Sam’s Actions Stay Silent (-1 , -1) (-3 , 0) Betray (0 , -3) (-2 , -2) 푢푆푎푚(퐵푒푡푟푎푦, 푆푖푙푒푛푡) 푢퐽표ℎ푛(퐵푒푡푟푎푦, 푆푖푙푒푛푡) 푠푆푎푚 푠퐽표ℎ푛 CSC2556 - Nisarg Shah 4 Player Strategies • Pure strategy ➢ Deterministic choice of an action, e.g., “Betray” • Mixed strategy ➢ Randomized choice of an action, e.g., “Betray with probability 0.3, and stay silent with probability 0.7” CSC2556 - Nisarg Shah 5 Dominant Strategies ′ • For player 푖, 푠푖 dominates 푠푖 if 푠푖 is “better than” ′ 푠푖 , irrespective of other players’ strategies. • Two variants: weak and strict domination ′ ➢ 푢푖 푠푖, 푠Ԧ−푖 ≥ 푢푖 푠푖 , 푠Ԧ−푖 , ∀푠Ԧ−푖 ➢ Strict inequality for some 푠Ԧ−푖 ← Weak domination ➢ Strict inequality for all 푠Ԧ−푖 ← Strict domination • 푠푖 is a strictly (or weakly) dominant strategy for player 푖 if it strictly (or weakly) dominates every other strategy CSC2556 - Nisarg Shah 6 Dominant Strategies • Q: How does this relate to strategyproofness? • A: Strategyproofness means “truth-telling should be a weakly dominant strategy for every player”. CSC2556 - Nisarg Shah 7 Example: Prisoner’s Dilemma • Recap: John’s Actions Stay Silent Betray Sam’s Actions Stay Silent (-1 , -1) (-3 , 0) Betray (0 , -3) (-2 , -2) • Each player strictly wants to ➢ Betray if the other player will stay silent ➢ Betray if the other player will betray • Betray = strictly dominant strategy for each player CSC2556 - Nisarg Shah 8 Iterated Elimination • What if there are no dominant strategies? ➢ No single strategy dominates every other strategy ➢ But some strategies might still be dominated • Assuming everyone knows everyone is rational… ➢ Can remove their dominated strategies ➢ Might reveal a newly dominant strategy • Eliminating only strictly dominated vs eliminating weakly dominated CSC2556 - Nisarg Shah 9 Iterated Elimination • Toy example: ➢ Microsoft vs Startup ➢ Enter the market or stay out? Startup Enter Stay Out Microsoft Enter (2 , -2) (4 , 0) Stay Out (0 , 4) (0 , 0) • Q: Is there a dominant strategy for startup? • Q: Do you see a rational outcome of the game? CSC2556 - Nisarg Shah 10 Iterated Elimination • “Guess 2/3 of average” ➢ Each student guesses a real number between 0 and 100 (inclusive) ➢ The student whose number is the closest to 2/3 of the average of all numbers wins! • Piazza Poll: What would you do? CSC2556 - Nisarg Shah 11 Nash Equilibrium • If we find dominant strategies, or a unique outcome after iteratively eliminating dominated strategies, it may be considered the rational outcome of the game. • What if this is not the case? Professor Attend Be Absent Students Attend (3 , 1) (-1 , -3) Be Absent (-1 , -1) (0 , 0) CSC2556 - Nisarg Shah 12 Nash Equilibrium • Instead of hoping to find strategies that players would play irrespective of what other players play, we want to find strategies that players would play given what other players play. • Nash Equilibrium ➢ A strategy profile 푠Ԧ is in Nash equilibrium if 푠푖 is the best action for player 푖 given that other players are playing 푠Ԧ−푖 ′ ′ 푢푖 푠푖, 푠Ԧ−푖 ≥ 푢푖 푠푖 , 푠Ԧ−푖 , ∀푠푖 CSC2556 - Nisarg Shah 13 Recap: Prisoner’s Dilemma John’s Actions Stay Silent Betray Sam’s Actions Stay Silent (-1 , -1) (-3 , 0) Betray (0 , -3) (-2 , -2) • Nash equilibrium? • (Dominant strategies) CSC2556 - Nisarg Shah 14 Recap: Microsoft vs Startup Startup Enter Stay Out Microsoft Enter (2 , -2) (4 , 0) Stay Out (0 , 4) (0 , 0) • Nash equilibrium? • (Iterated elimination of strongly dominated strategies) CSC2556 - Nisarg Shah 15 Recap: Attend or Not Professor Attend Be Absent Students Attend (3 , 1) (-1 , -3) Be Absent (-1 , -1) (0 , 0) • Nash equilibria? • Lack of predictability CSC2556 - Nisarg Shah 16 Example: Rock-Paper-Scissor P1 Rock Paper Scissor P2 Rock (0 , 0) (-1 , 1) (1 , -1) Paper (1 , -1) (0 , 0) (-1 , 1) Scissor (-1 , 1) (1 , -1) (0 , 0) • Pure Nash equilibrium? CSC2556 - Nisarg Shah 17 Nash’s Beautiful Result • Theorem: Every normal form game admits a mixed- strategy Nash equilibrium. • What about Rock-Paper-Scissor? P1 Rock Paper Scissor P2 Rock (0 , 0) (-1 , 1) (1 , -1) Paper (1 , -1) (0 , 0) (-1 , 1) Scissor (-1 , 1) (1 , -1) (0 , 0) CSC2556 - Nisarg Shah 18 Indifference Principle • If the mixed strategy of player 푖 in a Nash equilibrium has support 푇푖, the expected payoff of player 푖 from each 푠푖 ∈ 푇푖 must be identical. • Derivation of rock-paper-scissor on the board. CSC2556 - Nisarg Shah 19 Stag-Hunt Hunter 1 Stag Hare Hunter 2 Stag (4 , 4) (0 , 2) Hare (2 , 0) (1 , 1) • Game ➢ Stag requires both hunters, food is good for 4 days for each hunter. ➢ Hare requires a single hunter, food is good for 2 days ➢ If they both catch the same hare, they share. • Two pure Nash equilibria: (Stag,Stag), (Hare,Hare) CSC2556 - Nisarg Shah 20 Stag-Hunt Hunter 1 Stag Hare Hunter 2 Stag (4 , 4) (0 , 2) Hare (2 , 0) (1 , 1) • Two pure Nash equilibria: (Stag,Stag), (Hare,Hare) ➢ Other hunter plays “Stag” → “Stag” is best response ➢ Other hunter plays “Hare” → “Hare” is best reponse • What about mixed Nash equilibria? CSC2556 - Nisarg Shah 21 Stag-Hunt Hunter 1 Stag Hare Hunter 2 Stag (4 , 4) (0 , 2) Hare (2 , 0) (1 , 1) • Symmetric: 푠 → {Stag w.p. 푝, Hare w.p. 1 − 푝} • Indifference principle: ➢ Given the other hunter plays 푠, equal 피[reward] for Stag and Hare ➢ 피 Stag = 푝 ∗ 4 + 1 − 푝 ∗ 0 ➢ 피 Hare = 푝 ∗ 2 + 1 − 푝 ∗1 ➢ Equate the two ⇒ 푝 = 1/3 CSC2556 - Nisarg Shah 22 Extra Fun 1: Cunning Airlines • Two travelers lose their luggage. • Airline agrees to refund up to $100 to each. • Policy: Both travelers would submit a number between 2 and 99 (inclusive). ➢ If both report the same number, each gets this value. ➢ If one reports a lower number (푠) than the other (푡), the former gets 푠+2, the latter gets 푠-2. s t . 95 96 97 98 99 100 CSC2556 - Nisarg Shah 23 Extra Fun 2: Ice Cream Shop • Two brothers, each wants to set up an ice cream shop on the beach ([0,1]). • If the shops are at 푠, 푡 (with 푠 ≤ 푡) 푠+푡 푠+푡 ➢ The brother at 푠 gets 0, , the other gets , 1 2 2 0 s t 1 CSC2556 - Nisarg Shah 24 Nash Equilibria: Critique • Noncooperative game theory provides a framework for analyzing rational behavior. • But it relies on many assumptions that are often violated in the real world. • Due to this, human actors are observed to play Nash equilibria in some settings, but play something far different in other settings. CSC2556 - Nisarg Shah 25 Nash Equilibria: Critique • Assumptions: ➢ Rationality is common knowledge. o All players are rational. o All players know that all players are rational. o All players know that all players know that all players are rational. o … [Aumann, 1976] o Behavioral economics ➢ Rationality is perfect = “infinite wisdom” o Computationally bounded agents ➢ Full information about what other players are doing. o Bayes-Nash equilibria CSC2556 - Nisarg Shah 26 Nash Equilibria: Critique • Assumptions: ➢ No binding contracts. o Cooperative game theory ➢ No player can commit first. o Stackelberg games (will study this in a few lectures) ➢ No external help. o Correlated equilibria ➢ Humans reason about randomization using expectations. o Prospect theory CSC2556 - Nisarg Shah 27 Nash Equilibria: Critique • Also, there are often multiple equilibria, and no clear way of “choosing” one over another. • For many classes of games, finding a single equilibrium is provably hard. ➢ Cannot expect humans to find it if your computer cannot. CSC2556 - Nisarg Shah 28 Nash Equilibria: Critique • Conclusion: ➢ For human agents, take it with a grain of salt. ➢ For AI agents playing against AI agents, perfect! CSC2556 - Nisarg Shah 29 Price of Anarchy and Stability • If players play a Nash equilibrium instead of “socially optimum”, how bad can it be? • Objective function: sum of utilities/costs • Price of Anarchy (PoA): compare the optimum to the worst Nash equilibrium • Price of Stability (PoS): compare the optimum to the best Nash equilibrium CSC2556 - Nisarg Shah 30 Price of Anarchy and Stability • Price of Anarchy (PoA) Max social utility Min social utility in any NE Costs → flip: Nash equilibrium • Price of Stability (PoS) divided by optimum Max social utility Max social utility in any NE CSC2556 - Nisarg Shah 31 Revisiting Stag-Hunt Hunter 1 Stag Hare Hunter 2 Stag (4 , 4) (0 , 2) Hare (2 , 0) (1 , 1) • Optimum social utility = 4+4 = 8 • Three equilibria: ➢ (Stag, Stag) : Social utility = 8 ➢ (Hare, Hare) : Social utility = 2 ➢ (Stag:1/3 - Hare:2/3, Stag:1/3 - Hare:2/3) o Social utility = (1/3)*(1/3)*8 + (1-(1/3)*(1/3))*2 = Btw 2 and 8 • Price of stability? Price of anarchy? CSC2556 - Nisarg Shah 32 Cost Sharing Game • 푛 players on directed weighted graph 퐺 • Player 푖 푠1 푠2 ➢ Wants to go from 푠푖 to 푡푖 1 1 ➢ Strategy set 푆푖 = {directed 푠푖 → 푡푖 paths} ➢ Denote his chosen path by 푃푖 ∈ 푆푖 10 10 10 • Each edge 푒 has cost 푐푒 (weight) ➢
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