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1 Smooth Games and Intrinsic Robustness Christodoulou and Koutsoupias, Roughgarden Slides stolen/modified from Tim Roughgarden TexPoint fonts used in EMF.

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Presentation on theme: "1 Smooth Games and Intrinsic Robustness Christodoulou and Koutsoupias, Roughgarden Slides stolen/modified from Tim Roughgarden TexPoint fonts used in EMF."— Presentation transcript:

1 1 Smooth Games and Intrinsic Robustness Christodoulou and Koutsoupias, Roughgarden Slides stolen/modified from Tim Roughgarden TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A A A A A A A

2 2

3 Congestion Games Agent i has a set of strategies, S i, each strategy s in S i is a set of resources The cost to an agent is the sum of the costs of the resources r in s used by the agent when choosing s The cost of a resource is a function of the number of agents using the resource f r (# agents) 3

4 4 Price of Anarchy Price of anarchy: [Koutsoupias/Papadimitriou 99] quantify inefficiency w.r.t some objective function. –e.g., Nash equilibrium: an outcome such that no player better off by switching strategies Definition: price of anarchy (POA) of a game (w.r.t. some objective function): optimal obj fn value equilibrium objective fn value the closer to 1 the better

5 5 Network w/2 players: st 2x 12 5x 5 0

6 6 Def: the cost C(f) of flow f = sum of all costs incurred by traffic (avg cost × traffic rate) Formally: if c P (f) = sum of costs of edges of P (w.r.t. the flow f), then: C(f) =  P f P c P (f) st st x 1 ½ ½ Cost = ½½ +½1 = ¾

7 7 Def: linear cost fn is of form c e (x)=a e x+b e

8 8 Nash Equilibrium: To Minimize Cost: Price of anarchy = 28/24 = 7/6. if multiple equilibria exist, look at the worst one st 2x 12 5x 5 cost = = 24 cost = = 28 st 2x 12 5x 5 0 0

9 9 Theorem : [Roughgarden/Tardos 00] for every non- atomic flow network with linear cost fns: ≤ 4/3 × i.e., price of anarchy non atomic flow ≤ 4/3 in the linear latency case. cost of non- atomic Nash flow cost of opt flow

10 10 Abstract Setup n players, each picks a strategy s i player i incurs a cost C i (s) Important Assumption: objective function is cost(s) :=  i C i (s) Key Definition: A game is (λ,μ)-smooth if, for every pair s,s * outcomes (λ > 0; μ < 1):  i C i (s * i,s -i ) ≤ λ●cost(s * ) + μ●cost(s)

11 11 Smooth => POA Bound Next: “canonical” way to upper bound POA (via a smoothness argument). notation: s = a Nash eq; s * = optimal Assuming (λ,μ)-smooth: cost(s) =  i C i (s) [defn of cost] ≤  i C i (s * i,s -i ) [s a Nash eq] ≤ λ●cost(s * ) + μ●cost(s) [(*)] Then: POA (of pure Nash eq) ≤ λ/(1-μ).

12 12 “Robust” POA Best (λ,μ)-smoothness parameters: cost(s) =  i C i (s) ≤  i C i (s * i,s -i ) ≤ λ●cost(s * ) + μ●cost(s) Minimizing: λ/(1-μ).

13 Congestion games with affine cost functions are (5/3,1/3)- smooth Claim: For all non-negative integers y, z : 13

14 Thus, 14 a,b ≥0

15 15 Why Is Smoothness Stronger? Key point: to derive POA bound, only needed  i C i (s * i,s -i ) ≤ λ●cost(s * ) + μ●cost(s) to hold in special case where s = a Nash eq and s * = optimal. Smoothness: requires (*) for every pair s,s * outcomes. –even if s is not a pure Nash equilibrium

16 16 The Need for Robustness Meaning of a POA bound: if the game is at an equilibrium, then outcome is near-optimal.

17 17 The Need for Robustness Meaning of a POA bound: if the game is at an equilibrium, then outcome is near-optimal. Problem: what if can’t reach equilibrium? (pure) equilibrium might not exist might be hard to compute, even centrally –[Fabrikant/Papadimitriou/Talwar], [Daskalakis/ Goldberg/Papadimitriou], [Chen/Deng/Teng], etc. might be hard to learn in a distributed way Worry: are POA bounds “meaningless”?

18 18 Robust POA Bounds High-Level Goal: worst-case bounds that apply even to non-equilibrium outcomes! best-response dynamics, pre-convergence –[Mirrokni/Vetta 04], [Goemans/Mirrokni/Vetta 05], [Awerbuch/Azar/Epstein/Mirrokni/Skopalik 08] correlated equilibria –[Christodoulou/Koutsoupias 05] coarse correlated equilibria aka “price of total anarchy” aka “no-regret players” –[Blum/Even-Dar/Ligett 06], [Blum/Hajiaghayi/Ligett/Roth 08]

19 19 Lots of previous work uses smoothness Bounds atomic (unweighted) selfish routing [Awerbuch/Azar/Epstein 05], [Christodoulou/Koutsoupias 05], [Aland/Dumrauf/Gairing/Monien/Schoppmann 06], [Roughgarden 09] nonatomic selfish routing [Roughgarden/Tardos 00],[Perakis 04] [Correa/Schulz/Stier Moses 05] weighted congestion games [Aland/Dumrauf/Gairing/Monien/Schoppmann 06], [Bhawalkar/Gairing/Roughgarden 10] submodular maximization games [Vetta 02], [Marden/Roughgarden 10] coordination mechanisms [Cole/Gkatzelis/Mirrokni 10]

20 Beyond Pure Nash Equilibria (Static) 20 pure Nash mixed Nash correlated eq CCE Mixed: Correlated: Coarse Correlated:

21 Beyond Nash Equilibria (non-Static) Definition: a sequence s 1,s 2,...,s T of outcomes is no-regret if: for each player i, each fixed action q i : –average cost player i incurs over sequence no worse than playing action q i every time –if every player uses e.g. “multiplicative weights” then get o(1) regret in poly-time –empirical distribution = "coarse correlated eq" 21 pure Nash mixed Nash correlated eq no-regret

22 An Out-of-Equilibrium Bound Theorem: [Roughgarden STOC 09] in a (λ,μ)-smooth game, average cost of every no-regret sequence at most [λ/(1-μ)] x cost of optimal outcome. (the same bound we proved for pure Nash equilibria) 22

23 23 Smooth => No-Regret Bound notation: s 1,s 2,...,s T = no regret; s * = optimal Assuming (λ,μ)-smooth:  t cost(s t ) =  t  i C i (s t ) [defn of cost]

24 24 Smooth => No-Regret Bound notation: s 1,s 2,...,s T = no regret; s * = optimal Assuming (λ,μ)-smooth:  t cost(s t ) =  t  i C i (s t ) [defn of cost] =  t  i [C i (s * i,s t -i ) + ∆ i,t ] [∆ i,t := C i (s t )- C i (s * i,s t -i )]

25 25 Smooth => No-Regret Bound notation: s 1,s 2,...,s T = no regret; s * = optimal Assuming (λ,μ)-smooth:  t cost(s t ) =  t  i C i (s t ) [defn of cost] =  t  i [C i (s * i,s t -i ) + ∆ i,t ] [∆ i,t := C i (s t )- C i (s * i,s t -i )] ≤  t [λ●cost(s * ) + μ●cost(s t )] +  i  t ∆ i,t [(*)]

26 26 Smooth => No-Regret Bound notation: s 1,s 2,...,s T = no regret; s * = optimal Assuming (λ,μ)-smooth:  t cost(s t ) =  t  i C i (s t ) [defn of cost] =  t  i [C i (s * i,s t -i ) + ∆ i,t ] [∆ i,t := C i (s t )- C i (s * i,s t -i )] ≤  t [λ●cost(s * ) + μ●cost(s t )] +  i  t ∆ i,t [(*)] No regret:  t ∆ i,t ≤ 0 for each i. To finish proof: divide through by T.

27 Intrinsic Robustness Theorem: [Roughgarden STOC 09] for every set C, unweighted congestion games with cost functions restricted to C are tight: maximum [pure POA] = minimum [λ/(1-μ)] congestion games w/cost functions in C (λ,μ): all such games are (λ,μ)-smooth 27

28 Intrinsic Robustness Theorem: [Roughgarden STOC 09] for every set C, unweighted congestion games with cost functions restricted to C are tight: maximum [pure POA] = minimum [λ/(1-μ)] weighted congestion games [Bhawalkar/ Gairing/Roughgarden ESA 10] and submodular maximization games [Marden/Roughgarden CDC 10] are also tight in this sense congestion games w/cost functions in C (λ,μ): all such games are (λ,μ)-smooth 28


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