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Smooth Games and Intrinsic Robustness

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.: A A A A A A A A A. Congestion Games.

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Smooth Games and Intrinsic Robustness

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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. Read the TexPoint manual before you delete this box.: AAAAAAAAA

  2. Congestion Games • Agent i has a set of strategies, Si, each strategy s in Si 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 fr(# agents)

  3. 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): the closer to 1 the better equilibrium objective fn value optimal obj fn value

  4. Atomic identical flow is a Congestion game Network w/2 players: 2x 12 0 s t 5x 5

  5. s t Costs: atomic and non-atomic flow Def: the cost C(f) of flow f = sum of all costs incurred by traffic (avg cost × traffic rate) Formally: ifcP(f) = sum of costs of edges of P (w.r.t. the flow f), then: C(f) = P fP•cP(f) x ½ s t ½ 1 Cost = ½•½ +½•1 = ¾

  6. Linear costs Def:linear cost fn is of form ce(x)=aex+be

  7. Atomic identical flow Linear costs Nash Equilibrium: To Minimize Cost: Price of anarchy = 28/24 = 7/6. • if multiple equilibria exist, look at the worst one 2x 12 2x 12 0 0 s t s t 5x 5x 5 5 cost = 14+10 = 24 cost = 14+14 = 28

  8. POA non-atomic flow 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 opt flow cost of non-atomic Nashflow

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

  10. 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 Ci(s) [defn of cost] ≤ i Ci(s*i,s-i) [s a Nash eq] ≤ λ●cost(s*) + μ●cost(s) [(*)] Then: POA (of pure Nash eq) ≤ λ/(1-μ).

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

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

  13. a,b≥0 Thus, Any congestion game (includes atomic unit flow)

  14. Why Is Smoothness Stronger? Key point: to derive POA bound, only needed iCi(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 isnot a pure Nash equilibrium

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

  16. 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”?

  17. 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]

  18. 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]

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

  20. Beyond Nash Equilibria(non-Static) Definition: a sequence s1,s2,...,sTof outcomes is no-regret if: • for each player i, each fixed action qi: • average cost player i incurs over sequence no worse than playing action qievery time • if every player uses e.g. “multiplicative weights” then get o(1) regret in poly-time • empirical distribution = "coarse correlated eq" no-regret correlated eq mixed Nash pure Nash

  21. 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. Smooth => No-Regret Bound • notation: s1,s2,...,sT = no regret; s* = optimal Assuming (λ,μ)-smooth: t cost(st) = t i Ci(st) [defn of cost]

  23. Smooth => No-Regret Bound • notation: s1,s2,...,sT = no regret; s* = optimal Assuming (λ,μ)-smooth: t cost(st) = t i Ci(st) [defn of cost] = t i [Ci(s*i,st-i) + ∆i,t] [∆i,t:= Ci(st)- Ci(s*i,st-i)]

  24. Smooth => No-Regret Bound • notation: s1,s2,...,sT = no regret; s* = optimal Assuming (λ,μ)-smooth: t cost(st) = t i Ci(st) [defn of cost] = t i [Ci(s*i,st-i) + ∆i,t] [∆i,t:= Ci(st)- Ci(s*i,st-i)] ≤ t [λ●cost(s*) + μ●cost(st)] + i t ∆i,t[(*)]

  25. Smooth => No-Regret Bound • notation: s1,s2,...,sT = no regret; s* = optimal Assuming (λ,μ)-smooth: t cost(st) = t iCi(st) [defn of cost] = t i[Ci(s*i,st-i) + ∆i,t] [∆i,t:= Ci(st)- Ci(s*i,st-i)] ≤ t [λ●cost(s*) + μ●cost(st)] + it ∆i,t[(*)] No regret:t ∆i,t ≤ 0 for each i. To finish proof: divide through by T.

  26. 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. 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

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