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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

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

congestion games
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)

price of anarchy
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

slide5

Atomic identical flow

is a Congestion game

Network w/2 players:

2x

12

0

s

t

5x

5

slide6

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

slide7

Linear costs

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

slide8

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

slide9

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

abstract setup
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)

smooth poa bound
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).

robust poa
“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).

congestion games with affine cost functions are 5 3 1 3 smooth
Congestion games with affine cost functions are (5/3,1/3)-smooth
  • Claim: For all non-negative integers y, z :
slide14

a,b≥0

Thus,

Any congestion game

(includes atomic unit flow)

why is smoothness stronger
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
the need for robustness
The Need for Robustness

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

the need for robustness1
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”?

robust poa bounds
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]
lots of previous work uses smoothness bounds
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]

beyond pure nash equilibria static
Beyond Pure Nash Equilibria (Static)

Mixed:

CCE

Correlated:

correlated eq

Coarse Correlated:

mixed Nash

pure

Nash

beyond nash equilibria non static
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

an out of equilibrium bound
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)

smooth no regret bound
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]

smooth no regret bound1
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)]

smooth no regret bound2
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[(*)]

smooth no regret bound3
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.

intrinsic robustness
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

intrinsic robustness1
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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