Pure and Bayes-Nash Price of Anarchy for GSP Renato Paes Leme Cornell Éva Tardos Cornell & MSR Keyword Auctions organic search results sponsored search links Keyword Auctions Keyword Auctions Prospective advertisers Selling one Ad Slot $2 $5 $7 $3 Selling one Ad Slot $2 Pays $5 per click $5 $7 $3 Vickrey Auction -Truthful - Efficient - Simple -… Auction Model b1 $$$ $$$ b3 b2 $$ b4 b5 b6 $$ $ $ Auction Model Vickrey Auction b2 - Notb Truthful 4 - Not Efficient - Even Simpler -… b1 $$$ VCG bAuction $$$ 3 -Truthful $$ - Efficient Generalized - Simple (?) b5 -Second …$$ Price Auction $ b6 $ Our Results Generalized Second Price Auction (GSP), although not optimal, has good social welfare guarantees: • 1.618 for Pure Price of Anarchy • 8 for Bayes-Nash Price of Anarchy • GSP with uncertainty (Simplified) Model • αj : click-rate of slot j • vi : value of player i • bi : bid (declared value) v1 v2 • Assumption: bi ≤ vi Since playing bi > vi is v3 dominated strategy. b1 α1 α2 b2 b3 α3 (Simplified) Model v1 b1 v2 b2 v3 pays b1 per click α1 b3 α2 α3 (Simplified) Model vi bi σ = π-1 i = π(j) αj j = σ(i) Utility of player i : ui(b) = ασ(i) ( vi - bπ(σ(i) + 1)) Model vi bi σ = π-1 i = π(j) αj j = σ(i) Utility of player i : ui(b) = ασ(i) ( vi - bπ(σ(i) + 1)) next highest bid Model vi bi σ = π-1 i = π(j) αj j = σ(i) Nash equilibrium: ui(bi,b-i) ≥ ui(b’i,b-i) Is truth-telling always Nash ? Example Non-truthful u1 = 10.9(2-0) (2-1) v1 = 2 bb1 1==0.9 2 α1 = 1 v2 = 1 b1 = 1 α2 = 0.9 Measuring inefficiency vi bi σ = π-1 i = π(j) Social welfare = ∑i vi ασ(i) Optimal allocation = ∑i vi αi αj j = σ(i) Measuring inefficiency Price of Anarchy = max Opt Nash SW(Nash) = Main Theorem 1 Thm: Pure Price of Anarchy ≤ 1.618 If bi ≤ vi and (b1…bn) are bid in equilibrium, then for the allocation σ : ∑i vi ασ(i) ≥ 1.618-1 ∑i vi αi Previously known [EOS, Varian]: Price of Stability = 1 Modeling uncertainty: GSP as a Bayesian Game GSP as a Bayesian Game b b? GSP as a Bayesian Game Idea: Optimize against a distribution. b b b b b b b b b b b b b Bayes-Nash solution concept • Bayes-Nash models the uncertainty of other players about valuations • Values vi are independent random vars • Optimize against a distribution Thm: Bayes-Nash PoA ≤ 8 Bayesian Model v1 ~ V 1 b1(v1) α1 v2 ~ V 2 v3 ~ V 3 b2(v2) b3(v3) α2 α3 Model vi ~ V i bi(vi) αj i = π(j) j = σ(i) Bayes-Nash equilibrium: E[ui(bi,b-i)|vi] ≥ E[ui(b’i,b-i)|vi] Expectation over v-i Bayes-Nash Equilibrium vi are random variables μ(i) = slot that player i occupies in Opt (also a random variable) Bayes-Nash PoA = E[∑i vi αμ(i)] E[∑i vi ασ(i)] Previously known [G-S]: Price of Stability ≠ 1 Sketch of the proof ασ(j) vπ(i) ≥ 1 + vj αi v1 α1 therefore: ασ(j) 1 vπ(i) 1 ≥ ≥ or αi vj 2 2 v2j αi2 Simple and intuitive condition on matchings in equilibrium. ασ(j) 3 vvπ(i) 3 Opt Sketch of the proof ασ(j) vπ(i) ≥ 1 + v αi j vj vπ(i) αi ασ(j) Need to show only for i < j and π(i) > π(j). It is a combination of 3 relations: ασ(j) ( vj – bπ(σ(j)+1) ) ≥ αi ( vj – bπ(i) ) [ Nash ] bπ(σ(j)+1) ≥ 0 bπ(i) ≤ vπ(i) [conservative] Sketch of the proof ασ(j) vπ(i) ≥ 1 + vj αi 2 SW = ∑i ασ(i) vi + αi vπ(i) = = ∑i αi vi ασ(i) +vπ(i) vi αi ≥ ∑i αi vi = Opt ≥ Proof idea: new structural condition Pure Nash version: vi ασ(i) +αivπ(i) ≥ αivi vj vπ(i) αi ασ(j) Bayes Nash version: viE[ασ(i)|vi] + E[αμ(i) vπμ (i)|vi] ≥ ¼ viE[αμ(i)|vi] Upcoming results [Lucier-Paes Leme-Tardos] • Improved Bayes-Nash PoA to 3.164 • Valid also for correlated distributions • Future directions: • Tight Pure PoA (we think it is 1.259)