Regret Minimization for Reserve Prices in Second-Price Auctions

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1 Regret Minimization for Reserve Prices in Second-Price Auctions Nicolò Cesa-Bianchi Università degli Studi di Milano Joint work with: Claudio Gentile (Varese) and Yishay Mansour (Tel-Aviv) N. Cesa-Bianchi (UNIMI) Second-Price Auctions 1 / 21

2 Real-Time Bidding From: wifiadexchange.com N. Cesa-Bianchi (UNIMI) Second-Price Auctions 2 / 21

3 Real-Time Bidding SSP (supply-side platforms): assist the seller (publisher) e.g., by optimizing the reserve price DSP (demand-side platforms): assist the buyer (advertiser) e.g., by optimizing bids RTB process 1 A user visits the publisher website creating an impression 2 A call for bids is sent to ad exchanges through SSP 3 The ad exchanges query DSP for advertisers bids 4 The bids are submitted 5 Winner is selected and winner s ad displayed N. Cesa-Bianchi (UNIMI) Second-Price Auctions 3 / 21

4 Second-price auctions Mechanism [Myerson, 1981] Highest bid B (1) wins, but the price is reduced to the second highest bid B (2) Alice bids \$0.80 and Bob bids \$0.55. Alice wins and pays \$0.55 Theorem: Bidding the true value is a dominant strategy Assume Alice bids B = v, her true value for the impression Bid less and lose: Alice bids B < v and loses the auction. Then B < B (2) < v and she lost a payoff of v B (2) Bid more and win: Alice bids B > v and wins the auction. Then v < B (1) < B and she ends up paying B (1) > v N. Cesa-Bianchi (UNIMI) Second-Price Auctions 4 / 21

5 Reserve price: how much the seller values the item 1 Mechanism If the highest bid B (1) 0.8 is below the reserve price, 0.6 then the item is not sold Otherwise, the winning 0.4 bid is reduced to the maximum between the 0.2 second highest bid B (2) and the reserve price Theorem B (2) B (1) Bidding the true value is a dominant strategy even in the presence of a nonzero reserve price N. Cesa-Bianchi (UNIMI) Second-Price Auctions 5 / 21

6 Additional components Soft floor Allows to lower the reserve price by running first-price auctions when the highest bid is between reserve price and soft floor Estimated Clear Price (ECP) An estimate of a bid that is likely to win based on historical win rates Features available to SSP and/or DSP location and carrier (if user is mobile) publisher name website section placement (identifies a space where the ad can be shown) banner identifier user identifier ECP N. Cesa-Bianchi (UNIMI) Second-Price Auctions 6 / 21

7 Some actual data N. Cesa-Bianchi (UNIMI) Second-Price Auctions 7 / 21

8 Some actual data N. Cesa-Bianchi (UNIMI) Second-Price Auctions 8 / 21

9 The formal auction model For each impression: 1 Seller chooses reserve price p 2 Bids B 1,..., B m are drawn (hidden from seller) 3 Bidder with highest bid B (1) wins the auction 4 Revenue R(p, B 1,..., B m ) is revealed to seller Some uncomfortable assumptions In each auction, bids are drawn i.i.d. from some unknown distribution The bid distribution is the same across multiple auctions Seller knows number of bidders m (or its distribution) N. Cesa-Bianchi (UNIMI) Second-Price Auctions 9 / 21

10 The revenue function Second-price auction with reserve price R ( p, B (1), B (2)) = B (2) I { B (2) > p } + p I { B (2) p B (1)} 1 Notation for bids: B (1) B (2) Note: 0.4 Revenue only depends on p and two highest 0.2 bids B (1), B (2) B (2) B (1) N. Cesa-Bianchi (UNIMI) Second-Price Auctions 10 / 21

11 The expected revenue (for a fixed number of bidders) [ µ(p) = E R ( p, B (1), B (2))] 1 = P ( B (2) > x ) dx + p P ( B (2) p B (1)) p N. Cesa-Bianchi (UNIMI) Second-Price Auctions 11 / 21

12 Remark If the bid distribution satisfies the monotone hazard rate assumption, then the optimal reserve price does not depend on number of bidders: p = 1 F(p ) f(p ) where F(p) = P ( B p ) and f(p) = df(p) dp We do not assume the monotone hazard rate assumption N. Cesa-Bianchi (UNIMI) Second-Price Auctions 12 / 21

13 Publisher s regret when using p 1, p 2,... ] [ T ( E µ(p ) µ(p t )) where p = argmax µ(p ) p t= p t p N. Cesa-Bianchi (UNIMI) Second-Price Auctions 13 / 21

14 Control regret in a sequence of T auctions Since bids are in [0, 1] regret after T auctions is always O(T) First approach Partition reserve prices in K = 1/ε bins and pick a reserve price p k for each bin k Run a multiarmed bandit algorithm over the set of prices p 1,..., p K T Regret(T) + = O ( T 2/3) for ε = T 1/3 ε ε T }{{} approx. cost } {{ } estim. cost This holds without any special assumption on the expected revenue function N. Cesa-Bianchi (UNIMI) Second-Price Auctions 14 / 21

15 Control regret in a sequence of T auctions Second approach Run a stochastic optimization algorithm that computes a sequence p 1, p 2,... of reserve prices to find the maximum of µ Using p t gives a stochastic revenue R t such that E[R t ] = µ(p t ) Regret grows like O ( T ) only under specific assumptions on µ (unimodality, smoothness, etc.) Main question Can we get T 1/2 regret without any assumption on the expected revenue function µ? N. Cesa-Bianchi (UNIMI) Second-Price Auctions 15 / 21

16 Main algorithmic idea Rewrite expected revenue function µ(p) = E [ B (2)] + } {{ } constant p 0 P ( B (2) x ) dx p P ( B p ) m } {{ } function of F(p) Express P ( B p ) m in terms of F(p) = P ( B (2) p ) Compute approximation of F( ) = P ( B (2) ) by sampling B (2) Obtain approximation of expected revenue µ N. Cesa-Bianchi (UNIMI) Second-Price Auctions 16 / 21

17 Sampling second prices Goal: Sample B (2) in order to approximate µ 1 If p = 0, then revenue is always B (2) However, E [ B (2)] may be much smaller than µ(p ) Hence, approximating µ well by setting p = 0 is potentially wasteful p N. Cesa-Bianchi (UNIMI) Second-Price Auctions 17 / 21

18 Approximating the expected revenue 1 Find a rough approximation of µ by 0.8 using p = 0 a few times Use this approximation to find a region of prices that includes p with high probability Recurse on this region using least price as 0 reserve price p i p B (2) is sampled in green region using a nearly optimal reserve price N. Cesa-Bianchi (UNIMI) Second-Price Auctions 18 / 21

19 Controlling regret in each phase For each phase i = 1, 2,..., S Refine current region using T i auctions with reserve price p i (least price in current region) Regret in phase i when phase length is set to T i = T 1 2 i ( T i µ(p ) µ( p i ) ) C T i = CT T i 1 } {{ } confidence interval from previous phase Note: Choice of phase length implies that number of phases is S = 2 log log T N. Cesa-Bianchi (UNIMI) Second-Price Auctions 19 / 21

20 Finishing up T ( ) ( ) S ( ) µ(p ) µ(p t ) µ(p ) µ(0) T } {{ } }{{} 1 + µ(p ) µ( p i ) 1 = i=2 T S ( ) T + µ(p ) µ( p i ) t=1 T + i=2 S T i i=2 C T i 1 T + S CT = O ( (log log T) T ) T i T i N. Cesa-Bianchi (UNIMI) Second-Price Auctions 20 / 21

21 Open problems Extend to generalized second-price auction (multiple impressions sold in each auction) What if number m of bidders is unknown? What if bidders correlate (using, e.g., ECP)? N. Cesa-Bianchi (UNIMI) Second-Price Auctions 21 / 21

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