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# Sponsored Search - PowerPoint PPT Presentation

Sponsored Search . Presenter: Lory Al Moakar. Outline. Motivation Problem Definition VCG solution GSP(Generalized Second Price) GSP vs. VCG Is GSP incentive compatible? GSP has a Nash Equilibrium formed of market clearing prices.

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Presentation Transcript

Presenter:

Lory Al Moakar

• Motivation

• Problem Definition

• VCG solution

• GSP(Generalized Second Price)

• GSP vs. VCG

• Is GSP incentive compatible?

• GSP has a Nash Equilibrium formed of market clearing prices.

• Some GSP Nash Equilibria may not be socially optimal.

• Conclusion

• A note on further issues

• Search is by far the most lucrative area, accounting for 40 percent of the total online ad spending in the U.S., according to JupiterResearch.

• Search advertising is expected to grow from \$4.2 billion in 2005 to \$7.5 billion in 2010, … JupiterResearch has forecast.

• M slots such that M ≤ N

• rj the clickthrough rate for slot j i.e. the number of clicks the ad will receive if it is listed in slot j.

• Vithe declared value an advertiser is willing to pay per click

• Vi* is the true value an advertiser is willing to pay per click

• The value an advertiser i places on his/her ad in the jth slot Vij = Vi × rj

• Xij= 1 if advertiser i is assigned slot j

• Social Objective: find a perfect matching that maximizes the total valuation of the advertisers over all perfect matchings

6

a1•

a2•

a3•

•s1

•s2

•s3

• 3 slots

2

0

• Player i pays the mechanism :

• Pi = the best the other players can do if player i was not there – the best the other players can do when player i is there

• Assume advertiser i is assigned slot j if i tells the truth.

• VN-iMis the maximum valuation over all perfect matchings of all slots and all advertisers except i

• VN-iM-jis the maximum valuation over all perfect matchings of all slots except j and all advertisers except i

• The harm that i does by showing up is:

Pj = VN-iM – VN-iM-j

• Pj is the VCG price for advertiser i assigned to slot j.

Assume i lies and is assigned slot k ≠ j which is the slot i gets if it does not lie.

• Gain from lying = Vik* – Pk

• Gain from telling the truth = Vij* – Pj

• To be incentive compatible, we need to prove that the gain from lying ≤ gain from telling the truth

• prove that [Vij* – Pj ] – [Vik* – Pk ] ≥ 0

• Proof:

• Vik*– Pk = Vik* – [ VN-iM – VN-iM-k]

• Vij* – Pj = Vij* – [ VN-iM – VN-iM-j]

• [Vij* – Pj ] – [Vik* – Pk ] =Vij* + VN-iM-j – [Vik* + VN-iM-k]

= optimal valuation – (total valuation when i gets k) ≥ 0

•  This mechanism is incentive compatible.

• GSP assigns slots to highest M bids

• GSP charges advertiser who gets slot j the bid of the advertiser in slot j-1.

• used by Google and a variation of it used by yahoo

Counterexample:

• 2 slots with r1 = 10 and r2 = 4

• 3 advertisers with V1* = 7, V2* = 6 and V3* = 1

• if a1 bids truthfully, he/she will be assigned to s1 pays 60 and gains 70 with net profit of 10

• if a1 lies and bids 5, he/she will be assigned to s2 pays 4 and gains 28 with net profit of 24.

• a1 is better off with lying!

• Consider this example.

• 2 slots (r1 = 10 and r2 = 4) add r3 = 0

• 3 advertisers (V1* = 7, V2* = 6 and V3* = 1)

• VCG prices are

• P1 = 40 P2 = 4 Total 44

• GSP prices are

• P1 = 60 P2 = 4 Total 64

• PVCGi = VN-iM – VN-iM-i

• VN-iM =

• VN-iM-i =

• PVCGi =

• PVCGi+1 =

• ΔVCG = PVCGi – PVCGi+1 = Vi+1(ri – ri+1)

• ΔGSP = PGSPi – PGSPi+1= Vi+1×ri – Vi+2×ri+1

• ΔGSP – ΔVCG = ri (Vi+1 –Vi+2 ) ≥ 0

•  an advertiser pays less using VCG

•  a search engine gains less using VCG

• Consider the previous example:

• 2 slots with r1 = 10 and r2 = 4 r3 = 0

• 3 advertisers with V1* = 7, V2* = 6 and V3* = 1

• The market clearing prices per click for s1 , s2 and s3 respectively are (4, 1, 0)

• Are there bids that would result in these prices for the slots?

• Yes, a1 bids higher than 4, a2 bids 4, and a3 bids 1

•  a1 gets s1 and pays 4 per click  payoff = 70 – 40 = 30

•  a2 gets s2 and pays 1 per click  payoff = 24 – 4 = 20

•  a3 gets s3 and pays 0 per click  payoff = 0

• a1 lowers its bid to 4 - ε  gets s2

 payoff = 28 – 4 = 24 < 30  a1 does not have the incentive to lower its bid

• a2 raises its bid to 5  gets s1

 payoff = 60 – 40 = 20  a2 does not have the incentive to lower its bid

• This is a Nash equilibrium

• N advertisers sorted in decreasing order of valuation per click

• M slots + (N-M) slots with 0 click-through rate

• sort slots in decreasing order of click-though rates

• Consider any set of market clearing prices:

• Since for any set of market-clearing prices, a perfect matching in the resulting preferred seller graph maximizes the total valuation of each valuation for the slot it gets

 the advertiser with the highest valuation per click gets the top slot

 advertiser i gets slot i

• Plan of the proof:

• Construct a set of bids that produces a set of market clearing prices

• These bids form a Nash equilibrium

1. Construct a set of bids that produces a set of market clearing prices

Consider a set of market clearing prices pj

 price per click for slot j : pj* = pj / rj

show that p1*  p2*  p3* …… pM*

Proof:

• Consider any arbitrary slots j and k such that j < k

 show that pj*  pk*

• advertiser k prefers slot k to j (by property of market clearing prices)

• advertiser k ‘s payoff in slot k is (Vk* – pk*) rk

• advertiser k ’s payoff in slot j is (Vk* – pj*) rj

• k’s payoff per click in slot k = Vk* – pk*

• k’s payoff per click in slot j = Vk* – pj*

• since rj rk but advertiser k prefers slot k to slot j

 Vk* – pk*  Vk* – pj*  pj*  pk*

• Therefore, if advertiser i bids Vi such that Vi = pi-1* then the prices for the slots are market clearing prices.

2. These bids form a Nash equilibrium

Show that with the above bids, no advertiser wants to lower his/her bid and no advertiser wants to raise his/her price.

Proof:

• advertiser j in slot j lowers its bid and gets slot k

where j < k

• but since prices are market clearing  advertiser j prefers slot j over any other slot for its current price.

• advertiser j in slot j raises its bid to get slot i but pi > pj

• in order to get slot i, j has to bid higher than what advertiser i is paying  new pi > current pi

• since the current prices are market clearing prices

 advertiser j doesn’t want slot i at the current price so j does not want this slot at a higher price

 this set of bids forms a Nash Equilibrium

• Therefore: GSP has one good Nash Equilibrium in which advertisers get matched to slots in a way that maximizes social welfare i.e. the total valuation of all advertisers for their slots.

Counterexample:

• 3 slots with

• r1 = 10, r2 = 4, r3 = 0

• if a3 moves, he/she pays 3 which results in a negative payoff

• if a2 moves, he/she pays 3 and gets a payoff 24-3 = 21 less than 30.

• if a1 moves, he/she pays 5 and gets a payoff 70-50= 20  less than 24

•  a Nash equilibrium

• Is this Nash market clearing?

• No, since a1 would prefer s1 for its current price 30 and get a payoff of 70 – 30 = 40 instead of s2 but if a1 increases its bid then it has to pay 50 and get a payoff of 20 < 24 (its current payment).

VCG GSP?

the worst equilibrium for the search engines

the best equilibrium for the advertisers

Incentive compatible

GSP

Not clear if it maximizes revenue

Has one optimal equilibrium

truth-telling is generally not an equilibrium strategy

Conclusions

Further Issues GSP?

• A more general model : vij = vi ×αij

• αij is the probability that a user will click on this ad if advertiser i is in the jth slot

• What happens when a user specifies a budget and a set of keywords to bid on?

• Which ads to show given a set of keywords and a search query asking for similar keywords but not the exact ones? If a click occurs, then how much to charge the chosen advertisers who did not bid on these keywords?

• How many ads to show? Does more ads mean more revenue?

• Is pay-per-click the best model? How about pay-per-sale or pay-per-action to stop robot clicks?

• Does the identity of the other advertisers in the other slots affect an advertiser’s click-through-ratio?

References GSP?

• David Easley and Jon Kleinberg, Lecture Notes on