research internet advertising auctions david pennock, yahoo! research - new york contributed slides:...

ResearchResearch

Internet Advertising Auctions

David Pennock, Yahoo! Research - New York

Contributed slides:K.Asdemir, H.Bhargava, J.Feng, S.Lahaie, M.Schwarz

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Advertising Then and Now• Then: Think real estate

Phone callsManual negotiation“Half doesn’t work”

• Now: Think Wall StreetAutomation, automation, automationAdvertisers buy contextual attention:

User i on page j at time tComputer learns what ad is bestComputer mediates ad sales: Auction!Computer measures which ads work

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Advertising Then & Now: Video

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

http://ycorpblog.com/2008/04/06/this-one-goes-to-11/

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Advertising: NowTools Disciplines

• Auctions

• Machine learning

• Optimization

• Sales

• Economics &Computer Science

• Statistics &Computer Science

• Operations Research Computer Science

• Marketing

search “las vegas travel”, Yahoo!

Sponsored search auctions

Space next to search results is sold at auction

“las vegas travel” auction

Ad exchanges



Outline

• Motivation: Industry facts & figures

• Introduction to sponsored search– Brief and biased history

– Allocation and pricing: Google vs old Yahoo!

– Incentives and equilibrium

• Ad exchanges

• Selected survey of research

• Prediction markets

Auctions Applications

eBay

– 216 million/month

Google / Yahoo!

– 11 billion/month (US)


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Market Capitalization (billions)

Ebay (founded 1995) Google (founded 1998)Sotheby's (founded 1744)

• eBay • Google


• eBay • Google



Newsweek June 17, 2002

“The United States of EBAY”

• In 2001: 170 million transactions worth $9.3 billion in 18,000 categories “that together cover virtually the entire universe of human artifacts—Ferraris, Plymouths and Yugos; desk, floor, wall and ceiling lamps; 11 different varieties of pockets watches; contemporary Barbies, vintage Barbies, and replica Barbies.”

• “Since everything that transpires on Ebay is recorded, and most of it is public, the site constitutes a gold mine of data on American tastes and preoccupations.”

“The United States of Search”

• 11 billion searches/month

• 50% of web users search every day

• 13% of traffic to commercial sites

• 40% of product searches

• $8.7 billion 2007 US ad revenue (41% of $21.2 billion US online ads; 2% of all US ads)

• Still ~20% annual growth after years of nearly doubling

• Search data: Covers nearly everything that people think about: intensions, desires, diversions, interests, buying habits, ...

Online ad industry revenue



http://www.iab.net/media/file/IAB_PwC_2007_full_year.pdf

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Introduction tosponsored search

• What is it?• Brief and biased history• Allocation and pricing: Google vs Yahoo!• Incentives and equilibrium

search “las vegas travel”, Yahoo!


Space next to search results is sold at auction

“las vegas travel” auction


• Search engines auction off space next to search results, e.g. “digital camera”

• Higher bidders get higher placement on screen

• Advertisers pay per click: Only pay when users click through to their site; don’t pay for uncliked view (“impression”)


• Sponsored search auctions are dynamic and continuous: In principle a new “auction” clears for each new search query

• Prices can change minute to minute;React to external effects, cyclical & non-cyc– “flowers” before Valentines Day

– Fantasy football

– People browse during day, buy in evening

– Vioxx

Vioxx

0

5

10

15

20

25

30

9/14/089/15/089/16/089/17/089/18/089/19/089/20/089/21/089/22/089/23/089/24/089/25/089/26/089/27/089/28/089/29/089/30/0810/1/0810/2/0810/3/0810/4/0810/5/0810/6/0810/7/0810/8/0810/9/0810/10/0810/11/0810/12/0810/13/08

Date

Price ($)

Example price volatility: Vioxx

Sponsored search today

• 2007: ~ $10 billion industry– ‘06~$8.5B ‘05~$7B ‘04~$4B ‘03~$2.5B ‘02~$1B

• $8.7 billion 2007 US ad revenue (41% of US online ads; 2% of all US ads)

• Resurgence in web search, web advertising

• Online advertising spending still trailing consumer movement online

• For many businesses, substitute for eBay

• Like eBay, mini economy of 3rd party products & services: SEO, SEM

Sponsored SearchA Brief & Biased History

• Idealab GoTo.com (no relation to Go.com)

– Crazy (terrible?) idea, meant to combat search spam

– Search engine “destination” that ranks results based on who is willing to pay the most

– With algorithmic SEs out there, who would use it?

• GoTo Yahoo! Search Marketing

– Team w/ algorithmic SE’s, provide “sponsored results”

– Key: For commercial topics (“LV travel”, “digital camera”) actively searched for, people don’t mind (like?) it

– Editorial control, “invisible hand” keep results relevant

• Enter Google

– Innovative, nimble, fast, effective

– Licensed Overture patent (one reason for Y!s ~5% stake in G)


• Overture introduced the first design in 1997: first price, rank by bid

• Google then began running slot auctions in 2000: second price, rank by revenue (bid * CTR)

• In 2002, Overture (at this point acquired by Yahoo!) then switched to second-price. Still uses rank by bid; Moving toward rank by revenue

Thanks: S. Lahaie


• In the beginning:– Exact match, rank by bid, pay per click, human editors

– Mechanism simple, easy to understand, worked, somewhat ad hoc

• Today & tomorrow:– “AI” match, rank by expected revenue (Google), pay per

click/impression/conversion, auto editorial, contextual (AdSense, YPN), local, 2nd price (proxy bid), 3rd party optimizers, budgeting optimization, exploration exploitation, fraud, collusion, more attributes and expressiveness, more automation, personalization/targeting, better understanding (economists, computer scientists)

Sponsored Search ResearchA Brief & Biased History

• Circa 2004

– Weber & Zeng, A model of search intermediaries and paid referrals

– Bhargava & Feng, Preferential placement in Internet search engines

– Feng, Bhargava, & PennockImplementing sponsored search in web search engines: Computational evaluation of alternative mechanisms

– Feng, Optimal allocation mechanisms when bidders’ ranking for objects is common

– Asdemir, Internet advertising pricing models

– Asdemir, A theory of bidding in search phrase auctions: Can bidding wars be collusive?

– Mehta, Saberi, Vazirani, & VaziranAdWords and generalized on-line matching

• Key papers, survey, and ongoing research workshop series

– Edelman, Ostrovsky, and Schwarz, Internet Advertising and the Generalized Second Price Auction, 2005

– Varian, Position Auctions, 2006

– Lahaie, Pennock, Saberi, Vohra, Sponsored Search, Chapter 28 in Algorithmic Game Theory, Cambridge University Press, 2007

– 1st-3nd Workshops on Sponsored Search Auctions4th Workshop on Ad Auctions -- Chicago Julu 8-9, 2008

Allocation and pricing

• Allocation

– Yahoo!: Rank by decreasing bid

– Google: Rank by decreasing bid * E[CTR] (Rank by decreasing “revenue”)

• Pricing

– Pay “next price”: Min price to keep you in current position

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Yahoo Allocation: Bid Ranking“las vegas travel” auction search “las vegas travel”, Yahoo!

pays $2.95per click

pays $2.94

pays $1.02

... bidder ipays bidi+1+.01

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Google Allocation: $ Ranking“las vegas travel” auction

x E[CTR] = E[RPS]

x E[CTR] = E[RPS]

x E[CTR] = E[RPS]

x E[CTR] = E[RPS]

x E[CTR] = E[RPS]

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Google Allocation: $ Ranking“las vegas travel” auction search “las vegas travel”, Google

x .1 = .301

x .2 = .588

x .1 = .293

x E[CTR] = E[RPS]

x E[CTR] = E[RPS]

TripReservations

Expedia

pays 3.01*.1/.2+.01 = 1.51per click

pays 2.93*.1/.1+.01 = 2.94

pays bidi+1*CTRi+1/CTRi+.01

LVGravityZone

etc...

Aside: Second price auction(Vickrey auction)

• All buyers submit their bids privately

• buyer with the highest bid wins;pays the price of the second highest bid

$150$120

$90

$50

Only pays $120

Incentive Compatibility(Truthfulness)

• Telling the truth is optimal in second-price (Vickrey) auction

• Suppose your value for the item is $100;if you win, your net gain (loss) is $100 - price

• If you bid more than $100:

– you increase your chances of winning at price >$100

– you do not improve your chance of winning for < $100

• If you bid less than $100:

– you reduce your chances of winning at price < $100

– there is no effect on the price you pay if you do win

• Dominant optimal strategy: bid $100

– Key: the price you pay is out of your control

• Vickrey’s Nobel Prize due in large part to this result

Vickrey-Clark-Groves (VCG)

• Generalization of 2nd price auction

• Works for arbitrary number of goods, including allowing combination bids

• Auction procedure:– Collect bids

– Allocate goods to maximize total reported value (goods go to those who claim to value them most)

– Payments: Each bidder pays her externality;Pays: (sum of everyone else’s value without bidder) - (sum of everyone else’s value with bidder)

• Incentive compatible (truthful)

Yahoo! Confidential

Is Google pricing = VCG?

Well, not really …

Put Nobel Prize-winning theories to work.Google’s unique auction model uses Nobel Prize-winning economic theory to eliminate the winner’s curse – that feeling that you’ve paid too much. While the auction model lets advertisers bid on keywords, the AdWords™ Discounter makes sure that they only pay what they need in order to stay ahead of their nearest competitor.

https://google.com/adsense/afs.pdf

Yahoo! Confidential

VCG pricing

• (sum of everyone else’s value w/o bidder) - (sum of everyone else’s value with bidder)

• CTRi = advi * posi (key “separability” assumption)

• pricei = 1/advi*(∑j<ibidj*CTRj + ∑j>ibidj*advj*posj-1 -∑j≠ibidj*CTRj )

= 1/advi*(∑j>ibidj*advj*posj-1 - ∑j>ibidj*CTRj )

• Notes

– For truthful Y! ranking set advi = 1. But Y! ranking technically not VCG because not efficient allocation.

– Last position may require special handling

Yahoo! Confidential

Next-price equilibrium

• Next-price auction: Not truthful: no dominant strategy

• What are Nash equilibrium strategies? There are many!

• Which Nash equilibrium seems “focal” ?

• Locally envy-free equilibrium [Edelman, Ostrovsky, Schwarz 2005]

Symmetric equilibrium [Varian 2006]

Fixed point where bidders don’t want to move or – Bidders first choose the optimal position for them: position i

– Within range of bids that land them in position i, bidder chooses point of indifference between staying in current position and swapping up with bidder in position i-1

• Pure strategy (symmetric) Nash equilibrium

• Intuitive: Squeeze bidder above, but not enough to risk “punishment” from bidder above

Yahoo! Confidential

Next-price equilibrium

• Recursive solution:

posi-1*advi*bi = (posi-1-posi)*advi*vi+posi*advi+1*bi+1

bi = (posi-1-posi)*advi*vi+posi*advi+1*bi+1

posi-1*advi

• Nomenclature:Next price = “generalized second price” (GSP)

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Ad exchanges

• Right Media• Expressiveness

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Online Advertising Evolution

1. Direct: Publishers sell owned & operated (O&O) inventory

2. Ad networks: Big publishers place ads on affiliate sites, share revenueAOL, Google, Yahoo!, Microsoft

3. Ad exchanges: Match buy orders from advertisers with sell orders from publishers and ad networksKey distinction: exchange does not “own” inventory

Yahoo! Confidential

Exchange Basics

Exchange

Demand

Inventory

Netflix

Vonage

Auto.com…

Advertisers

Ad.com

CPX

Tribal…

Networks

MySpace

Six Apart

Looksmart

Monster…

Publishers

[Source: Ryan Christensen]

Yahoo! Confidential

Right Media Publisher Experience

• Publisher can select / reject specific advertisers

• Green = linked network

• Light Blue = direct advertiser

• Publishers can traffic their own deals by clicking “Add Advertiser”

The publisher can approve creative from each advertiser


Yahoo! Confidential

Right Media Advertiser Experience

• Advertisers can set targets for CPM, CPC and CPA campaigns

• Set budgets and frequency caps

• Locate publishers, upload creative and traffic campaigns


Yahoo! Confidential

Expressiveness

• “I’ll pay 10% more for Males 18-35”

• “I’ll pay $0.05 per impression, $0.25 per click, and $5.25 per conversion”

• “I’ll pay 50% more for exclusive display, or w/o Acme”

• “My marginal value per click is decreasing/increasing”

• “Never/Always show me next to Acme”“Never/Always show me on adult sites”“Show me when Amazon.com is 1st algo search result”

• “I need at least 10K impressions, or none”

• “Spread out my exposure over the month”

• “I want three exposures per user, at least one in the evening”

Design parameters: Advertiser needs/wants,computational/cognitive complexity, revenue

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Expressiveness Example

• Competition constraints

3 x .05 = .15

1 x .05 = .05

b xCTR = RPS

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Expressiveness Example


4 x .07 = .28

b xCTR = RPS

monopoly bid

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Expressiveness: Design

• Multi-attribute bidding

Advertiser1

Advertiser2

Male users (50%)

$1 $2

Female users (50%)

$2 $1

Un-differentiated

$1.50 $1.50

Advertiser1

Advertiser2

Pre-qualified (50%)

$2 $2

Other (50%) $1 $1

Un-differentiated

$1.50 $1.50

Yahoo! Confidential

Expressiveness: Less is More

• Pay per conversion: Advertisers pay for user actions (sales, sign ups, extended browsing, ...)

– Network sends traffic

– Advertisers rate users/types 0-100Pay in proportion

– Network learns, optimizes traffic, repeat

• Fraud: Short-term gain only: If advertisers lie, they stop getting traffic

Yahoo! Confidential

Expressiveness: Less is More

• “I’m a dry cleaner in Somerset, New Jersey with $100/month. Advertise for me.”

• Can advertisers trust network to optimize?

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Coming Convergence:ML and Mechanism Design

Mechanism(Rules)

e.g. Auction,Exchange, ...

Stats/ML/OptEngine

Stats/ML/OptEngine

Stats/ML/OptEngine

Stats/ML/OptEngine

Stats/ML/OptEngine

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ML Inner Loop

• Optimal allocation (ad-user match) depends on: bid, E[clicks], E[sales], relevance, ad, advertiser, user, context (page, history), ...

• Expectations must be learned• Learning in dynamic setting requires

exploration/exploitation tradeoff• Mechanism design must factor all this

in! Nontrivial.

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Selected Survey ofInternet Advertising Research

An Analysis of Alternative Slot Auction Designs for

Sponsored SearchSebastien Lahaie, Harvard University**work partially conducted at Yahoo! Research

ACM Conference on Electronic Commerce, 2006

Source: S. Lahaie

Objective

•Initiate a systematic study of Yahoo! and Google slot auctions designs.

•Look at both “short-run” incomplete information case, and “long-run” complete information case.

Source: S. Lahaie

Outline•Incomplete information (one shot game)

• Incentives

•Efficiency

• Informational requirements

•Revenue

•Complete Information (long-run equilibrium)

•Existence of equilibria

•Characterization of equilibria

•Efficiency of equilibria (“price of anarchy”)

Source: S. Lahaie

The Model• slots, bidders

•The type of bidder i consists of

•a value per click of , realization

•a relevance , realization

• is bidder i’s revenue, realization

•Ad in slot is viewed with probability

So CTRi,k =

•Bidder i’s utility function is quasi-linear:

Source: S. Lahaie

The Model (cont’d)

• is i.i.d on according to

• is continuous and has full support

• is common knowledge

•Probabilities are common knowledge.

•Only bidder i knows realization

•Both seller and bidder i know , but other bidders do not

Source: S. Lahaie

Auction Formats

•Rank-by-bid (RBB): bidders are ranked according to their declared values ( )

•Rank-by-revenue (RBR): bidders are ranked according to their declared revenues ( )

•First-price: a bidder pays his declared value

•Second-price (next-price): For RBB, pays next highest price. For RBR, pays

•All payments are per click

Source: S. Lahaie

•First-price: neither RBB nor RBR is truthful

•Second-price: being truthful is not a dominant strategy, nor is it an ex post Nash equilibrium (by example):

•Use Holmstrom’s lemma to derive truthful payment rules for RBB and RBR:

•RBR with truthful payment rule is VCG

Incentives

1 61 4

Source: S. Lahaie

Efficiency•Lemma: In a RBB auction with either a

first- or second-price payment rule, the symmetric Bayes-Nash equilibrium bid is strictly increasing with value. For RBR it is strictly increasing with product.

•RBB is not efficient (by example).

•Proposition: RBR is efficient (proof).

0.5 6

1 4

Source: S. Lahaie

First-Price Bidding Equilibria• is the expected resulting

clickthrough rate, in a symmetric equilibrium of the RBB auction, to a bidder with value y and relevance 1.

• is defined similarly for bidder with product y and relevance 1.

•Proposition: Symmetric Bayes-Nash equilibrium strategies in a first-price RBB and RBR auction are given by, respectively:

Source: S. Lahaie

Informational Requirements

•RBB: bidder need not know his own relevance, or the distribution over relevance.

•RBR: must know own relevance and joint distribution over value and relevance.

Source: S. Lahaie

Revenue Ranking

•Revenue equivalence principle: auctions that lead to the same allocations in equilibrium have the same expected revenue.

•Neither RBB nor RBR dominates in terms of revenue, for a fixed number of agents, slots, and a fixed .

Source: S. Lahaie

Complete Information Nash

Equilibria

Argument: a bidder always tries to match the next-lowest bid to minimize costs. But it is not an equilibrium for all to bid 0.

Argument: corollary of characterization lemma.

Source: S. Lahaie

Characterization of Equilibria

•RBB: same characterization with replacing

Source: S. Lahaie

Price of AnarchyDefine:

Source: S. Lahaie

Exponential Decay

• Typical model of decaying clickthrough rate:

• [Feng et al. ’05] find that their actual clickthrough data is fit well by such a model with

• In this case

Source: S. Lahaie

Conclusion

• Incomplete information (on-shot game):

•Neither first- nor second-pricing leads to truthfulness.

•RBR is efficient, RBB is not

•RBB has weaker informational requirements

•Neither RBB nor RBR is revenue-dominant

• Complete information (long-run equilibrium):

•First-price leads to no pure strategy Nash equilibria, but second-price has many.

•Value in equilibrium is constant factor away from “standard” value.

Source: S. Lahaie

Future Work

•Better characterization of revenue properties: under what conditions on does either RBB or RBR dominate?

•Revenue results for complete information case (relation to Edelman et al.’s “locally envy-free equilibria”).

Source: S. Lahaie

Research Problem: Online Estimation of Clickrates

•Make virtually no assumptions on clickrates.

•Each different ranking yields (1) information on clickrates and (2) revenue.

•Tension between optimizing current revenue based on current information, and gaining more info on clickrates to optimize future revenue (multi-armed bandit problem...)

•Twist: chosen policy determines rankings, which will affect agent’s equilibrium behavior.

Source: S. Lahaie

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Equilibrium revenue simulations of hybrid sponsored search mechanisms

Sebastien Lahaie, Harvard University**work conducted at Yahoo! Research

David Pennock, Yahoo! Research

Yahoo! Confidential

Revenue effects

• What gives most revenue?– Key: If rules change, advertiser bids will change

– Use Edelman et al. envy-free equilibrium solution

OvertureHighest bid wins

Google/Yahoo!Highest bid*CTR wins

s=0s=1/2 ?

s=1s=3/4 ?

HybridHighest bid*(CTR)s wins

Yahoo! Confidential

Monte-Carlo simulations

• 10 bidders, 10 positions

• Value and relevance are i.i.d. and have lognormal marginals with mean and variance (1,0.2) and (1,0.5) resp.

• Spearman correlation between value and relevance is varied between -1 and 1.

• Standard errors are within 2% of plotted estimates.

Source: S. Lahaie

Yahoo! Confidential

Source: S. Lahaie

Yahoo! Confidential

Preliminary Conclusions

• With perfectly negative correlation(-1), revenue, efficiency, and relevance exhibits threshold behavior

• Squashing up to this threshold can improve revenue without too much sacrifice in efficiency or relevance

• Squashing can significantly improve revenue with positive correlation

Source: S. Lahaie

Pragmatic Robots and Equilibrium Bidding in GSP

Auctions

Michael Schwarz, Yahoo! Research

Ben Edelman, Harvard University

Source: M. Schwarz

Yahoo! Confidential

Testing game theory

• Empirical game theory– Analytic solutions intractable in all but simplest settings

– Laboratory experiments cumbersome, costly

– Agent-based simulation: easy, cheap, allow massive exploration; Key: modeling realistic strategies

• Ideal for agent-based simulation: when real economic decisions are already delegated to software

“If pay-per-click marketing is so strategic, how can it be automated? That’s why we developed Rules-Based Bidding. Rules-Based Bidding allows you to apply the kind of rules you would use if you were managing your bids manually.” Atlas http://www.atlasonepoint.com/products/bidmanager/rulesbased

Thanks: M. Schwarz

Yahoo! Confidential

Bidders’ actual strategiesSource: M. Schwarz

Yahoo! Confidential

Models of GSP

1. Static game of complete information

2. Generalized English Auction (simple dynamic model)

More realistic model

• Each period one random bidder can change his bid

• Before the move a bidder observes all standing bids

Source: M. Schwarz

Yahoo! Confidential

Pragmatic Robot (PR)

• Find current optimal position iImplies range of possible bids: Static best response (BR set)

• Choose envy-free point inside BR set:Bid up to point of indifference between position i and position i-1

• If start in equilibrium PRs stay in equilibrium

Source: M. Schwarz

Yahoo! Confidential

Convergence of PRSimulation

0 100 200 300 400 500 600 700 8000.2

0.4

0.6

0.8

1

1.2

1.4

1.6

simulation rounds - convergence to 0.000001 after 329 iterations

Total Surplus Search Engine RevenueAdvertiser Surplus Computed Equilibrium

Source: M. Schwarz

Yahoo! Confidential

Convergence of PRSource: M. Schwarz

Yahoo! Confidential

Convergence of PR

• The fact that PR converges supports the assertion that the equilibrium of a simple model informs us about the outcome of intractable dynamic game that inspired it

Complex game that we can not solve

Simple model inspired by a complex game

?

Source: M. Schwarz

Yahoo! Confidential

Playing with Ideal Subjects

Largest Gap (commercially available strategy)Moves your keyword listing to the largest bid gap within a specified set of positions

Regime One: 15 robots all play Largest Gap

Regime Two: one robot becomes pragmatic

By becoming Pragmatic pay off is up 16%Other assumptions: values are log normal, mean valuation 1, std dev 0.7 of the underlying normal, bidders move sequentially in random order

Source: M. Schwarz

Yahoo! Confidential

ROI

• Setting ROI target is a popular strategy

• For any ROI goal the advertiser who switches to pragmatic gets higher payoff

Source: M. Schwarz

Yahoo! Confidential

If others play ROI targeter

• Bidders 1,...,K-1 bid according to the ROI targeting strategy

• What is K’s best response?

bidder

bidder payoffs if bidder K plays

ROI targeting

PR

1

…

K-1

K 0.0387 0.0457

Source: M. Schwarz

Yahoo! Confidential

Reinforcement Learnervs Pragmatic Robot

• Pragmatic learner outperforms reinforcement learner (that we tried)

• Remark: reinforcement learning does not converge in a problem with big BR set

Source: M. Schwarz

Yahoo! Confidential

Conclusion

• A strategy inspired by theory seems useful in practice: PR beats commercially available strategies and other reasonable baselines

• Since PR converges and performs well, the equilibrium concept is sound in spite the fact that some theoretical assumptions are violated and there are plenty of players who are “irrational”

• When bidding agents are used for real economic decisions (e.g., search engine optimization), we have an ideal playground for empirical game theory simulations

Thanks: M. Schwarz

ResearchResearch

First Workshop on Sponsored Search Auctionsat ACM Electronic Commerce, 2005

Organizers:

Kursad Asdemir, University of Alberta Hemant Bharghava, University of California Davis Jane Feng, University of Florida Gary Flake, Microsoft David Pennock, Yahoo! Research

ResearchResearch

Papers

• Mechanism Design• Pay-Per-Percentage of Impressions: An Advertising

Method that is Highly Robust to Fraud, J.Goodman• Stochastic and Contingent-Payment Auctions,

C.Meek,D.M.Chickering, D.B.Wilson• Optimize-and-Dispatch Architecture for Expressive

Ad Auctions, D.Parkes, T.Sandholm• Sponsored Search Auction Design via Machine

Learning, M.-F. Balcan, A.Blum, J.D.Hartline, Y.Mansour• Knapsack Auctions, G.Aggarwal, J.D. Hartline• Designing Share Structure in Auctions of Divisible

Goods, J.Chen, D.Liu, A.B.Whinston

•

•

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Papers

• Bidding Strategies• Strategic Bidder Behavior in Sponsored Search Auctions,

Benjamin Edelman, Michael Ostrovsky• A Formal Analysis of Search Auctions Including

Predictions on Click Fraud and Bidding Tactics, B.Kitts, P.Laxminarayan, B.LeBlanc, R.Meech

• User experience• Examining Searcher Perceptions of and Interactions with

Sponsored Results, B.J.Jansen, M. Resnick• Online Advertisers' Bidding Strategies for Search,

Experience, and Credence Goods: An Empirical Investigation, A.Animesh, V. Ramachandran,

• S.Vaswanathan•

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Stochastic Auctions C.Meek,D.M.Chickering, D.B.Wilson

• Ad ranking allocation rule is stochastic

• Why?• Reduces incentive for “bid jamming”• Naturally incorporates explore/exploit mix• Incentive for low value bidders to join/stay?

• Derive truthful pricing rule

• Investigate contingent-payment auctions:Pay per click, pay per action, etc.

• Investigate bid jamming, exploration strategies

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Expressive Ad AuctionsD.Parkes, T.Sandholm

• Propose expressive bidding semantics for ad auctions (examples next)• Good: Incr. economic efficiency, incr. revenue• Bad: Requires combinatorial optimization;

Ads need to be displayed within milliseconds

• To address computational complexity, propose “optimize and dispatch” architecture: Offline scheduler “tunes” an online (real-time) dispatcher

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Expressive bidding I

• Multi-attribute bidding

Advertiser1

Advertiser2

Male users (50%)

$1 $2

Female users (50%)

$2 $1

Un-differentiated

$1.50 $1.50

Advertiser1

Advertiser2

Pre-qualified (50%)

$2 $2

Other (50%) $1 $1

Un-differentiated

$1.50 $1.50

ResearchResearch

Expressive bidding II


3 x .05 = .15

1 x .05 = .05

b xCTR = RPS

ResearchResearch

Expressive bidding II


4 x .07 = .28

b xCTR = RPS

monopoly bid

ResearchResearch

Expressive bidding III

• Guaranteed future delivery• Decreasing/increasing marginal value• All or nothing bids• Pay per: impression, click, action, ...• Type/id of distribution site (content match)• Complex search query properties• Algo results properties (“piggyback bid”)• Ad infinitum• Keys: What advertisers want; what

advertisers value differently; controlling cognitive burden; computational complexity

Second Workshop on Sponsored Search Auctions

Kursad Asdemir, University of Alberta

Jason Hartline, Microsoft Research

Brendan Kitts, Microsoft

Chris Meek, Microsoft Research

Organizing Committee

Source: K. Asdemir

Objectives

Diversity Participants

Industry: Search engines and search engine marketers Academia: Engineering, business, economics schools

Approaches Mechanism Design Empirical Data mining / machine learning

New Ideas

Source: K. Asdemir

History & Overview

First Workshop on S.S.A. Vancouver, BC 2005 ~25 participants 10 papers + Open discussion 4 papers from Microsoft Research

Second Workshop on S.S.A. ~40-50 participants 10 papers + Panel 3 papers from Yahoo! Research

Source: K. Asdemir

Participants

Industry Yahoo!, Microsoft, Google Iprospect (Isobar), Efficient Frontier, HP Labs, Bell

Labs, CommerceNet

Academia Several schools

Source: K. Asdemir

Papers Mechanism design

Edelman, Ostrovsky, and Schwarz Iyengar and Kumar Liu, Chen, and Whinston Borgs et al.

Bidding behavior Zhou and Lukose Szymanski and Lee Asdemir Borgs et al.

Data mining Regelson and Fain Sebastian, Bartz, and Murthy

Source: K. Asdemir

Panel: Models of Sponsored Search:What are the Right Questions? Proposed by

Lance Fortnow and Rakesh Vohra

Panel members Kamal Jain, Microsoft Research Rakesh Vohra, Northwestern University Michael Schwarz, Yahoo! Inc David Pennock, Yahoo! Inc

Source: K. Asdemir

Panel Discussions Mechanisms

Competition between mechanisms Ambiguity vs Transparency: “Pricing” versus “auctions” Involving searchers

Budget Hard or a soft constraint Flighting (How to spend the budget over time?)

Pay-per-what? CPM, CPC, CPS Risk sharing Fraud resistance

Transcript available!

Source: K. Asdemir

ResearchResearch

Web resources

• 1st Workshop website & papers:http://research.yahoo.com/workshops/ssa2005/

• 1st Workshop notes (by Rohit Khare):http://wiki.commerce.net/wiki/RK_SSA_WS_Notes

• 2nd Workshop website & papers:http://www.bus.ualberta.ca/kasdemir/ssa2/

• 2nd Workshop panel transcript:(thanks Hartline & friends!)http://research.microsoft.com/~hartline/papers/panel-SSA-06.pdf

• 3rd Workshop websitehttp://opim-sun.wharton.upenn.edu/ssa3/index.html

• 4th Workshop websitehttp://research.yahoo.com/workshops/adauctions2008/

More Challenges

• Unifying search, display, content, offline

• Economics of attention

• Directly rewarding users, control, privacy3-party game theoretic equilibrium

• Predicting click through rates

• Detecting spam/fraud

• Pay per “action” / conversion

• Number/location/size of of ads

• Improved targeting / expressiveness

• $15B Question: Monetizing social networks, user-generated content

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Prediction Markets

David Pennock, Yahoo! Research

ResearchResearch

Bet = Credible Opinion

• Which is more believable?More Informative?

• Betting intermediaries• Las Vegas, Wall Street, Betfair, Intrade,...• Prices: stable consensus of a large

number of quantitative, credible opinions• Excellent empirical track record

Obama will win the 2008 US Presidential election

“I bet $100 Obama will win at 1 to 2 odds”

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A Prediction Market

• Take a random variable, e.g.

• Turn it into a financial instrument payoff = realized value of variable

$1 if $0 if

I am entitled to:

Bird Flu Outbreak US 2008?(Y/N)

Bird FluUS ’08

Bird FluUS ’08

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http://intrade.com



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Prediction Markets:Examples & Research

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The Wisdom of CrowdsBacked in dollars• What you can say/learn

% chance that• Obama wins• GOP wins Texas• YHOO stock > 30• Duke wins tourney• Oil prices fall• Heat index rises• Hurricane hits Florida• Rains at place/time

• Where

• IEM, Intrade.com• Intrade.com• Stock options market• Las Vegas, Betfair• Futures market• Weather derivatives• Insurance company• Weatherbill.com

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Prediction MarketsWith Money Without





















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The Widsom of CrowdsBacked in “Points”• HSX.com• Newsfutures.com• InklingMarkets.com• Foresight Exchange• CasualObserver.net• FTPredict.com• Yahoo!/O’Reilly Tech Buzz• ProTrade.com• StorageMarkets.com• TheSimExchange.com• TheWSX.com• Alexadex, Celebdaq, Cenimar, BetBubble, Betocracy, CrowdIQ,

MediaMammon,Owise, PublicGyan, RIMDEX, Smarkets, Trendio, TwoCrowds

• http://www.chrisfmasse.com/3/3/markets/#Play-Money_Prediction_Markets

http://tradesports.com

http://betfair.com



Screen capture 2007/05/18



Screen capture 2008/05/07



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Example: IEM 1992

[Source: Berg, DARPA Workshop, 2002]

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Example: IEM

[Source: Berg, DARPA Workshop, 2002]

Does it work? Yes, evidence from real markets, laboratory

experiments, and theory Racetrack odds beat track experts [Figlewski 1979] Orange Juice futures improve weather forecast [Roll 1984] I.E.M. beat political polls 451/596 [Forsythe 1992, 1999][Oliven

1995][Rietz 1998][Berg 2001][Pennock 2002]

HP market beat sales forecast 6/8 [Plott 2000]

Sports betting markets provide accurate forecasts of game outcomes [Gandar 1998][Thaler 1988][Debnath EC’03][Schmidt 2002]

Laboratory experiments confirm information aggregation[Plott 1982;1988;1997][Forsythe 1990][Chen, EC’01]

Theory: “rational expectations” [Grossman 1981][Lucas 1972]

Market games work [Servan-Schreiber 2004][Pennock 2001]

[Thanks: Yiling Chen]

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Prediction Markets:Does Money Matter?

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The Wisdom of CrowdsWith Money Without

IEM: 237 Candidates HSX: 489 Movies

1 2 5 10 20 50 100estimate

1

2

5

10

20

50

100

actual

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The Wisdom of CrowdsWith Money Without

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Real markets vs. market gamesHSX FX, F1P6

probabilisticforecasts

forecast source avg log scoreF1P6 linear scoring -1.84F1P6 F1-style scoring -1.82betting odds -1.86F1P6 flat scoring -2.03F1P6 winner scoring -2.32

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Does money matter? Play vs real, head to headExperiment• 2003 NFL Season• ProbabilitySports.com

Online football forecasting competition

• Contestants assess probabilities for each game

• Quadratic scoring rule• ~2,000 “experts”, plus:• NewsFutures (play $)• Tradesports (real $)

• Used “last trade” prices

Results:• Play money and real

money performed similarly• 6th and 8th respectively

• Markets beat most of the ~2,000 contestants• Average of experts

came 39th (caveat)

Electronic Markets, Emile Servan-Schreiber, Justin Wolfers, David Pennock and Brian Galebach

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0

25

50

75

100

TradeSports Prices

0 20 40 60 80 100NewsFutures Prices

Fitted Value: Linear regression

45 degree line

n=416 over 208 NFL games.Correlation between TradeSports and NewsFutures prices = 0.97

Prices: TradeSports and NewsFutures

Prediction Performance of MarketsRelative to Individual Experts

020406080

100120140160180200220240260280300

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Week into the NFL season

Rank

NewsFutures

Tradesports

0

10

20

30

40

50

60

70

80

90

100

Observed Frequency of Victory

0 10 20 30 40 50 60 70 80 90 100Trading Price Prior to Game

TradeSports: Correlation=0.96NewsFutures: Correlation=0.94

Data are grouped so that prices are rounded to the nearest ten percentage points; n=416 teams in 208 games

Market Forecast Winning Probability and Actual Winning ProbabilityPrediction Accuracy

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Does money matter? Play vs real, head to head

Probability-Football Avg

TradeSports(real-money)

NewsFutures(play-money)

DifferenceTS - NF

Mean Absolute Error

= lose_price

[lower is better]

0.443

(0.012)

0.439

(0.011)

0.436

(0.012)

0.003

(0.016)

Root Mean Squared Error

= ?Average( lose_price2 )

[lower is better]

0.476

(0.025)

0.468

(0.023)

0.467

(0.024)

0.001

(0.033)

Average Quadratic Score

= 100 - 400*( lose_price2 )

[higher is better]

9.323

(4.75)

12.410

(4.37)

12.427

(4.57)

-0.017

(6.32)

Average Logarithmic Score

= Log(win_price)

[higher (less negative) is better]

-0.649

(0.027)

-0.631

(0.024)

-0.631

(0.025)

0.000

(0.035)

Statistically:TS ~ NFNF >> AvgTS > Avg

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A Problem w/ Virtual CurrencyPrinting Money

Alice1000

Betty1000

Carol1000

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A Problem w/ Virtual CurrencyPrinting Money

Alice5000

Betty1000

Carol1000

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YootlesA Social Currency

Alice0

Betty0

Carol0

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I owe you 5

Alice-5

Betty0

Carol5

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credit: 5 credit: 10

I owe you 5

Alice-5

Betty0

Carol5

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I owe you 5 I owe you 5

Alice-5

Betty0

Carol5

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I owe you 5 I owe you 5

Alice3995

Betty0

Carol5

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YootlesA Social Currency• For tracking gratitude among friends• A yootle says “thanks, I owe you one”

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Combinatorial Betting

ResearchResearchCombinatorics ExampleMarch Madness

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Combinatorics ExampleMarch Madness• Typical today

Non-combinatorial• Team wins Rnd 1• Team wins Tourney• A few other “props”• Everything explicit

(By def, small #)• Every bet indep:

Ignores logical & probabilistic relationships

• Combinatorial• Any property• Team wins Rnd k

Duke > {UNC,NCST}ACC wins 5 games

• 2264 possible props(implicitly defined)

• 1 Bet effects related bets “correctly”;e.g., to enforce logical constraints

Expressiveness:Getting Information

• Things you can say today:– (43% chance that) Hillary wins

– GOP wins Texas

– YHOO stock > 30 Dec 2007

– Duke wins NCAA tourney

• Things you can’t say (very well) today:– Oil down, DOW up, & Hillary wins

– Hillary wins election, given that she wins OH & FL

– YHOO btw 25.8 & 32.5 Dec 2007

– #1 seeds in NCAA tourney win more than #2 seeds

Expressiveness:Processing Information

• Independent markets today:– Horse race win, place, & show pools

– Stock options at different strike prices

– Every game/proposition in NCAA tourney

– Almost everything: Stocks, wagers, intrade, ...

• Information flow (inference) left up to traders

• Better: Let traders focus on predicting whatever they want, however they want: Mechanism takes care of logical/probabilistic inference

• Another advantage: Smarter budgeting

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Automated Market Makers

• A market maker (a.k.a. bookmaker) is a firm or person who is almost always willing to accept both buy and sell orders at some prices

• Why an institutional market maker? Liquidity! • Without market makers, the more expressive the betting

mechanism is the less liquid the market is (few exact matches)• Illiquidity discourages trading: Chicken and egg• Subsidizes information gathering and aggregation: Circumvents

no-trade theorems

• Market makers, unlike auctioneers, bear risk. Thus, we desire mechanisms that can bound the loss of market makers

• Market scoring rules [Hanson 2002, 2003, 2006]

• Dynamic pari-mutuel market [Pennock 2004]

[Thanks: Yiling Chen]

Overview: Complexity Results

Permutations Boolean

General Pair Subset General 2-clause Restrict Tourney

Call Market

NP-hard NP-hard Poly co-NP-complete

? ?

Market Maker

(LMSR)

#P-hard #P-hard #P-hard #P-hard #P-hard Poly

ResearchResearch

New Prediction Game

ResearchResearch

Mech Design for Prediction

Financial Markets Prediction Markets

Primary Social welfare (trade)Hedging risk

Information aggregation

Secondary Information aggregation Social welfare (trade)Hedging risk

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Mech Design for Prediction

• Standard Properties• Efficiency• Inidiv. rationality• Budget balance• Revenue• Truthful (IC)• Comp. complexity

• Equilibrium• General, Nash, ...

• PM Properties• #1: Info aggregation• Expressiveness• Liquidity• Bounded budget• Truthful (IC)• Indiv. rationality• Comp. complexity

• Equilibrium• Rational

expectations

Competes with:experts, scoringrules, opinionpools, ML/stats,polls, Delphi

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Discussion

• Are incentives for virtual currency strong enough?• Yes (to a degree)• Conjecture: Enough to get what people already know;

not enough to motivate independent research• Reduced incentive for information discovery possibly

balanced by better interpersonal weighting

• Statistical validations show HSX, FX, NF are reliable sources for forecasts• HSX predictions >= expert predictions• Combining sources can help

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Catalysts

• Markets have long history of predictive accuracy: why catching on now as tool?

• No press is bad press: Policy Analysis Market (“terror futures”)

• Surowiecki's “Wisdom of Crowds”• Companies:

• Google, Microsoft, Yahoo!; CrowdIQ, HSX, InklingMarkets, NewsFutures

• Press: BusinessWeek, CBS News, Economist, NYTimes, Time, WSJ, ...http://us.newsfutures.com/home/articles.html

CFTC Role

• MayDay 2008: CFTC asks for help

• Q: What to do with prediction markets?

• Right now, the biggest prediction markets are overseas, academic (1), or just for fun

• CFTC may clarify, drive innovation

• Or not

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Conclusion

• Prediction Markets:hammer = market, nail = prediction• Great empirical successes• Momentum in academia and industry• Fascinating (algorithmic) mechanism design

questions, including combinatorial betting

• Points-paid peers produce prettygood predictions

research internet advertising auctions david pennock, yahoo! research - new york contributed slides:...

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