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Internet Advertising Auctions. David Pennock , Yahoo! Research - New York Contributed slides: K.Asdemir, H.Bhargava, J.Feng, S.Lahaie , M.Schwarz. Advertising Then and Now. Then: Think real estate Phone calls Manual negotiation “Half doesn’t work”.

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internet advertising auctions

Internet Advertising Auctions

David Pennock, Yahoo! Research - New York

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

advertising then and now
Advertising Then and Now
  • Then: Think real estatePhone 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
advertising then now video
Advertising Then & Now: Video

advertising now tools disciplines

Machine learning



Economics &Computer Science

Statistics &Computer Science

Operations Research Computer Science


Advertising: NowTools Disciplines
sponsored search auctions

search “las vegas travel”, Yahoo!

“las vegas travel” auction

Sponsored search auctions

Space next to search results is sold at auction

  • 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

216 million/month

Google / Yahoo!

11 billion/month (US)

Auctions Applications
newsweek june 17 2002 the united states of ebay
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
“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
Online ad industry revenue

introduction to sponsored search

Introduction tosponsored search

What is it?

Brief and biased history

Allocation and pricing: Google vs Yahoo!

Incentives and equilibrium

sponsored search auctions15

search “las vegas travel”, Yahoo!

“las vegas travel” auction

Sponsored search auctions

Space next to search results is sold at auction

sponsored search auctions16
Sponsored search auctions
  • 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 auctions17
Sponsored search auctions
  • 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
sponsored search today
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 search a brief biased history
Sponsored SearchA Brief & Biased History
  • Idealab  (no relation to
    • 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)
sponsored search a brief biased history21

Thanks: S. Lahaie

Sponsored SearchA Brief & Biased History
  • 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
sponsored search a brief biased history22
Sponsored SearchA Brief & Biased History
  • 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 research a brief biased history
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 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
yahoo allocation bid ranking
Yahoo Allocation: Bid Ranking

search “las vegas travel”, Yahoo!

“las vegas travel” auction

pays $2.95per click

pays $2.94

pays $1.02

... bidder ipays bidi+1+.01

google allocation ranking
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]

google allocation ranking27





Google Allocation: $ Ranking

search “las vegas travel”, Google

“las vegas travel” auction

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

x .1 = .301

x .2 = .588

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

x .1 = .293

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

x E[CTR] = E[RPS]

x E[CTR] = E[RPS]

aside second price auction vickrey auction
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

Only pays $120





incentive compatibility truthfulness
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
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)
is google pricing vcg
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.

vcg pricing
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
next price equilibrium
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
next price equilibrium34
Next-price equilibrium
  • Recursive solution:posi-1*advi*bi = (posi-1-posi)*advi*vi+posi*advi+1*bi+1bi = (posi-1-posi)*advi*vi+posi*advi+1*bi+1 posi-1*advi
  • Nomenclature:Next price = “generalized second price” (GSP)
ad exchanges35

Ad exchanges

Right Media


online advertising evolution
Online Advertising Evolution
  • Direct: Publishers sell owned & operated (O&O) inventory
  • Ad networks: Big publishers place ads on affiliate sites, share revenueAOL, Google, Yahoo!, Microsoft
  • Ad exchanges: Match buy orders from advertisers with sell orders from publishers and ad networksKey distinction: exchange does not “own” inventory
exchange basics







Six Apart








[Source: Ryan Christensen]

Exchange Basics
right media publisher experience

[Source: Ryan Christensen]

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

right media advertiser experience

[Source: Ryan Christensen]

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
  • “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 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

expressiveness example
Expressiveness Example
  • Competition constraints

b xCTR = RPS

3 x .05 = .15

1 x .05 = .05

expressiveness example42
Expressiveness Example

monopoly bid

  • Competition constraints

b xCTR = RPS

4 x .07 = .28

expressiveness design
Expressiveness: Design
  • Multi-attribute bidding
expressiveness less is more
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
expressiveness less is more45
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?
coming convergence ml and mechanism design






Coming Convergence:ML and Mechanism Design


e.g. Auction,Exchange, ...

ml inner loop
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.
an analysis of alternative slot auction designs for sponsored search

Source: S. Lahaie

An Analysis of Alternative Slot Auction Designs for Sponsored Search
  • Sebastien Lahaie, Harvard University*
  • *work partially conducted at Yahoo! Research
  • ACM Conference on Electronic Commerce, 2006

Source: S. Lahaie

  • 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

  • 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”)
the model

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:
the model cont d

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
auction formats

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

Source: S. Lahaie

  • 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).
first price bidding equilibria

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:
informational requirements

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.
revenue ranking

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 .
complete information nash equilibria

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.

characterization of equilibria

Source: S. Lahaie

Characterization of Equilibria
  • RBB: same characterization with replacing
exponential decay

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

  • 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.
future work

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”).
research problem online estimation of clickrates

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.
equilibrium revenue simulations of hybrid sponsored search mechanisms

Equilibrium revenue simulations of hybrid sponsored search mechanisms

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

David Pennock, Yahoo! Research

revenue effects
Revenue effects
  • What gives most revenue?
    • Key: If rules change, advertiser bids will change
    • Use Edelman et al. envy-free equilibrium solution


Highest bid wins

Google/Yahoo!Highest bid*CTR wins

HybridHighest bid*(CTR)s wins



s=1/2 ?

s=3/4 ?

monte carlo simulations

Source: S. Lahaie

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.
preliminary conclusions

Source: S. Lahaie

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
testing game theory

Thanks: M. Schwarz

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

models of gsp

Source: M. Schwarz

Models of GSP
  • Static game of complete information
  • 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
pragmatic robot pr

Source: M. Schwarz

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
convergence of pr81

Source: M. Schwarz

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


Simple model inspired by a complex game

Complex game that we can not solve

playing with ideal subjects

Source: M. Schwarz

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

  • Setting ROI target is a popular strategy
  • For any ROI goal the advertiser who switches to pragmatic gets higher payoff
if others play roi targeter

Source: M. Schwarz

If others play ROI targeter
  • Bidders 1,...,K-1 bid according to the ROI targeting strategy
  • What is K’s best response?
reinforcement learner vs pragmatic robot
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


Thanks: M. Schwarz

  • 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
first workshop on sponsored search auctions at acm electronic commerce 2005

First Workshop on Sponsored Search Auctionsat ACM Electronic Commerce, 2005


Kursad Asdemir, University of AlbertaHemant Bharghava, University of California DavisJane Feng, University of FloridaGary Flake, MicrosoftDavid Pennock, Yahoo! Research

  • 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
  • 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
stochastic auctions c meek d m chickering d b wilson
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
expressive ad auctions d parkes t sandholm
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
expressive bidding i
Expressive bidding I
  • Multi-attribute bidding
expressive bidding ii
Expressive bidding II
  • Competition constraints

b xCTR = RPS

3 x .05 = .15

1 x .05 = .05

expressive bidding ii94
Expressive bidding II

monopoly bid

  • Competition constraints

b xCTR = RPS

4 x .07 = .28

expressive bidding iii
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

Source: K. Asdemir

Second Workshop on Sponsored Search Auctions

Organizing Committee

Kursad Asdemir, University of Alberta

Jason Hartline, Microsoft Research

Brendan Kitts, Microsoft

Chris Meek, Microsoft Research


Source: K. Asdemir

  • Diversity
    • Participants
        • Industry: Search engines and search engine marketers
        • Academia: Engineering, business, economics schools
    • Approaches
        • Mechanism Design
        • Empirical
        • Data mining / machine learning
  • New Ideas
history overview

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

  • Industry
    • Yahoo!, Microsoft, Google
    • Iprospect (Isobar), Efficient Frontier, HP Labs, Bell Labs, CommerceNet
  • Academia
    • Several schools

Source: K. Asdemir

  • 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
panel models of sponsored search what are the right questions

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
panel discussions

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!
web resources
Web resources
  • 1st Workshop website & papers:
  • 1st Workshop notes (by Rohit Khare):
  • 2nd Workshop website & papers:
  • 2nd Workshop panel transcript:(thanks Hartline & friends!)
  • 3rd Workshop website
  • 4th Workshop website
more challenges
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
prediction markets

Prediction Markets

David Pennock, Yahoo! Research

bet credible opinion
Bet = Credible Opinion

Obama will win the 2008 US Presidential election

  • 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

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

a prediction market
A Prediction Market
  • Take a random variable, e.g.
  • Turn it into a financial instrument payoff = realized value of variable

Bird Flu Outbreak US 2008?(Y/N)

I am entitled to:

Bird FluUS ’08

Bird FluUS ’08

$1 if

$0 if

the wisdom of crowds backed 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



Stock options market

Las Vegas, Betfair

Futures market

Weather derivatives

Insurance company

The Wisdom of CrowdsBacked in dollars
the widsom of crowds backed in points
The Widsom of CrowdsBacked in “Points”
  • Foresight Exchange
  • Yahoo!/O’Reilly Tech Buzz
  • Alexadex, Celebdaq, Cenimar, BetBubble, Betocracy, CrowdIQ, MediaMammon,Owise, PublicGyan, RIMDEX, Smarkets, Trendio, TwoCrowds

Screen capture 2008/05/07

Screen capture 2007/05/18

does it work

[Thanks: Yiling Chen]

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]
real markets vs market games


Real markets vs. market games


FX, F1P6

forecast source avg log score

F1P6 linear scoring -1.84

F1P6 F1-style scoring -1.82

betting odds -1.86

F1P6 flat scoring -2.03

F1P6 winner scoring -2.32

does money matter play vs real head to head

2003 NFL Season 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


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)

Does money matter? Play vs real, head to head

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

does money matter play vs real head to head124
Does money matter? Play vs real, head to head

Statistically:TS ~ NFNF >> Avg

TS > Avg

yootles a social currency
YootlesA Social Currency




yootles a social currency128
YootlesA Social Currency

I owe you 5




yootles a social currency129
YootlesA Social Currency

I owe you 5

credit: 5

credit: 10




yootles a social currency130
YootlesA Social Currency

I owe you 5

I owe you 5

credit: 5

credit: 10




yootles a social currency131
YootlesA Social Currency

I owe you 5

I owe you 5

credit: 5

credit: 10




yootles a social currency132
YootlesA Social Currency
  • For tracking gratitude among friends
  • A yootle says “thanks, I owe you one”
combinatorics example march madness135
Typical todayNon-combinatorial

Team wins Rnd 1

Team wins Tourney

A few other “props”

Everything explicit(By def, small #)

Every bet indep: Ignores logical & probabilistic relationships


Any property

Team wins Rnd kDuke > {UNC,NCST}ACC wins 5 games

2264 possible props(implicitly defined)

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

Combinatorics ExampleMarch Madness
expressiveness getting information
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
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
automated market makers

[Thanks: Yiling Chen]

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]
mech design for prediction142
Standard Properties


Inidiv. rationality

Budget balance


Truthful (IC)

Comp. complexity


General, Nash, ...

PM Properties

#1: Info aggregation



Bounded budget

Truthful (IC)

Indiv. rationality

Comp. complexity


Rational expectations

Mech Design for Prediction

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

  • 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
  • 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, ...
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
  • 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