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Research 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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Presentation on theme: "Research Internet Advertising Auctions David Pennock, Yahoo! Research - New York Contributed slides: K.Asdemir, H.Bhargava, J.Feng, S.Lahaie, M.Schwarz."— Presentation transcript:

1 Research Internet Advertising Auctions David Pennock, Yahoo! Research - New York Contributed slides: K.Asdemir, H.Bhargava, J.Feng, S.Lahaie, M.Schwarz

2 Research Advertising Then and Now Then: Think real estate Phone calls Manual negotiation “Half doesn’t work” Now: Think Wall Street Automation, automation, automation Advertisers buy contextual attention: User i on page j at time t Computer learns what ad is best Computer mediates ad sales: Auction! Computer measures which ads work

3 Research Advertising Then & Now: Video

4 Research Advertising: Now Tools Disciplines Auctions Machine learning Optimization Sales Economics & Computer Science Statistics & Computer Science Operations Research Computer Science Marketing

5 search “las vegas travel”, Yahoo! Sponsored search auctions Space next to search results is sold at auction “las vegas travel” auction

6 Ad exchanges

7 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

8 Auctions Applications eBay –216 million/month Google / Yahoo! –11 billion/month (US)

9 Auctions Applications eBayGoogle

10 Auctions Applications eBayGoogle

11 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.”

12 “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,...

13 Online ad industry revenue

14 Research Introduction to sponsored search What is it? Brief and biased history Allocation and pricing: Google vs Yahoo! Incentives and equilibrium

15 search “las vegas travel”, Yahoo! Sponsored search auctions Space next to search results is sold at auction “las vegas travel” auction

16 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”)

17 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

18 Example price volatility: Vioxx

19 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

20 Sponsored Search A 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)

21 Sponsored Search A 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 Thanks: S. Lahaie

22 Sponsored Search A 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)

23 Sponsored Search Research A 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, & Pennock Implementing 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, & Vaziran AdWords 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 Auctions 4th Workshop on Ad Auctions -- Chicago Julu 8-9, 2008

24 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

25 Research Yahoo Allocation: Bid Ranking “las vegas travel” auction search “las vegas travel”, Yahoo! pays $2.95 per click pays $2.94 pays $1.02... bidder i pays bid i+1 +.01

26 Research Google Allocation: $ Ranking “las vegas travel” auction x E[CTR] = E[RPS]

27 Research 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] TripReservations Expedia pays 3.01*.1/.2+.01 = 1.51 per click pays 2.93*.1/.1+.01 = 2.94 pays bid i+1 *CTR i+1 /CTR i +.01 LVGravityZone etc...

28 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

29 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

30 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)

31 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.

32 Yahoo! Confidential VCG pricing (sum of everyone else’s value w/o bidder) - (sum of everyone else’s value with bidder) CTR i = adv i * pos i (key “separability” assumption) price i = 1/adv i *(∑ j i bid j *adv j *pos j-1 -∑ j≠i bid j *CTR j ) = 1/adv i *(∑ j>i bid j *adv j *pos j-1 - ∑ j>i bid j *CTR j ) Notes –For truthful Y! ranking set adv i = 1. But Y! ranking technically not VCG because not efficient allocation. –Last position may require special handling

33 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

34 Yahoo! Confidential Next-price equilibrium Recursive solution: pos i-1 *adv i *b i = (pos i-1 -pos i )*adv i *v i +pos i *adv i+1 *b i+1 b i = (pos i-1 -pos i )*adv i *v i +pos i *adv i+1 *b i+1 pos i-1 *adv i Nomenclature: Next price = “generalized second price” (GSP)

35 Research Ad exchanges Right Media Expressiveness

36 Research Online Advertising Evolution 1.Direct: Publishers sell owned & operated (O&O) inventory 2.Ad networks: Big publishers place ads on affiliate sites, share revenue AOL, Google, Yahoo!, Microsoft 3.Ad exchanges: Match buy orders from advertisers with sell orders from publishers and ad networks Key distinction: exchange does not “own” inventory

37 Yahoo! Confidential Exchange Basics Exchange Demand Inventory Netflix Vonage … Advertisers CPX Tribal … Networks MySpace Six Apart Looksmart Monster … Publishers[Source: Ryan Christensen]

38 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 [Source: Ryan Christensen]

39 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 [Source: Ryan Christensen]

40 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 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

41 Research Expressiveness Example Competition constraints 3 x.05 =.15 1 x.05 =.05 b xCTR = RPS

42 Research Expressiveness Example Competition constraints 4 x.07 =.28 b xCTR = RPS monopoly bid

43 Research Expressiveness: Design Multi-attribute bidding Advertiser 1 Advertiser 2 Male users (50%) $1$2 Female users (50%) $2$1 Un- differentiated $1.50 Advertiser 1 Advertiser 2 Pre-qualified (50%) $2 Other (50%)$1 Un- differentiated $1.50

44 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 0-100 Pay in proportion –Network learns, optimizes traffic, repeat Fraud: Short-term gain only: If advertisers lie, they stop getting traffic

45 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?

46 Research Coming Convergence: ML and Mechanism Design Mechanism (Rules) e.g. Auction, Exchange,... Stats/ML/Opt Engine

47 Research 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.

48 Research Selected Survey of Internet Advertising Research

49 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

50 Slot Auctions Every time a search is performed on a keyword on Yahoo! and Google, an auction is cleared that determines ads alongside. An auction allows for automatic price discovery. The good being sold is the attention of a user in an appropriate “intentional stance”. Payment is “per click”, as opposed to “per impression” or “per conversion”. Source: S. Lahaie

51 Sponsored Search In 2005, roughly 80% of Google’s revenue and 45% of Yahoo!’s revenue likely came from sponsored search (estimates using Yahoo! Finance and Nielsen/NetRatings). The combined market cap of Yahoo! and Google is $150 billion. In 2004, industry-wide sponsored search revenues were $3.9 billion, or 40% of total Internet advertising revenues (PricewaterhouseCoopers). Source: S. Lahaie

52 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

53 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

54 Related Work [Feng et al. ’05] compare the revenue performance of various ranking mechanisms via simulations. [Liu and Chen ’05] study slot auction mechanisms with a single slot, where agents have binary types. [Edelman et al. ’05] study the “locally envy-free” equilibria of slot auctions and their revenue properties. [Varian ’06] gives bounds used to infer bidder values given their bids. Source: S. Lahaie

55 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 CTR i,k = Bidder i’s utility function is quasi-linear: Source: S. Lahaie

56 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

57 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

58 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 6 1 4 Source: S. Lahaie

59 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). 0.56 14 Source: S. Lahaie

60 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

61 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

62 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

63 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

64 Characterization of Equilibria RBB: same characterization with replacing Source: S. Lahaie

65 Price of Anarchy Define: Source: S. Lahaie

66 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

67 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

68 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

69 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

70 Research Equilibrium revenue simulations of hybrid sponsored search mechanisms Sebastien Lahaie, Harvard University* *work conducted at Yahoo! Research David Pennock, Yahoo! Research

71 Yahoo! Confidential Revenue effects What gives most revenue? –Key: If rules change, advertiser bids will change –Use Edelman et al. envy-free equilibrium solution Overture Highest bid wins Google/Yahoo! Highest bid*CTR wins s=0 s=1/2 ? s=1 s=3/4 ? Hybrid Highest bid*(CTR) s wins

72 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

73 Yahoo! Confidential Source: S. Lahaie

74 Yahoo! Confidential Source: S. Lahaie

75 Yahoo! Confidential Source: S. Lahaie

76 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

77 Pragmatic Robots and Equilibrium Bidding in GSP Auctions Michael Schwarz, Yahoo! Research Ben Edelman, Harvard University Source: M. Schwarz

78 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 Thanks: M. Schwarz

79 Yahoo! Confidential Bidders’ actual strategies Source: M. Schwarz

80 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

81 Yahoo! Confidential Background Information Envy free point: a player is indifferent between being at his position or moving up one position and paying his bid Equilibrium bids are at envy free points b i = s i - (s i - b k+1 ) b i,,s i and α i are bid, value, and expected number of clicks respectively α k α k-1 Source: M. Schwarz

82 Yahoo! Confidential Pragmatic Robot (PR) Find current optimal position i Implies 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

83 Yahoo! Confidential Convergence of PR Simulation Source: M. Schwarz

84 Yahoo! Confidential Convergence of PR Source: M. Schwarz

85 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

86 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

87 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

88 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 K0.03870.0457 Source: M. Schwarz

89 Yahoo! Confidential Strange Strategy Strange Strategy: bid one cent if everybody bids one cent, bid $10000 if at lest one bidder bids more than one cent. Strange Strategy beats PR PR beats Strange Strategy Source: M. Schwarz

90 Yahoo! Confidential Reinforcement Learner vs 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

91 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

92 Research First Workshop on Sponsored Search Auctions at 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

93 Research 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

94 Research 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

95 Research 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

96 Research Expressive Ad Auctions D.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

97 Research Expressive bidding I Multi-attribute bidding Advertiser 1 Advertiser 2 Male users (50%) $1$2 Female users (50%) $2$1 Un- differentiated $1.50 Advertiser 1 Advertiser 2 Pre-qualified (50%) $2 Other (50%)$1 Un- differentiated $1.50

98 Research Expressive bidding II Competition constraints 3 x.05 =.15 1 x.05 =.05 b xCTR = RPS

99 Research Expressive bidding II Competition constraints 4 x.07 =.28 b xCTR = RPS monopoly bid

100 Research 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

101 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

102 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

103 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

104 Participants Industry  Yahoo!, Microsoft, Google  Iprospect (Isobar), Efficient Frontier, HP Labs, Bell Labs, CommerceNet Academia  Several schools Source: K. Asdemir

105 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

106 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

107 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

108 Research Web resources 1st Workshop website & papers: 1st Workshop notes (by Rohit Khare): 2nd Workshop website & papers: 2nd Workshop panel transcript: (thanks Hartline & friends!) panel-SSA-06.pdf 3rd Workshop website 4th Workshop website

109 More Challenges Unifying search, display, content, offline Economics of attention Directly rewarding users, control, privacy 3-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

110 Research Prediction Markets David Pennock, Yahoo! Research

111 Research 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”

112 Research 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 Flu US ’08

113 Research

114 Research Prediction Markets: Examples & Research

115 Research 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 Where IEM, Stock options market Las Vegas, Betfair Futures market Weather derivatives Insurance company

116 Research Prediction Markets With Money Without

117 Research The Widsom of Crowds Backed in “Points” Foresight Exchange Yahoo!/O’Reilly Tech Buzz Alexadex, Celebdaq, Cenimar, BetBubble, Betocracy, CrowdIQ, MediaMammon,Owise, PublicGyan, RIMDEX, Smarkets, Trendio, TwoCrowds

118 Screen capture 2007/05/18 Screen capture 2008/05/07

119 Research Example: IEM 1992 [Source: Berg, DARPA Workshop, 2002]

120 Research Example: IEM [Source: Berg, DARPA Workshop, 2002]

121 Research Example: IEM [Source: Berg, DARPA Workshop, 2002]

122 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]

123 Research Prediction Markets: Does Money Matter?

124 Research The Wisdom of Crowds With Money Without IEM: 237 CandidatesHSX: 489 Movies

125 Research The Wisdom of Crowds With Money Without

126 Research Real markets vs. market games HSXFX, F1P6 probabilistic forecasts forecast sourceavg 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

127 Research Does money matter? Play vs real, head to head Experiment 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 Results: Play money and real money performed similarly 6 th and 8 th respectively Markets beat most of the ~2,000 contestants Average of experts came 39 th (caveat) Electronic Markets, Emile Servan- Schreiber, Justin Wolfers, David Pennock and Brian Galebach

128 Research

129 Research Does money matter? Play vs real, head to head Statistically: TS ~ NF NF >> Avg TS > Avg

130 Research A Problem w/ Virtual Currency Printing Money Alice 1000 Betty 1000 Carol 1000

131 Research A Problem w/ Virtual Currency Printing Money Alice 5000 Betty 1000 Carol 1000

132 Research Yootles A Social Currency Alice 0 Betty 0 Carol 0

133 Research Yootles A Social Currency I owe you 5 Alice -5 Betty 0 Carol 5

134 Research Yootles A Social Currency credit: 5credit: 10 I owe you 5 Alice -5 Betty 0 Carol 5

135 Research Yootles A Social Currency credit: 5credit: 10 I owe you 5 Alice -5 Betty 0 Carol 5

136 Research Yootles A Social Currency credit: 5credit: 10 I owe you 5 Alice 3995 Betty 0 Carol 5

137 Research Yootles A Social Currency For tracking gratitude among friends A yootle says “thanks, I owe you one”

138 Research Combinatorial Betting

139 Research Combinatorics Example March Madness

140 Research 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 2 2 64 possible props (implicitly defined) 1 Bet effects related bets “correctly”; e.g., to enforce logical constraints

141 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

142 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

143 Research 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]

144 Overview: Complexity Results PermutationsBoolean GeneralPairSubsetGeneral2-clauseRestrict Tourney Call Market NP-hard Polyco-NP- complete ?? Market Maker (LMSR) #P-hard Poly

145 Research New Prediction Game

146 Research Mech Design for Prediction Financial MarketsPrediction Markets PrimarySocial welfare (trade) Hedging risk Information aggregation SecondaryInformation aggregationSocial welfare (trade) Hedging risk

147 Research 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, scoring rules, opinion pools, ML/stats, polls, Delphi

148 Research 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

149 Research 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,...

150 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

151 Research 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

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