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  • Multi-way interaction: game theory Yahoo! Is in the business of improving people’s utility to the point that they are willing to pay in some form. We improve users utility by giving them content, community, they pay in fees or in attention. We improve advertiser utility by given them users’ attention. To the extent that we can maximize user and advertiser value, our “take” will grow. Yahoo! Is about aligning incentives. Getting people to click where we want them to click. But you can’t do it by force. You have to create the mechanism such that the payers are glad to do what you wanted them to do, because it’s in their own interest. Mechanism design. Also, can’t go too far or they will defect. “ wisdom of crowds”
  • Why is incentive compatibility good? It make the buyers decision process much easier, perhaps encouraging them to participate. They need not endlessly strategize about what they other bidders are thinking, what the other bidders are thinking about what they are thinking, etc. They simply bid their true value.
  • No particular reason to think the equilibrium I told you about is one that bidders will actually converge to. Maybe, maybe not. Fudenberg & Levine: Partial Best Response
  • We started the workshop with an article on the history of the sponsored search auctions. Apart from this paper, papers from three main approaches were presented at the workshop. The mechanism design papers include Edelman, … . This is a widely cited paper that characterizes the equilibrium of the GSP currently in use. Iyengar and Kumar revenue maximizing and efficiency maximizing mechanisms Liu, chen, whinston not only on bid but with a signal
  • sponsored-search-upe..

    1. 1. Selected Survey of Sponsored Search Research at Yahoo! Research & the 1st & 2nd Workshops on Sponsored Search Auctions David Pennock , Yahoo! Research - New York Contributed slides: K.Asdemir, H.Bhargava, J.Feng, S.Lahaie , M.Schwarz
    2. 2. Sponsored search auctions <ul><li>Space next to search results is sold at auction </li></ul>search “las vegas travel”, Yahoo! “ las vegas travel” auction
    3. 3. Outline <ul><li>Yahoo! Research & microeconomics group </li></ul><ul><li>Motivation: Industry facts & figures </li></ul><ul><li>Introduction to sponsored search </li></ul><ul><ul><li>Brief and biased history </li></ul></ul><ul><ul><li>Allocation and pricing: Google vs Yahoo! </li></ul></ul><ul><ul><li>Incentives and equilibrium </li></ul></ul><ul><li>Selected survey of research at Yahoo! </li></ul><ul><ul><li>Mechanism design </li></ul></ul><ul><ul><ul><li>Analytic comparison of mechanisms [Lahaie] </li></ul></ul></ul>
    4. 4. Outline <ul><li>Selected survey of research at Yahoo! </li></ul><ul><ul><li>Mechanism Design (cont’d) </li></ul></ul><ul><ul><ul><li>Learning click rates: N-armed bandit formulation [Pandey & Olsten] </li></ul></ul></ul><ul><ul><ul><li>Simulation I: Static [Feng, Bhargava, Pennock] </li></ul></ul></ul><ul><ul><ul><li>Simulation II: Equilibrium [Lahaie, Pennock] </li></ul></ul></ul><ul><ul><li>Bidding agent design </li></ul></ul><ul><ul><ul><li>Pragmatic robot [Schwarz, Edelman] </li></ul></ul></ul>
    5. 5. Outline <ul><li>Brief summaries of the 1st & 2nd Workshops on Sponsored Search </li></ul><ul><li>Yahoo!/O’Reilly Tech Buzz Game </li></ul><ul><li>Not covered </li></ul><ul><ul><li>Sponsored search: budget optimization, click rate prediction, content match, engine switching, expressive bidding, intelligent match, interactivity, inventory prediction, keyword-advertiser graph clustering/recommendation, long-run effects, pricing, query classification, & more ... </li></ul></ul><ul><ul><li>General ad systems, algorithmic search, machine learning, other mechanism design problems </li></ul></ul>
    6. 6. Yahoo! Research <ul><li>New, growing, world-class researchers in search, machine learning, systems, UI, & microeconomics </li></ul><ul><li>Relatively open, connected to academia, yet grounded in real problems </li></ul><ul><li>Y!R-NYC in Manhattan: 9 scientists & growing Sub-concentrations: ML & microeconomics </li></ul><ul><li>Hiring interns & scientists </li></ul><ul><li>Academic outreach, visitors, collaborations Come visit us! </li></ul>
    7. 7. Microeconomics @ Y! <ul><li>Two faces of microeconomics </li></ul><ul><ul><li>Analysis of economic behavior Why Yahoo!?: Scale + data make possible entirely new science </li></ul></ul><ul><ul><li>Design of economic mechanisms Why Yahoo!?: Ad systems, Commerce, Community Incentives </li></ul></ul><ul><li>And now a third </li></ul><ul><ul><li>Computation : Internet infrastructure, Massive Scale, Optimization, Machine Learning / Stats </li></ul></ul>
    8. 8. Microeconomics @ Y!R <ul><li>Big Picture </li></ul><ul><ul><li>Yahoo! as unprecedented social system </li></ul></ul><ul><ul><li>Microecon toolbox: Probes and levers </li></ul></ul><ul><ul><li>Guidance for (massive) Eng/UI levers </li></ul></ul><ul><li>Examples </li></ul><ul><ul><li>Ad systems; Sponsored search </li></ul></ul><ul><ul><li>Matching: Personals, HotJobs, Ads </li></ul></ul><ul><ul><li>Commerce: Shopping, Local, Auctions </li></ul></ul><ul><ul><li>Points-based communities: Answers Reputation systems </li></ul></ul><ul><ul><li>Wisdom of Crowds </li></ul></ul>
    9. 9. Who <ul><li>Computationally-Literate Economists </li></ul><ul><li>Economically-Literate Computer Scientists </li></ul><ul><li>Yiling Chen, Rica Gonen, Mohammad Mahdian, David Pennock, Dan Reeves, Michael Schwarz </li></ul><ul><li>2006 Ph.D interns: Fong, Lahaie, Lee, Nikolova </li></ul><ul><li>Consulting: Levin, Milgrom, Ostrovsky </li></ul><ul><li>Courting: -------, -------, ------, ------, -------- </li></ul>
    10. 10. Who <ul><li>Computationally-Literate Economists </li></ul><ul><li>Economically-Literate Computer Scientists </li></ul><ul><li>Yiling Chen, Rica Gonen, Mohammad Mahdian, David Pennock, Dan Reeves, Michael Schwarz </li></ul><ul><li>2006 Ph.D interns: Fong, Lahaie, Lee, Nikolova </li></ul><ul><li>Consulting: Levin, Milgrom, Ostrovsky </li></ul><ul><li>Courting: -------, -------, ------, ------, -------- </li></ul>Lab Test: Hoping to Overtake Its Rivals, Yahoo Stocks Up on Academics Data-Rich Fantasy Land Economists and Search Gurus Fill New Research Team The Secret To Google's Success “Close-mouthed Google has opened up about AdWords since the three economists cracked its code”
    11. 11. Auctions: 2000 View <ul><li>Yesterday </li></ul><ul><li>“ Today” (~2000) </li></ul><ul><ul><li>eBay: 4 million; 450k new/day </li></ul></ul>Going once, … going twice, ...
    12. 12. Auctions: 2000 View <ul><li>Yesterday </li></ul><ul><li>“ Today” (~2000) </li></ul>
    13. 13. Auctions: 2000 View <ul><li>Yesterday </li></ul><ul><li>“ Today” (~2000) </li></ul>
    14. 14. Auctions: 2006 View <ul><li>Yesterday </li></ul><ul><ul><li>eBay </li></ul></ul><ul><ul><li>200 million/month </li></ul></ul><ul><li>Today </li></ul><ul><ul><li>Google / Yahoo! </li></ul></ul><ul><ul><li>6 billion/month (US) </li></ul></ul>
    15. 15. Auctions: 2006 View <ul><li>Yesterday </li></ul><ul><li>Today </li></ul>
    16. 16. Auctions: 2006 View <ul><li>Yesterday </li></ul><ul><li>Today </li></ul>
    17. 17. Newsweek June 17, 2002 “The United States of EBAY” <ul><li>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.” </li></ul><ul><li>“ 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.” </li></ul>
    18. 18. “The United States of Search” <ul><li>6 billion searches/month </li></ul><ul><li>50% of web users search every day </li></ul><ul><li>13% of traffic to commercial sites </li></ul><ul><li>40% of product searches </li></ul><ul><li>$5 billion 2005 US ad revenue (41% of US online ads; 2% of all US ads) </li></ul><ul><li>Doubling every year for four years </li></ul><ul><li>Search data: Covers nearly everything that people think about: intensions, desires, diversions, interests, buying habits, ... </li></ul>
    19. 19. Introduction to sponsored search <ul><li>What is it? </li></ul><ul><li>Brief and biased history </li></ul><ul><li>Allocation and pricing: Google vs Yahoo! </li></ul><ul><li>Incentives and equilibrium </li></ul>
    20. 20. Sponsored search auctions <ul><li>Space next to search results is sold at auction </li></ul>search “las vegas travel”, Yahoo! “ las vegas travel” auction
    21. 21. Sponsored search auctions <ul><li>Search engines auction off space next to search results, e.g. “digital camera” </li></ul><ul><li>Higher bidders get higher placement on screen </li></ul><ul><li>Advertisers pay per click: Only pay when users click through to their site; don’t pay for uncliked view (“impression”) </li></ul>
    22. 22. Sponsored search auctions <ul><li>Sponsored search auctions are dynamic and continuous: In principle a new “auction” clears for each new search query </li></ul><ul><li>Prices can change minute to minute; React to external effects, cyclical & non-cyc </li></ul><ul><ul><li>“ flowers” before Valentines Day </li></ul></ul><ul><ul><li>Fantasy football </li></ul></ul><ul><ul><li>People browse during day, buy in evening </li></ul></ul><ul><ul><li>Vioxx </li></ul></ul>
    23. 23. Example price volatility: Vioxx
    24. 24. Sponsored search today <ul><li>2005: ~ $7 billion industry </li></ul><ul><ul><li>2004: ~ $4B; 2003: ~ $2.5B; 2002: ~ $1B </li></ul></ul><ul><li>$5 billion 2005 US ad revenue (41% of US online ads; 2% of all US ads) </li></ul><ul><li>Resurgence in web search, web advertising </li></ul><ul><li>Online advertising spending still trailing consumer movement online </li></ul><ul><li>For many businesses, substitute for eBay </li></ul><ul><li>Like eBay, mini economy of 3rd party products & services: SEO, SEM </li></ul>
    25. 25. Sponsored Search A Brief & Biased History <ul><li>Idealab  GoTo.com (no relation to Go.com) </li></ul><ul><ul><li>Crazy (terrible?) idea, meant to combat search spam </li></ul></ul><ul><ul><li>Search engine “destination” that ranks results based on who is willing to pay the most </li></ul></ul><ul><ul><li>With algorithmic SEs out there, who would use it? </li></ul></ul><ul><li>GoTo   Yahoo! Search Marketing </li></ul><ul><ul><li>Team w/ algorithmic SE’s, provide “sponsored results” </li></ul></ul><ul><ul><li>Key: For commercial topics (“LV travel”, “digital camera”) actively searched for, people don’t mind (like?) it </li></ul></ul><ul><ul><li>Editorial control, “invisible hand” keep results relevant </li></ul></ul><ul><li>Enter Google </li></ul><ul><ul><li>Innovative, nimble, fast, effective </li></ul></ul><ul><ul><li>Licensed Overture patent (one reason for Y!s ~5% stake in G) </li></ul></ul>
    26. 26. Sponsored Search A Brief & Biased History <ul><li>Overture introduced the first design in 1997: first price, rank by bid </li></ul><ul><li>Google then began running slot auctions in 2000: second price, rank by revenue (bid * CTR) </li></ul><ul><li>In 2002, Overture (at this point acquired by Yahoo!) then switched to second-price. Still uses rank by bid; Moving toward rank by revenue </li></ul>Thanks: S. Lahaie
    27. 27. Sponsored Search A Brief & Biased History <ul><li>In the beginning: </li></ul><ul><ul><li>Exact match, rank by bid, pay per click, human editors </li></ul></ul><ul><ul><li>Mechanism simple, easy to understand, worked, somewhat ad hoc </li></ul></ul><ul><li>Today & tomorrow: </li></ul><ul><ul><li>“ 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) </li></ul></ul>
    28. 28. Sponsored Search Research A Brief & Biased History <ul><li>Weber & Zeng, A model of search intermediaries and paid referrals </li></ul><ul><li>Bhargava & Feng, Preferential placement in Internet search engines </li></ul><ul><li>Feng, Bhargava, & Pennock Implementing sponsored search in web search engines: Computational evaluation of alternative mechanisms </li></ul><ul><li>Feng, Optimal allocation mech’s when bidders’ ranking for objects is common </li></ul><ul><li>Asdemir, Internet advertising pricing models </li></ul><ul><li>Asdemir, A theory of bidding in search phrase auctions: Can bidding wars be collusive? </li></ul><ul><li>Mehta, Saberi, Vazirani, & Vaziran AdWords and generalized on-line matching </li></ul><ul><li>1st & 2nd Workshop on Sponsored Search Auctions at ACM Electronic Commerce Conference </li></ul>
    29. 29. Allocation and pricing <ul><li>Allocation </li></ul><ul><ul><li>Yahoo!: Rank by decreasing bid </li></ul></ul><ul><ul><li>Google: Rank by decreasing bid * E[CTR] (Rank by decreasing “revenue”) </li></ul></ul><ul><li>Pricing </li></ul><ul><ul><li>Pay “next price”: Min price to keep you in current position </li></ul></ul>
    30. 30. 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
    31. 31. 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]
    32. 32. 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] 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 TripReservations Expedia LVGravityZone etc...
    33. 33. Aside: Second price auction (Vickrey auction) <ul><li>All buyers submit their bids privately </li></ul><ul><li>buyer with the highest bid wins; pays the price of the second highest bid </li></ul>$150 $120 $90 $50  Only pays $120
    34. 34. Incentive Compatibility (Truthfulness) <ul><li>Telling the truth is optimal in second-price (Vickrey) auction </li></ul><ul><li>Suppose your value for the item is $100; if you win, your net gain (loss) is $100 - price </li></ul><ul><li>If you bid more than $100: </li></ul><ul><ul><li>you increase your chances of winning at price >$100 </li></ul></ul><ul><ul><li>you do not improve your chance of winning for < $100 </li></ul></ul><ul><li>If you bid less than $100: </li></ul><ul><ul><li>you reduce your chances of winning at price < $100 </li></ul></ul><ul><ul><li>there is no effect on the price you pay if you do win </li></ul></ul><ul><li>Dominant optimal strategy: bid $100 </li></ul><ul><ul><li>Key: the price you pay is out of your control </li></ul></ul><ul><li>Vickrey’s Nobel Prize due in large part to this result </li></ul>
    35. 35. Vickrey-Clark-Groves (VCG) <ul><li>Generalization of 2nd price auction </li></ul><ul><li>Works for arbitrary number of goods, including allowing combination bids </li></ul><ul><li>Auction procedure: </li></ul><ul><ul><li>Collect bids </li></ul></ul><ul><ul><li>Allocate goods to maximize total reported value (goods go to those who claim to value them most) </li></ul></ul><ul><ul><li>Payments: Each bidder pays her externality; Pays: (sum of everyone else’s value without bidder) - (sum of everyone else’s value with bidder) </li></ul></ul><ul><li>Incentive compatible (truthful) </li></ul>
    36. 36. Is Google pricing = VCG? <ul><li>Well, not really … </li></ul>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
    37. 37. VCG pricing <ul><li>(sum of everyone else’s value w/o bidder) - (sum of everyone else’s value with bidder) </li></ul><ul><li>CTR i = adv i * pos i (key “separability” assumption) </li></ul><ul><li>price i = 1/adv i *(∑ j<i bid j *CTR j + ∑ 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 ) </li></ul><ul><li>Notes </li></ul><ul><ul><li>For truthful Y! ranking set adv i = 1. But Y! ranking technically not VCG because not efficient allocation. </li></ul></ul><ul><ul><li>Last position may require special handling </li></ul></ul>
    38. 38. Next-price equilibrium <ul><li>Next-price auction: Not truthful: no dominant strategy </li></ul><ul><li>What are Nash equilibrium strategies? There are many! </li></ul><ul><li>Which Nash equilibrium seems “focal” ? </li></ul><ul><li>Locally envy-free equilibrium [Edelman, Ostrovsky, Schwarz 2005] Symmetric equilibrium [Varian 2006] Fixed point where bidders don’t want to move  or  </li></ul><ul><ul><li>Bidders first choose the optimal position for them: position i </li></ul></ul><ul><ul><li>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 </li></ul></ul><ul><li>Pure strategy (symmetric) Nash equilibrium </li></ul><ul><li>Intuitive: Squeeze bidder above, but not enough to risk “punishment” from bidder above </li></ul>
    39. 39. Next-price equilibrium <ul><li>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 </li></ul><ul><li>Nomenclature: Next price = “generalized second price” (GSP) </li></ul>
    40. 40. Selected survey of sponsored search research at Yahoo! <ul><li>Analytic comparison of mechanisms [Lahaie] </li></ul><ul><li>Learning click rates: N-armed bandit formulation [Pandey & Olsten] </li></ul><ul><li>Simulation I: Static [Feng, Bhargava, Pennock] </li></ul><ul><li>Simulation II: Equilibrium [Lahaie, Pennock] </li></ul><ul><li>Pragmatic robot [Schwarz, Edelman] </li></ul>
    41. 41. An Analysis of Alternative Slot Auction Designs for Sponsored Search <ul><li>Sebastien Lahaie , Harvard University* </li></ul><ul><li>*work partially conducted at Yahoo! Research </li></ul><ul><li>ACM Conference on Electronic Commerce, 2006 </li></ul>Source: S. Lahaie
    42. 42. Slot Auctions <ul><li>Every time a search is performed on a keyword on Yahoo! and Google, an auction is cleared that determines ads alongside. </li></ul><ul><li>An auction allows for automatic price discovery. </li></ul><ul><li>The good being sold is the attention of a user in an appropriate “intentional stance”. </li></ul><ul><li>Payment is “per click”, as opposed to “per impression” or “per conversion”. </li></ul>Source: S. Lahaie
    43. 43. Sponsored Search <ul><li>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). </li></ul><ul><li>The combined market cap of Yahoo! and Google is $150 billion. </li></ul><ul><li>In 2004, industry-wide sponsored search revenues were $3.9 billion, or 40% of total Internet advertising revenues (PricewaterhouseCoopers). </li></ul>Source: S. Lahaie
    44. 44. Objective <ul><li>Initiate a systematic study of Yahoo! and Google slot auctions designs. </li></ul><ul><li>Look at both “short-run” incomplete information case, and “long-run” complete information case. </li></ul>Source: S. Lahaie
    45. 45. Outline <ul><li>Incomplete information (one shot game) </li></ul><ul><ul><li>Incentives </li></ul></ul><ul><ul><li>Efficiency </li></ul></ul><ul><ul><li>Informational requirements </li></ul></ul><ul><ul><li>Revenue </li></ul></ul><ul><li>Complete Information (long-run equilibrium) </li></ul><ul><ul><li>Existence of equilibria </li></ul></ul><ul><ul><li>Characterization of equilibria </li></ul></ul><ul><ul><li>Efficiency of equilibria (“price of anarchy”) </li></ul></ul>Source: S. Lahaie
    46. 46. Related Work <ul><li>[Feng et al. ’05] compare the revenue performance of various ranking mechanisms via simulations. </li></ul><ul><li>[Liu and Chen ’05] study slot auction mechanisms with a single slot, where agents have binary types. </li></ul><ul><li>[Edelman et al. ’05] study the “locally envy-free” equilibria of slot auctions and their revenue properties. </li></ul><ul><li>[Varian ’06] gives bounds used to infer bidder values given their bids. </li></ul>Source: S. Lahaie
    47. 47. The Model <ul><li>slots, bidders </li></ul><ul><li>The type of bidder i consists of </li></ul><ul><ul><li>a value per click of , realization </li></ul></ul><ul><ul><li>a relevance , realization </li></ul></ul><ul><li>is bidder i’s revenue, realization </li></ul><ul><li>Ad in slot is viewed with probability So CTR i,k = </li></ul><ul><li>Bidder i’s utility function is quasi-linear: </li></ul>Source: S. Lahaie
    48. 48. The Model (cont’d) <ul><li>is i.i.d on according to </li></ul><ul><li>is continuous and has full support </li></ul><ul><li>is common knowledge </li></ul><ul><li>Probabilities are common knowledge. </li></ul><ul><li>Only bidder i knows realization </li></ul><ul><li>Both seller and bidder i know , but other bidders do not </li></ul>Source: S. Lahaie
    49. 49. Auction Formats <ul><li>Rank-by-bid (RBB): bidders are ranked according to their declared values ( ) </li></ul><ul><li>Rank-by-revenue (RBR): bidders are ranked according to their declared revenues ( ) </li></ul><ul><li>First-price: a bidder pays his declared value </li></ul><ul><li>Second-price (next-price): For RBB, pays next highest price. For RBR, pays </li></ul><ul><li>All payments are per click </li></ul>Source: S. Lahaie
    50. 50. <ul><li>First-price: neither RBB nor RBR is truthful </li></ul><ul><li>Second-price: being truthful is not a dominant strategy, nor is it an ex post Nash equilibrium (by example): </li></ul><ul><li>Use Holmstrom’s lemma to derive truthful payment rules for RBB and RBR: </li></ul><ul><li>RBR with truthful payment rule is VCG </li></ul>Incentives Source: S. Lahaie 1 6 1 4
    51. 51. Efficiency <ul><li>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 . </li></ul><ul><li>RBB is not efficient (by example). </li></ul><ul><li>Proposition: RBR is efficient (proof). </li></ul>Source: S. Lahaie 0.5 6 1 4
    52. 52. First-Price Bidding Equilibria <ul><li>is the expected resulting clickthrough rate, in a symmetric equilibrium of the RBB auction, to a bidder with value y and relevance 1. </li></ul><ul><li>is defined similarly for bidder with product y and relevance 1. </li></ul><ul><li>Proposition: Symmetric Bayes-Nash equilibrium strategies in a first-price RBB and RBR auction are given by, respectively: </li></ul>Source: S. Lahaie
    53. 53. Informational Requirements <ul><li>RBB: bidder need not know his own relevance, or the distribution over relevance. </li></ul><ul><li>RBR: must know own relevance and joint distribution over value and relevance. </li></ul>Source: S. Lahaie
    54. 54. Revenue Ranking <ul><li>Revenue equivalence principle : auctions that lead to the same allocations in equilibrium have the same expected revenue. </li></ul><ul><li>Neither RBB nor RBR dominates in terms of revenue, for a fixed number of agents, slots, and a fixed . </li></ul>Source: S. Lahaie
    55. 55. 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
    56. 56. Characterization of Equilibria <ul><li>RBB: same characterization with replacing </li></ul>Source: S. Lahaie
    57. 57. Price of Anarchy Define: Source: S. Lahaie
    58. 58. Exponential Decay <ul><li>Typical model of decaying clickthrough rate: </li></ul><ul><li>[Feng et al. ’05] find that their actual clickthrough data is fit well by such a model with </li></ul><ul><li>In this case </li></ul>Source: S. Lahaie
    59. 59. Conclusion <ul><li>Incomplete information (on-shot game): </li></ul><ul><ul><li>Neither first- nor second-pricing leads to truthfulness. </li></ul></ul><ul><ul><li>RBR is efficient, RBB is not </li></ul></ul><ul><ul><li>RBB has weaker informational requirements </li></ul></ul><ul><ul><li>Neither RBB nor RBR is revenue-dominant </li></ul></ul><ul><li>Complete information (long-run equilibrium): </li></ul><ul><ul><li>First-price leads to no pure strategy Nash equilibria, but second-price has many. </li></ul></ul><ul><ul><li>Value in equilibrium is constant factor away from “standard” value. </li></ul></ul>Source: S. Lahaie
    60. 60. Future Work <ul><li>Better characterization of revenue properties: under what conditions on does either RBB or RBR dominate? </li></ul><ul><li>Revenue results for complete information case (relation to Edelman et al.’s “locally envy-free equilibria”). </li></ul>Source: S. Lahaie
    61. 61. Research Problem: Online Estimation of Clickrates <ul><li>Make virtually no assumptions on clickrates. </li></ul><ul><li>Each different ranking yields (1) information on clickrates and (2) revenue. </li></ul><ul><li>Tension between optimizing current revenue based on current information, and gaining more info on clickrates to optimize future revenue (multi-armed bandit problem...) </li></ul><ul><li>Twist: chosen policy determines rankings, which will affect agent’s equilibrium behavior. </li></ul>Source: S. Lahaie
    62. 62. Handling Advertisements of Unknown Quality in Search Advertising Sandeep Pandey , Carnegie Mellon University Christopher Olston , Yahoo! Research and CMU Neural Information Processing Systems , 2006
    63. 63. CTR estimation <ul><li>Explore/exploit tradeoff </li></ul><ul><ul><li>Exploit: Use current CTR est’s to rank </li></ul></ul><ul><ul><li>Explore: Try new or low rank advertisers in higher positions to improve CTR est’s </li></ul></ul>
    64. 64. Analytic results <ul><li>Unbudgeted: Cast as independent multi-armed bandits, propose “MIX” policy </li></ul><ul><li>Budgeted: New budgeted multi-armed multi-bandit formulation (BMMP) </li></ul><ul><li>bpol(N) >= opt(N)/2 + O(ln N) </li></ul>
    65. 65. Experiments: Real Y! data
    66. 66. Extensions <ul><li>Using prior information e.g. algorithmic relevance of listing </li></ul><ul><li>Allowing ads to come and go at any time </li></ul><ul><li>Additional performance bounds </li></ul>
    67. 67. Implementing sponsored search in web search engines: Computational evaluation of alternative mechanisms Jane Feng , University of Florida Hemant Bhargava , University of California Davis David Pennock , Yahoo! Research Informs Journal on Computing , forthcoming
    68. 68. Simulation model Source: J. Feng
    69. 69. Simulation model <ul><li> = relevance  click rate (CTR) </li></ul><ul><li>v = advertiser value </li></ul><ul><li>(  ,v) = bivariate normal </li></ul><ul><li>Revenue: </li></ul>
    70. 70. Allocation Rules Tested <ul><li>Bid ranking </li></ul><ul><li>Revenue ranking </li></ul><ul><li>Relevance ranking </li></ul><ul><li>Posted price </li></ul>
    71. 71. Simulation Results
    72. 72. Simulation Results Number of paid slots
    73. 73. Simulation Results Effect of editorial control
    74. 74. Simulation Results Effect of naive learning of 
    75. 76. Equilibrium revenue simulations of hybrid sponsored search mechanisms Sebastien Lahaie , Harvard University* *work conducted at Yahoo! Research David Pennock , Yahoo! Research
    76. 77. Monte-Carlo simulations <ul><li>10 bidders, 10 positions </li></ul><ul><li>Value and relevance are i.i.d. and have lognormal marginals with mean and variance (1,0.2) and (1,0.5) resp. </li></ul><ul><li>Spearman correlation between value and relevance is varied between -1 and 1. </li></ul><ul><li>Standard errors are within 2% of plotted estimates. </li></ul>Source: S. Lahaie
    77. 78. Revenue effects <ul><li>What gives most revenue ? </li></ul><ul><ul><li>Key : If rules change, advertiser bids will change </li></ul></ul><ul><ul><li>Use Edelman et al. envy-free equilibrium solution </li></ul></ul>Y! today Highest bid wins Google/Panama Highest bid*CTR wins s=0 s=1/2 ? s=1 s=3/4 ? Hybrid Highest bid*(CTR) s wins
    78. 79. Source: S. Lahaie
    79. 80. Source: S. Lahaie
    80. 81. Source: S. Lahaie
    81. 82. Preliminary Conclusions <ul><li>With perfectly negative correlation (-1), revenue, efficiency, and relevance exhibits threshold behavior </li></ul><ul><li>Squashing up to this threshold can improve revenue without too much sacrifice in efficiency or relevance </li></ul><ul><li>Squashing can significantly improve revenue with positive correlation </li></ul>Source: S. Lahaie
    82. 83. Pragmatic Robots and Equilibrium Bidding in GSP Auctions Michael Schwarz , Yahoo! Research Ben Edelman , Harvard University Source: M. Schwarz
    83. 84. Testing game theory <ul><li>Empirical game theory </li></ul><ul><ul><li>Analytic solutions intractable in all but simplest settings </li></ul></ul><ul><ul><li>Laboratory experiments cumbersome, costly </li></ul></ul><ul><ul><li>Agent-based simulation: easy, cheap, allow massive exploration; Key: modeling realistic strategies </li></ul></ul><ul><li>Ideal for agent-based simulation: when real economic decisions are already delegated to software </li></ul><ul><li>“ 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 </li></ul>Thanks: M. Schwarz
    84. 85. Bidders’ actual strategies Source: M. Schwarz
    85. 86. Models of GSP <ul><li>Static game of complete information </li></ul><ul><li>Generalized English Auction (simple dynamic model) </li></ul><ul><li>More realistic model </li></ul><ul><li>Each period one random bidder can change his bid </li></ul><ul><li>Before the move a bidder observes all standing bids </li></ul>Source: M. Schwarz
    86. 87. Background Information <ul><li>Envy free point: a player is indifferent between being at his position or moving up one position and paying his bid </li></ul><ul><li>Equilibrium bids are at envy free points </li></ul><ul><li>b i = s i - ( s i - b k+1 ) </li></ul><ul><li>b i, , s i and α i are bid, value, and expected number of clicks respectively </li></ul>α k α k-1 Source: M. Schwarz
    87. 88. Pragmatic Robot (PR) <ul><li>Find current optimal position i Implies range of possible bids: Static best response (BR set) </li></ul><ul><li>Choose envy-free point inside BR set: Bid up to point of indifference between position i and position i-1 </li></ul><ul><li>If start in equilibrium PRs stay in equilibrium </li></ul>Source: M. Schwarz
    88. 89. Convergence of PR Simulation Source: M. Schwarz
    89. 90. Convergence of PR Source: M. Schwarz
    90. 91. Convergence of PR <ul><li>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 </li></ul>Complex game that we can not solve Simple model inspired by a complex game ? Source: M. Schwarz
    91. 92. Playing with Ideal Subjects <ul><li>Largest Gap (commercially available strategy) Moves your keyword listing to the largest bid gap within a specified set of positions </li></ul><ul><li>Regime One: 15 robots all play Largest Gap </li></ul><ul><li>Regime Two: one robot becomes pragmatic </li></ul><ul><li>By becoming Pragmatic pay off is up 16% </li></ul><ul><li>Other assumptions: values are log normal, mean valuation 1, std dev 0.7 of the underlying normal, bidders move sequentially in random order </li></ul>Source: M. Schwarz
    92. 93. ROI <ul><li>Setting ROI target is a popular strategy </li></ul><ul><li>For any ROI goal the advertiser who switches to pragmatic gets higher payoff </li></ul>Source: M. Schwarz
    93. 94. If others play ROI targeter <ul><li>Bidders 1,...,K-1 bid according to the ROI targeting strategy </li></ul><ul><li>What is K ’s best response? </li></ul>Source: M. Schwarz bidder payoffs if bidder K plays 0.0457 0.0387 K K-1 … 1 PR ROI targeting bidder
    94. 95. Strange Strategy <ul><li>Strange Strategy: bid one cent if everybody bids one cent, bid $10000 if at lest one bidder bids more than one cent. </li></ul><ul><li>Strange Strategy beats PR </li></ul><ul><li>PR beats Strange Strategy </li></ul>Source: M. Schwarz
    95. 96. Reinforcement Learner vs Pragmatic Robot <ul><li>Pragmatic learner outperforms reinforcement learner (that we tried) </li></ul><ul><li>Remark: reinforcement learning does not converge in a problem with big BR set </li></ul>Source: M. Schwarz
    96. 97. Conclusion <ul><li>A strategy inspired by theory seems useful in practice: PR beats commercially available strategies and other reasonable baselines </li></ul><ul><li>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” </li></ul><ul><li>When bidding agents are used for real economic decisions (e.g., search engine optimization), we have an ideal playground for empirical game theory simulations </li></ul>Thanks: M. Schwarz
    97. 98. 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
    98. 99. Papers <ul><li>Mechanism Design </li></ul><ul><ul><li>Pay-Per-Percentage of Impressions: An Advertising Method that is Highly Robust to Fraud, J.Goodman </li></ul></ul><ul><ul><li>Stochastic and Contingent-Payment Auctions, C.Meek,D.M.Chickering, D.B.Wilson </li></ul></ul><ul><ul><li>Optimize-and-Dispatch Architecture for Expressive Ad Auctions, D.Parkes, T.Sandholm </li></ul></ul><ul><ul><li>Sponsored Search Auction Design via Machine Learning, M.-F. Balcan, A.Blum, J.D.Hartline, Y.Mansour </li></ul></ul><ul><ul><li>Knapsack Auctions, G.Aggarwal, J.D. Hartline </li></ul></ul><ul><ul><li>Designing Share Structure in Auctions of Divisible Goods, J.Chen, D.Liu, A.B.Whinston </li></ul></ul>
    99. 100. Papers <ul><li>Bidding Strategies </li></ul><ul><ul><li>Strategic Bidder Behavior in Sponsored Search Auctions, Benjamin Edelman, Michael Ostrovsky </li></ul></ul><ul><ul><li>A Formal Analysis of Search Auctions Including Predictions on Click Fraud and Bidding Tactics, B.Kitts, P.Laxminarayan, B.LeBlanc, R.Meech </li></ul></ul><ul><li>User experience </li></ul><ul><ul><li>Examining Searcher Perceptions of and Interactions with Sponsored Results, B.J.Jansen, M. Resnick </li></ul></ul><ul><ul><li>Online Advertisers' Bidding Strategies for Search, Experience, and Credence Goods: An Empirical Investigation, A.Animesh, V. Ramachandran, </li></ul></ul><ul><ul><li>S.Vaswanathan </li></ul></ul>
    100. 101. Stochastic Auctions C.Meek,D.M.Chickering, D.B.Wilson <ul><li>Ad ranking allocation rule is stochastic </li></ul><ul><li>Why? </li></ul><ul><ul><li>Reduces incentive for “bid jamming” </li></ul></ul><ul><ul><li>Naturally incorporates explore/exploit mix </li></ul></ul><ul><ul><li>Incentive for low value bidders to join/stay? </li></ul></ul><ul><li>Derive truthful pricing rule </li></ul><ul><li>Investigate contingent-payment auctions: Pay per click, pay per action, etc. </li></ul><ul><li>Investigate bid jamming, exploration strategies </li></ul>
    101. 102. Expressive Ad Auctions D.Parkes, T.Sandholm <ul><li>Propose expressive bidding semantics for ad auctions (examples next) </li></ul><ul><ul><li>Good: Incr. economic efficiency, incr. revenue </li></ul></ul><ul><ul><li>Bad: Requires combinatorial optimization; Ads need to be displayed within milliseconds </li></ul></ul><ul><li>To address computational complexity, propose “optimize and dispatch” architecture: Offline scheduler “tunes” an online (real-time) dispatcher </li></ul>
    102. 103. Expressive bidding I <ul><li>Multi-attribute bidding </li></ul>$1.50 $1.50 Un-differentiated $1 $2 Female users (50%) $2 $1 Male users (50%) Advertiser 2 Advertiser 1 $1.50 $1.50 Un-differentiated $1 $1 Other (50%) $2 $2 Pre-qualified (50%) Advertiser 2 Advertiser 1
    103. 104. Expressive bidding II <ul><li>Competition constraints </li></ul>3 x .05 = .15 1 x .05 = .05 b xCTR = RPS
    104. 105. Expressive bidding II <ul><li>Competition constraints </li></ul>4 x .07 = .28 b xCTR = RPS monopoly bid
    105. 106. Expressive bidding III <ul><li>Guaranteed future delivery </li></ul><ul><li>Decreasing/increasing marginal value </li></ul><ul><li>All or nothing bids </li></ul><ul><li>Pay per: impression, click, action, ... </li></ul><ul><li>Type/id of distribution site (content match) </li></ul><ul><li>Complex search query properties </li></ul><ul><li>Algo results properties (“piggyback bid”) </li></ul><ul><li>Ad infinitum </li></ul><ul><li>Keys: What advertisers want; what advertisers value differently; controlling cognitive burden; computational complexity </li></ul>
    106. 107. 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
    107. 108. Objectives <ul><li>Diversity </li></ul><ul><ul><li>Participants </li></ul></ul><ul><ul><ul><ul><li>Industry: Search engines and search engine marketers </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Academia: Engineering, business, economics schools </li></ul></ul></ul></ul><ul><ul><li>Approaches </li></ul></ul><ul><ul><ul><ul><li>Mechanism Design </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Empirical </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Data mining / machine learning </li></ul></ul></ul></ul><ul><li>New Ideas </li></ul>Source: K. Asdemir
    108. 109. History & Overview <ul><li>First Workshop on S.S.A. </li></ul><ul><ul><li>Vancouver, BC 2005 </li></ul></ul><ul><ul><li>~25 participants </li></ul></ul><ul><ul><li>10 papers + Open discussion </li></ul></ul><ul><ul><li>4 papers from Microsoft Research </li></ul></ul><ul><li>Second Workshop on S.S.A. </li></ul><ul><ul><li>~40-50 participants </li></ul></ul><ul><ul><li>10 papers + Panel </li></ul></ul><ul><ul><li>3 papers from Yahoo! Research </li></ul></ul>Source: K. Asdemir
    109. 110. Participants <ul><li>Industry </li></ul><ul><ul><li>Yahoo!, Microsoft, Google </li></ul></ul><ul><ul><li>Iprospect (Isobar), Efficient Frontier, HP Labs, Bell Labs, CommerceNet </li></ul></ul><ul><li>Academia </li></ul><ul><ul><li>Several schools </li></ul></ul>Source: K. Asdemir
    110. 111. Papers <ul><li>Mechanism design </li></ul><ul><ul><li>Edelman, Ostrovsky, and Schwarz </li></ul></ul><ul><ul><li>Iyengar and Kumar </li></ul></ul><ul><ul><li>Liu, Chen, and Whinston </li></ul></ul><ul><ul><li>Borgs et al. </li></ul></ul><ul><li>Bidding behavior </li></ul><ul><ul><li>Zhou and Lukose </li></ul></ul><ul><ul><li>Szymanski and Lee </li></ul></ul><ul><ul><li>Asdemir </li></ul></ul><ul><ul><li>Borgs et al. </li></ul></ul><ul><li>Data mining </li></ul><ul><ul><li>Regelson and Fain </li></ul></ul><ul><ul><li>Sebastian, Bartz, and Murthy </li></ul></ul>Source: K. Asdemir
    111. 112. Panel: Models of Sponsored Search: What are the Right Questions? <ul><li>Proposed by </li></ul><ul><ul><li>Lance Fortnow and Rakesh Vohra </li></ul></ul><ul><li>Panel members </li></ul><ul><ul><li>Kamal Jain, Microsoft Research </li></ul></ul><ul><ul><li>Rakesh Vohra, Northwestern University </li></ul></ul><ul><ul><li>Michael Schwarz, Yahoo! Inc </li></ul></ul><ul><ul><li>David Pennock, Yahoo! Inc </li></ul></ul>Source: K. Asdemir
    112. 113. Panel Discussions <ul><li>Mechanisms </li></ul><ul><ul><li>Competition between mechanisms </li></ul></ul><ul><ul><li>Ambiguity vs Transparency: “Pricing” versus “auctions” </li></ul></ul><ul><ul><li>Involving searchers </li></ul></ul><ul><li>Budget </li></ul><ul><ul><li>Hard or a soft constraint </li></ul></ul><ul><ul><li>Flighting (How to spend the budget over time?) </li></ul></ul><ul><li>Pay-per-what? CPM, CPC, CPS </li></ul><ul><ul><li>Risk sharing </li></ul></ul><ul><ul><li>Fraud resistance </li></ul></ul><ul><li>Transcript available! </li></ul>Source: K. Asdemir
    113. 114. Web resources <ul><li>1st Workshop website & papers: http://research.yahoo.com/workshops/ssa2005/ </li></ul><ul><li>1st Workshop notes (by Rohit Khare): http://wiki.commerce.net/wiki/RK_SSA_WS_Notes </li></ul><ul><li>2nd Workshop website & papers: http://www.bus.ualberta.ca/kasdemir/ssa2/ </li></ul><ul><li>2nd Workshop panel transcript: (thanks Hartline & friends!) http://research.microsoft.com/~hartline/papers/ panel-SSA-06.pdf </li></ul>
    114. 115. <ul><li>Yahoo!,O’Reilly launched Buzz Game 3/05 @ETech </li></ul><ul><li>Research testbed for investigating prediction markets </li></ul><ul><li>Buy “stock” in hundreds of technologies </li></ul><ul><li>Earn dividends based on actual search “buzz” </li></ul><ul><li>API interface </li></ul><ul><li>Exchange mechanism is Yahoo! invention (dynamic parimutuel) </li></ul><ul><li>Cross btw stock market and horse race betting </li></ul>http://buzz.research.yahoo.com
    115. 116. Technology forecasts <ul><li>iPod phone </li></ul><ul><li>What’s next? Google Calendar? </li></ul><ul><li>Another Apple unveiling 10/12; iPod Video? </li></ul>search buzz price 9/8-9/18: searches for iPod phone soar; early buyers profit 8/29: Apple invites press to “secret” unveiling 8/28: buzz gamers begin bidding up iPod phone 9/7: Apple announces Rokr 9am 10/5
    116. 117. Forecast accuracy <ul><li>Average forecast error across 352 stocks </li></ul><ul><li>Market closing deadline focuses traders </li></ul><ul><li>Dividend levels matter </li></ul><ul><li>Intelligent strategies work </li></ul><ul><ul><li>Randomized bots lost money to real traders </li></ul></ul><ul><ul><li>Contest winner followed optimal buzz trading strategy (prices   buzz); Went from 4 th to 1 st place in final days </li></ul></ul><ul><ul><li>Forecast error does decrease over time </li></ul></ul>Early lessons learned end of phase 1 contest period forecast error rapidly declines as traders zero in on correct predictions
    117. 118. Forecast accuracy <ul><li>Stocks categorized by Day 0 implied buzz / actual buzz </li></ul><ul><li>Graph shows movement of actual buzz for each category </li></ul>
    118. 119. Tech Buzz Game
    119. 120. Pari-mutuel market Basic idea 1 1 1 1 1 1 1 1 1 1 1 1
    120. 121. Dynamic pari-mutuel market Basic idea 0.9 0.4 0.2 3 2.5 2 1.6 1.3 1.1 1 1
    121. 122. How are prices set? <ul><li>A price function p i (n) gives the instantaneous price of an infinitesimal additional share beyond the nth </li></ul><ul><li>Cost of buying n shares:  0 n p i (n) dn </li></ul><ul><li>Different reasonable assumptions lead to different price functions </li></ul>
    122. 123. Share-ratio price function <ul><li>One can view DPM as a market maker </li></ul><ul><li>Shares pay equal portion of total $$: C(Q final )/q o >= $1 </li></ul><ul><li>Ratio of shares q i /q j = ratio of prices p i /p j </li></ul><ul><li>Cost Function: </li></ul><ul><li>Price Function: </li></ul>
    123. 124. Price functions Closed form cost() & shares() p i /p j = S i /S j All money Closed form shares() ; Numeric cost() p i /p j = M i /M j All money Closed form cost() & shares() p i /p j = M i /M j Losing money Closed form cost() & shares() p 1 = P 2 p 2 = P 1 Losing money Result Constraint/ Assumption Share type
    124. 125. More Challenges <ul><li>Predicting click through rates </li></ul><ul><li>Detecting click spam </li></ul><ul><li>Pay per “action” / conversion </li></ul><ul><li>Number of ad slots </li></ul><ul><li>Improved targeting / expressiveness </li></ul><ul><li>Content match </li></ul>

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