Internet-Based Auctions and Markets

1. David M. Pennock Principal Research ScientistYahoo! Research - NYC Internet-BasedAuctions and Markets

2. Yesterday “Today” (~2000) eBay: 4 million; 450k new/day Going once, … going twice, ... Auctions: 2000 View

3. Yesterday “Today” (~2000) Auctions: 2000 View

4. Yesterday “Today” (~2000) Auctions: 2000 View

5. Yesterday eBay 200 million/month Today Google / Yahoo! 6 billion/month (US) Auctions: 2006 View

6. Yesterday Today Auctions: 2006 View

7. Yesterday Today Auctions: 2006 View

8. 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.”

9. “The United States of Search” • 6 billion searches/month • 50% of web users search every day • 13% of traffic to commercial sites • 40% of product searches • $5 billion 2005 US ad revenue (41% of US online ads; 2% of all US ads) • Doubling every year for four years • Search data: Covers nearly everything that people think about: intensions, desires, diversions, interests, buying habits, ...

10. Outline • Selected survey of Internet-based electronic markets • Auctions (e.g., eBay) • Combinatorial auctions • Sponsored search advertisement auctions (e.g., Google, Yahoo!) • Prediction markets (e.g., Iowa political markets, financial markets)

11. What is an auction? • Definition [McAfee & McMillan, JEL 1987]: • a market institution with an • explicit set of rules • determining resource allocation and prices • on the basis of bids from the market participants. • Examples:

12. Why auctions? • For object of unknown value • Flexible • Dynamic • Mechanized • reduces complexity of negotiations • ideal for computer implementation • Economically efficient!

13. Taxonomy of common auctions • Open auctions • English • Dutch • Sealed-bid auctions • first price • second price (Vickrey) • Mth price, M+1st price • continuous double auction

14. Open One item for sale Auctioneer begins low; typically with seller’s reserve price Buyers call out bids to beat the current price Last buyer remaining wins;pays the price that (s)he bid English auction

15. Dutch auction • Open • One item for sale • Auctioneer begins high;above the maximum foreseeable bid • Auctioneer lowers price in increments • First buyer willing to accept price wins;pays last announced price • less information

16. Sealed-bid first price auction • All buyers submit their bids privately • buyer with the highest bid wins;pays the price (s)he bid  $150 $120 $90 $50

17. Sealed-bid 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  $150 $120 $90 $50

18. Incentive Compatibility(Truthfulness) • Telling the truth is optimal in second-price 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

19. 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 difference between sum of everyone else’s value without bidder minus sum of everyone else’s value with bidder • Incentive compatible (truthful)

20. Collusion • Notice that, if some bidders collude, they might do better by lying (e.g., by forming a ring) • In general, essentially all auctions are subject to some sort of manipulation by collusion among buyers, sellers, and/or auctioneer.

21. Revenue Equivalence • Which auction is best for the seller? • In second-price auction, buyer pays < bid • In first-price auction, buyers “shade” bids • Theorem: • expected revenue for seller is the same! • requires technical assumptions on buyers, including “independent private values” • English = 2nd price; Dutch = 1st price

22. Mth price auction • English, Dutch, 1st price, 2nd price:N buyers and 1 seller • Generalize to N buyers and M sellers • Mth price auction: • sort all bids from buyers and sellers • price = the Mth highest bid • let n = # of buy offers >= price • let m = # of sell offers <= price • let x = min(n,m) • the x highest buy offers and x lowest sell offers win

23. Buy offers (N=4) Sell offers (M=5) Mth price auction $300 $150 $170 $120 $130 $90 $110 $50 $80

24. Sell offers (M=5) Buy offers (N=4) Mth price auction 1 $300 $170 2 $150 3  $130 4 price = $120 $120 5  $110  $90 $80  $50 • Winning buyers/sellers

25. Sell offers (M=5) Buy offers (N=4) M+1st price auction 1 $300 $170 2 $150 3  $130 4 $120 5  price = $110 $110  6 $90 $80  $50 • Winning buyers/sellers

26. Incentive Compatibility(Truthfulness) • M+1st price auction is incentive compatible for buyers • buyers’ dominant strategy is to bid truthfully • M=1 is Vickrey second-price auction • Mth price auction is incentive compatible for sellers • sellers’ dominate strategy is to make offers truthfully

27. Impossibility • Essentially no auction whatsoever can be simultaneously incentive compatible for both buyers and sellers! • if buyers are induced to reveal their true values, then sellers have incentive to lie, and vice versa • the only way to get both to tell the truth is to have some outside party subsidize the auction

28. Impossibility • Setup: 1 good, 1 buyer w/ value [a1,b1],seller w/ value [a2,b2], nonempty intersec. • Desirable properties / axioms: • (1) incentive compatible • (2) individually rational • (3) efficient • (4) no outside subsidy • (1)(4) are mutually inconsistent [M & S 83]

29. Sell offers (M=5) Buy offers (N=4) k-double auction 1 $300 $170 2 $150 3  $130 4 price = $110 + $10*k $120 5  $110  6 $90 $80  $50 • Winning buyers/sellers

30. Continuous double auction • k-double auction repeated continuously over time • buyers and sellers continually place offers • as soon as a buy offer > a sell offer, a transaction occurs • At any given time, there is no overlap btw highest buy offer & lowest sell offer

31. Continuous double auction

32. Winner’s curse • Common, unknown value for item (e.g., potential oil drilling site) • Most overly optimistic bidder wins; true value is probably less

33. Combinatorial auctions • E.g.: spectrum rights, computer system, … • n goods  bids allowed  2n combinations Maximizing revenue: NP-hard (set packing) • Enter computer scientists (hot topic)… • Survey: [Vries & Vohra 02]

34. Combinatorial auctions(Some) research issues • Preference elicitation [Sandholm 02] • Bidding languages [Nissan 00] & restrictions [Rothkopf 98] • Approximation • relation to incentive compatibility [Lehmann 99] and bounded rationality [Nisan & Ronen 00] • False-name bidders [Yokoo 01] • Winner determination • GVA (VCG) mechs, iterative mechs [Parkes 99]; “smart markets” • integer programming; specialized heuristics [Sandholm 99] • FCC spectrum auctions • Optimal auction design [Ronen 01] [Brewer 99] More: [Vries & Vohra 02]

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

36. 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”)

37. Sponsored Search • 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

38. Example price volatility: Vioxx

39. Sponsored Search Today • 2005: ~ $7 billion industry • 2004: ~ $4B; 2003: ~ $2.5B; 2002: ~ $1B • $5 billion 2005 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

40. Sponsored SearchA Brief & Biased History • Idealab  GoTo.com (no relation to Go.com) • Crazy (terrible?) idea, meant to combat search spam • Search engine “destination” that ranks results based on who is willing to pay the most • With algorithmic SEs out there, who would use it? • GoTo   Yahoo! Search Marketing • Team w/ algorithmic SE’s, provide “sponsored results” • Key: For commercial topics (“LV travel”, “digital camera”) actively searched for, people don’t mind (like?) it • Editorial control, “invisible hand” keep results relevant • Enter Google • Innovative, nimble, fast, effective • Licensed Overture patent (one reason for Y!s ~5% stake in G)

41. 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)

42. Sponsored Search ResearchA Brief & Biased History • 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 mech’s 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 • 1st & 2nd Workshop on Sponsored Search Auctions at ACM Electronic Commerce Conference

43. Allocation and pricing • Allocation • Yahoo!: Rank by decreasing bid • Google: Rank by decr. bid * E[CTR] • Pricing • Pay “next price”: Min price to keep you in current position • NOT Vickrey pricing, despite Google marketing collateral; Not truthful • Vickrey pricing possible but more complicated

44. Some Challenges • Predicting click through rates (CTR) • Detecting click spam • Pay per “action” / conversion • Number of ad slots • Improved targeting

45.  6 = 6 ? = 6 I am entitled to: $1 if $0 if A prediction market • Take a prediction question, e.g. • Turn it into a financial instrument payoff = realized value of variable 2007 CAEarthquake? US’08Pres =Clinton?

46. Aside: Terminology • Key aspect: payout is uncertain • Called variously: asset, security, contingent claim, derivative (future, option), stock, prediction market, information market, gamble, bet, wager, lottery • Historically mixed reputation • Esp. gambling aspect • A time when options were frowned upon • But when regulated serve important social roles...

47. Why? Reason 1 Get information • price  expectation of outcome(in theory, lab experiments, empirical studies, ...more later) • Do you have a prediction question whose expected outcome you’d like to know?A market in uncertainty can probably help

48.  6 = 6 I am entitled to: $1 if $0 if Getting information • Non-market approach: ask an expert • How much would you pay for this? • A: $5/36  $0.1389 • caveat: expert is knowledgeable • caveat: expert is truthful • caveat: expert is risk neutral, or ~ RN for $1 • caveat: expert has no significant outside stakes

49.  6 = 6 = 6 Getting information • Non-market approach:pay an expert • Ask the expert for his report r of the probabilityP( ) • Offer to pay the expert • $100 + log r if • $100 + log (1-r) if • It so happens that the expert maximizes expected profit by reporting r truthfully • caveat: expert is knowledgeable • caveat: expert is truthful • caveat: expert is risk neutral, or ~ RN • caveat: expert has no significant outside stakes “logarithmic scoring rule”, a “proper” scoring rule

50. I am entitled to:  6 = 6 = 6 $1 if $0 if Getting information • Market approach: “ask” the public—experts & non-experts alike—by opening a market: • Let any person i submit a bid order: an offer to buy qi units at price pi • Let any person j submit an ask order: an offer to sell qj units at price pj(if you sell 1 unit, you agree to pay $1 if ) • Match up agreeable trades (many poss. mechs...)