1. Internet-Based Auctions and Markets
David M. Pennock
Principal Research Scientist Yahoo! Research - NYC

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

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

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

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

6. Auctions: 2006 View
Yesterday
Today

7. Auctions: 2006 View
Yesterday
Today

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. English auction 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

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
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.

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

24. Mth price auction Buy offers (N=4) Sell offers (M=5) $300 1 $170 2
$150 3 
$130 4
price = $120
$120 5 
Notice that, as you would expect, all buyers who bid higher than the price win their bids, and all sellers who bid lower than the price win their bids. Also note that, when M=1, the Mth price auction is the same as the first price auction, assuming that the seller’s bid is below at least one of the buyers’ bids.
$110 
$90
$80 
$50
Winning buyers/sellers

25. M+1st price auction Buy offers (N=4) Sell offers (M=5) $300 1 $170 2
$150 3 
$130 4
$120 5 
Notice that, as you would expect, all buyers who bid higher than the price win their bids, and all sellers who bid lower than the price win their bids.
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]
I continue on this tour of impossibilities with a result about trading. Imagine the simple case where one seller has an object and one buyer is interested in that object. Both the seller and buyer have a valuation for the object distributed over some interval. Each agent knows its own valuation, but only knows the distribution for the other agent’s valuation. Once again, we can identify a few desirable properties that we would like in an ideal trading mechanism. We would like the mechanism to be such that it is in each individual’s interest to bid according to its true valuation. This is called an incentive compatible mechanism. We would like it to be individually rational, or such that neither agent would expect to be better off by opting out of the system. We would like it to be efficient, or to initiate a transaction if and only if the buyer’s valuation is higher than the seller’s. Finally, we would prefer that no outside subsidy be required to induce the agents to play. Myerson and Satterthwaite proved that no mechanism satisfies all four of these properties.

29. k-double auction Buy offers (N=4) Sell offers (M=5) $300 1 $170 2 $150
$130 4
price = $110 + $10*k
$120 5 
Notice that, as you would expect, all buyers who bid higher than the price win their bids, and all sellers who bid lower than the price win their bids.
$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. Sponsored search Space next to search results is sold at auction
search “las vegas travel”, Yahoo!
“las vegas travel” 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 Search A 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 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)

42. Sponsored Search Research A Brief & Biased History
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 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, & Vaziran AdWords and generalized on-line matching
1st & 2nd Workshop on Sponsored Search Auctions at ACM Electronic Commerce Conference

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

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. Getting information = 6 $1 if  6 $0 if
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
= 6
$1 if
 6
$0 if
I am entitled to:

49. Getting information Non-market approach: pay an expert
Ask the expert for his report r of the probability P( )
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
= 6
= 6
“logarithmic scoring rule”,
a “proper” scoring rule
 6

50. Getting information = 6 $1 if  6 $0 if = 6
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...)
= 6
$1 if
 6
$0 if
I am entitled to:
= 6

51. Real predictions
For dice example, no need for market: E[x] is known; no one should disagree
Real power comes for non-obvious predictions, e.g.
$1 if ;
$0 otherwise
I am entitled to:
$x if interest rate = x on Jan 1, 2004

52. $max(0,x-k) if MSFT = x on Jan 1, 2004
I am entitled to:
$max(0,x-k) if MSFT = x on Jan 1, 2004
call option
I am entitled to:
$f(future weather)
weather derivative
I am entitled to:
Bin Laden captured
$1 if ;
$0 otherwise
I am entitled to:
$1 if Kansas beats Marq. by > 4.5 points; $0 otherw.

54.
IPO

55. Play money; Real predictions

56. Cancer cured by 2010 Machine Go champion by 2020
Cancer cured by 2010
Machine Go champion by 2020

57. Does it work? Yes...
Evidence from real markets, laboratory experiments, and theory indicate that markets are good at gathering information from many sources and combining it appropriately; e.g.:
Markets like the Iowa Electronic Market predict election outcomes better than polls [Forsythe 1992, 1999][Oliven 1995][Rietz 1998][Berg 2001][Pennock 2002]
Futures and options markets rapidly incorporate information, providing accurate forecasts of their underlying commodities/securities [Sherrick 1996][Jackwerth 1996][Figlewski 1979][Roll 1984][Hayek 1945]
Sports betting markets provide accurate forecasts of game outcomes [Gandar 1998][Thaler 1988][Debnath EC’03][Schmidt 2002]

58. Does it work? Yes... E.g. (cont’d):
Laboratory experiments confirm information aggregation [Plott 1982;1988;1997][Forsythe 1990][Chen, EC-2001]
And field tests [Plott 2002]
Theoretical underpinnings: “rational expectations” [Grossman 1981][Lucas 1972]
Procedural explanation: agents learn from prices [Hanson 1998][Mckelvey 1986][Mckelvey 1990][Nielsen 1990]
Proposals to use information markets to help science [Hanson 1995], policymakers, decision makers [Hanson 1999], government [Hanson 2002], military [DARPA FutureMAP, PAM]
Even market games work! [Servan-Schreiber 2004][Pennock 2001]

59. Why? Reason 2
Manage risk
If is horribly terrible for you Buy a bunch of and if happens, you are compensated
= 6
= 6
$1 if
 6
$0 if
I am entitled to:
= 6

60. Why? Reason 2
Manage risk
If is horribly terrible for you Buy a bunch of and if happens, you are compensated
I am entitled to:
$1 if
$0 if

61. Reason 2: Manage risk What is insurance?
A bet that something bad will happen!
E.g., I’m betting my insurance co. that my house will burn down; they’re betting it won’t. Note we might agree on P(burn)!
Why? Because I’ll be compensated if the bad thing does happen
A risk-averse agent will seek to hedge (insure) against undesirable outcomes

62. E.g. stocks, options, futures, insurance, ..., sports bets, ...
Allocate risk (“hedge”)
insured transfers risk to insurer, for $$
farmer transfers risk to futures speculators
put option buyer hedges against stock drop; seller assumes risk
sports bet may hedge against other stakes in outcome
Aggregate information
price of insurance  prob of catastrophe
OJ futures prices yield weather forecasts
prices of options encode prob dists over stock movements
market-driven lines are unbiased estimates of outcomes
IEM political forecasts

63. What am I buying?
When you hedge/insure, you pay to reduce the unpredictability of future wealth
Risk-aversion: All else being equal, prefer certainty to uncertainty in future wealth
Typically, a less risk-averse party (e.g., huge insurance co, futures speculator) assumes the uncertainty (risk) in return for an expected profit

64. On hedging and speculating
Why would two parties agree to trade in a prediction market?
Speculation. They disagree on expected values (prob’s)
Hedging. They differ in their risk attitude or exposure – they trade to reallocate risk
Both (most likely)
Aside: legality is murky, though generally (2) is legal in the US while (1) often is not. In reality, it is nearly impossible to differentiate.

65. On computational issues
some 
On computational issues
Information aggregation is a form of distributed computation
Agent level
nontrivial optimization problem, even in 1 market; ultimately a game-theoretic question
probability representation, updating algorithm (Bayes net)
decision representation, algorithm (POMDP)
agent problem’s computational complexity, algorithms, approximations, incentives
Most of the economics questions have been asked
Many of the economics questions have been answered
Most of the computational questions have not even been asked, let alone answered
It’s not magic: can’t solve NP-hard problems in poly time. Can’t solve the halting problem. Where is the line drawn? What can a market compute? What mechs are best?
Can we compute aggregate expected values w/o exchanging information (zero-knowledge convergence)?
Economists generally assume unbounded rationality. Computer Scientists understand bounded computational ability ==> Computational equilibrium: no agent can compute how to do better

66. On computational issues
some 
On computational issues
Mechanism level
Single market
What can a market compute?
How fast (time complexity)?
Do some mechanisms converge faster (e.g., subsidy)
Multiple markets
How many securities to compute a given fn? How many secs to support “sufficient” social welfare? (expressivity and representational compactness)
Nontrivial combinatorics (auctioneer’s computational complexity; algorithms; approximations; incentives)
Most of the economics questions have been asked
Many of the economics questions have been answered
Most of the computational questions have not even been asked, let alone answered
It’s not magic: can’t solve NP-hard problems in poly time. Can’t solve the halting problem. Where is the line drawn? What can a market compute? What mechs are best?
Can we compute aggregate expected values w/o exchanging information (zero-knowledge convergence)?
Economists generally assume unbounded rationality. Computer Scientists understand bounded computational ability ==> Computational equilibrium: no agent can compute how to do better

67. On computational issues
some 
On computational issues
Machine learning, data mining
Beat the market (exploiting combinatorics?)
Explain the market, information retrieval
Detect fraud
Most of the economics questions have been asked
Many of the economics questions have been answered
Most of the computational questions have not even been asked, let alone answered
It’s not magic: can’t solve NP-hard problems in poly time. Can’t solve the halting problem. Where is the line drawn? What can a market compute? What mechs are best?
Can we compute aggregate expected values w/o exchanging information (zero-knowledge convergence)?
Economists generally assume unbounded rationality. Computer Scientists understand bounded computational ability ==> Computational equilibrium: no agent can compute how to do better

68. 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, ...