Transcript Internet-based Auctions and Markets

Internet-Based
Auctions and Markets
David M. Pennock
Principal Research Scientist
Yahoo! Research - NYC
Auctions: 2000 View
• Yesterday
Going once, …
going twice, ...
• “Today” (~2000)
– eBay: 4 million;
450k new/day
Auctions: 2000 View
• Yesterday
• “Today” (~2000)
Sotheby's (founded 1744)
Ebay (founded 1995)
18.00
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00
Market Capitalization (billions )
Auctions: 2000 View
• Yesterday
• “Today” (~2000)
Auctions: 2006 View
• Yesterday
• Today
– eBay
– Google / Yahoo!
– 200 million/month
– 6 billion/month (US)
Auctions: 2006 View
• Yesterday
• Today
Ebay (founded 1995)
Google (founded 1998)
120.00
100.00
80.00
60.00
40.00
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0.00
Market Capitalization (billions )
Auctions: 2006 View
• Yesterday
• Today
Newsweek June 17, 2002
“The United States of EBAY”
• In 2001: 170 million transactions worth $9.3 billion in
18,000 categories “that together cover virtually the
entire universe of human artifacts—Ferraris,
Plymouths and Yugos; desk, floor, wall and ceiling
lamps; 11 different varieties of pockets watches;
contemporary Barbies, vintage Barbies, and replica
Barbies.”
• “Since everything that transpires on Ebay is
recorded, and most of it is public, the site constitutes
a gold mine of data on American tastes and
preoccupations.”
“The United States of Search”
• 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, ...
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)
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:
Why auctions?
• For object of unknown value
• Flexible
• Dynamic
• Mechanized
– reduces complexity of negotiations
– ideal for computer implementation
• Economically efficient!
Taxonomy of common auctions
• Open auctions
– English
– Dutch
• Sealed-bid auctions
– first price
– second price (Vickrey)
– Mth price, M+1st price
– continuous double auction
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
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
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
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
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
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)
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.
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
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
Mth price auction
• Buy offers (N=4)
• Sell offers (M=5)
$300
$150
$120
$170
$130
$90
$110
$50
$80
Mth price auction
• Buy offers (N=4)
• Sell offers (M=5)
$300 1
$170 2
3
 $150
$130 4
5
 $120
$110 
$90
$80 
$50
price = $120
Winning buyers/sellers
M+1st price auction
• Buy offers (N=4)
• Sell offers (M=5)
$300 1
$170 2
3
 $150
$130 4
5
 $120
$110 6
$90
$80 
$50
price = $110
Winning buyers/sellers
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
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
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]
k-double auction
• Buy offers (N=4)
• Sell offers (M=5)
$300 1
$170 2
3
 $150
$130 4
5
 $120
$110 6
$90
$80 
$50
price = $110 + $10*k
Winning buyers/sellers
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
Continuous double auction
Winner’s curse
• Common, unknown value for item
(e.g., potential oil drilling site)
• Most overly optimistic bidder wins;
true value is probably less
0.09
0.08
probability
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$ valuation of item
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]
Combinatorial auctions
(Some) research issues
•
Preference elicitation
•
Bidding languages
•
Approximation
[Sandholm 02]
[Nissan 00]
& restrictions [Rothkopf 98]
– relation to incentive compatibility [Lehmann 99]
and bounded rationality [Nisan & Ronen 00]
•
False-name bidders
•
Winner determination
[Yokoo 01]
[Brewer 99]
– GVA (VCG) mechs, iterative mechs [Parkes 99]; “smart markets”
– integer programming; specialized heuristics [Sandholm 99]
•
FCC spectrum auctions
•
Optimal auction design [Ronen 01]
More: [Vries & Vohra 02]
Sponsored search
Space next to search results is sold at auction
search “las vegas travel”, Yahoo!
“las vegas travel” auction
Sponsored Search Auctions
• Search engines auction off space next
to search results, e.g. “digital camera”
• Higher bidders get higher placement
on screen
• Advertisers pay per click: Only pay
when users click through to their site;
don’t pay for uncliked view
(“impression”)
Sponsored Search
• 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
Date
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P rice ($)
Example price volatility: Vioxx
Vioxx
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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
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)
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)
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
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
Some Challenges
• Predicting click through rates (CTR)
• Detecting click spam
• Pay per “action” / conversion
• Number of ad slots
• Improved targeting
A prediction market
• Take a prediction question, e.g.
2007 CA
Earthquake?
US’08Pres =
Clinton?
=6?
• Turn it into a financial instrument payoff
= realized value of variable
I am entitled to:
$1 if
=6
$0 if
6
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...
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
Getting information
• Non-market approach: ask an expert
– How much would you pay for this?
I am entitled to:
$1 if
=6
$0 if
6
• 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
Getting information
• Non-market approach: pay an expert
– Ask the expert for his report r of the probability
=6 )
P(
– Offer to pay the expert
• $100 + log r
• $100 + log (1-r)
if
if
=6
6
“logarithmic scoring rule”,
a “proper” scoring rule
• 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
Getting information
• Market approach: “ask” the public—experts &
non-experts alike—by opening a market:
I am entitled to:
$1 if
=6
$0 if
6
• 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
= 6)
• Match up agreeable trades (many poss. mechs...)
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.
I am entitled to:
$1 if
; $0 otherwise
I am entitled to:
$x if interest rate = x on Jan 1, 2004
I am entitled to:
call option
$max(0,x-k) if MSFT = x
on Jan 1, 2004
I am entitled to:
$f(future weather)
weather derivative
I am entitled to:
Bin Laden
$1 if captured ;
I am entitled to:
$0 otherwise
$1 if Kansas beats Marq.
by > 4.5 points; $0 otherw.
http://tradesports.com
http://www.biz.uiowa.edu/iem
http://www.wsex.com/
IPO
Play money;
Real predictions
http://www.hsx.com/
http://www.ideosphere.com
Cancer
cured
by 2010
Machine Go
champion
by 2020
http://us.newsfutures.com/
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]
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]
Why? Reason 2
Manage risk
= 6 is horribly terrible for you
• If
Buy a bunch of
I am entitled to:
$1 if
and if
=6
$0 if
6
= 6 happens, you are
compensated
Why? Reason 2
Manage risk
• If
is horribly terrible for you
Buy a bunch of
I am entitled to:
$1 if
and if
$0 if
happens, you are
compensated
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
E.g. stocks, options, futures, insurance,
..., sports bets, ...
• Allocate risk (“hedge”)
• Aggregate information
– insured transfers risk
to insurer, for $$
– farmer transfers risk
to futures speculators
– price of insurance
 prob of catastrophe
– OJ futures prices yield
weather forecasts
– put option buyer
hedges against stock
drop; seller assumes
risk
– prices of options
encode prob dists over
stock movements
– market-driven lines are
unbiased estimates of
outcomes
– IEM political forecasts
– sports bet may hedge
against other stakes
in outcome
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
On hedging and speculating
•
Why would two parties agree to trade in a
prediction market?
1. Speculation. They disagree on expected
values (prob’s)
2. Hedging. They differ in their risk attitude or
exposure – they trade to reallocate risk
3. 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.
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
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)
some
Oncomputational
issues
• Machine learning, data mining
– Beat the market (exploiting
combinatorics?)
– Explain the market, information
retrieval
– Detect fraud
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, ...
http://us.newsfutures.com/home/articles.html