Internet-Based Auctions and Markets

Internet-BasedAuctions and Markets

David M. Pennock

Principal Research ScientistYahoo! Research - NYC

Auctions: 2000 View

Going once, …going twice, ...

• Yesterday • “Today” (~2000)

– eBay: 4 million; 450k new/day

Auctions: 2000 View

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Market Capitalization (billions)

Sotheby's (founded 1744) Ebay (founded 1995)

• Yesterday • “Today” (~2000)

Auctions: 2000 View

• Yesterday • “Today” (~2000)

Auctions: 2006 View

• Yesterday– eBay

– 200 million/month

• Today– Google / Yahoo!

– 6 billion/month (US)

Auctions: 2006 View

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Market Capitalization (billions)

Ebay (founded 1995) Google (founded 1998)

• Yesterday • Today

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

$150$120

$90

$50

Only pays $120

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

$150

$120

$90

$50

$300

$170

$130

$110

• Buy offers (N=4) • Sell offers (M=5)

$80

$50$80

$90$110

$120$130

Mth price auction

$150$170$300

• Buy offers (N=4) • Sell offers (M=5)

12345

Winning buyers/sellers

price = $120

$50$80

$90$110

$120$130

M+1st price auction

$150$170$300

• Buy offers (N=4) • Sell offers (M=5)

12345

Winning buyers/sellers

6 price = $110

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]

$50$80

$90$110

$120$130

k-double auction

$150$170$300

• Buy offers (N=4) • Sell offers (M=5)

12345

Winning buyers/sellers

6

price = $110 + $10*k

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

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$ valuation of item

probability

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 [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]

More: [Vries & Vohra 02]

[Brewer 99]

search “las vegas travel”, Yahoo!

Sponsored search

Space next to search results is sold at auction

“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

Vioxx

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9/14/089/15/089/16/089/17/089/18/089/19/089/20/089/21/089/22/089/23/089/24/089/25/089/26/089/27/089/28/089/29/089/30/0810/1/0810/2/0810/3/0810/4/0810/5/0810/6/0810/7/0810/8/0810/9/0810/10/0810/11/0810/12/0810/13/08

Date

Price ($)

Example price volatility: Vioxx

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 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)

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)

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

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.

• Turn it into a financial instrument payoff = realized value of variable

= 6 ?

= 6$1 if 6$0 if

I am entitled to:

US’08Pres =Clinton?

2007 CAEarthquake?

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?

• 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:

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

6

“logarithmic scoring rule”,a “proper” scoring rule

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...)

= 6$1 if 6$0 if

I am entitled to:

= 6

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

I am entitled to:

$1 if ; $0 otherwise

I am entitled to:

Bin Ladencaptured

$max(0,x-k) if MSFT = xon Jan 1, 2004

I am entitled to:

call option

$f(future weather)I am entitled to:

weather derivative

$1 if Kansas beats Marq.by > 4.5 points; $0 otherw.

I am entitled to:

http://tradesports.com

http://www.biz.uiowa.edu/iem

IIPPOO

http://www.wsex.com/

Play money;Real predictions

http://www.hsx.com/

http://us.newsfutures.com/

Cancercured

by 2010

Machine Gochampionby 2020

http://www.ideosphere.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

• If is horribly terrible for youBuy a bunch of

and if happens, you are compensated

= 6

= 6$1 if 6$0 if

I am entitled to:

= 6

Why? Reason 2

Manage risk

• If is horribly terrible for youBuy a bunch of

and if happens, you are compensated

$1 if $0 if

I am entitled to:

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”)– 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

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.

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

some

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