Transcript Prediction Markets and Information Aggregation

Prediction Markets
& Information Aggregation
Yiling Chen, Harvard SEAS
Preference vs. Information

Preference



Information



I prefer orange to apple
I’m willing to pay $50 for this item
About some uncertain event
Information helps to update beliefs
Sometimes mixed together
Information Elicitation and Aggregation
Problem

Events of interest
Will Democratic party win the Presidential election?
 Will US economy still in recession in 2010?
 Will there be a bird flu outbreak by August 2011?
 Will sales of printers exceed 30K in July?
……


Information is dispersed among individuals

Want to aggregate dispersed information to make an
informed prediction
We can ask experts, but

How to identify them?

How to ensure them to tell the truth?



Incentivize experts using proper scoring rules
Need to pay every expert
How to resolve conflicts among experts?

Impossibility results
Bet = Credible Opinion

Q: Will Pittsburgh Panthers win the NCAA tournament?
Info
Info
Panthers will
not win the
NCAA.

Betting intermediaries

Las Vegas, Wall Street, Betfair, Intrade,...
I bet $1000
Panthers will
win the NCAA.
Prediction Markets


A prediction market is a betting intermediary that is
designed for information aggregation and prediction.
Payoffs of the traded item is associated with
outcomes of future events.
$1×Percentage
of
$1 ififObama
winswin
$1
Panthers
Vote Share
$f(x) That
$0 Otherwise
$0Obama
Otherwise
Wins
Why Markets? – Get Information

Speculation price discovery
price  expectation of random variable | all information
$1 if Panthers win, $0 otherwise
Value of Contract
Payoff
Event
Outcome
$1
Panthers win
$0
Panthers lose
?
$P( Panthers win )
Equilibrium Price  Value of Contract  P( Panthers Win )
Market Efficiency
Does it work?

I.E.M. beat political polls 451/596
[Forsythe 1992, 1999][Oliven
1995][Rietz 1998][Berg 2001][Pennock 2002]
Iowa
caucus
Super
Tuesday
[Source: Berg, DARPA Workshop, 2002]
IEM 1992
Example: IEM
Does it work?

Microsoft Prediction Market





August 2004: Predict internal product ship date
Official, accepted schedule: mid-November 2004
25 traders @ $50, made up of testers, developers, etc.
Securities: Pre-NOV, NOV, DEC, JAN, FEB, Post-FEB
Within a few minutes of opening, NOV dropped to $0.012…
Does it work?

Yes, evidence from real markets, laboratory experiments,
and theory







Racetrack odds beat track experts [Figlewski 1979]
Orange Juice futures improve weather forecast [Roll 1984]
HP markets beat sales forecast 6/8 [Plott 2000]
Google, GE, Elli Lily, and more all have positive evidence
Sports betting markets provide accurate forecasts of game
outcomes [Gandar 1998][Thaler 1988][Debnath EC’03][Schmidt 2002]
Market games work [Servan-Schreiber 2004][Pennock 2001]
Laboratory experiments confirm information aggregation
[Plott 1982;1988;1997][Forsythe 1990][Chen, EC’01]

Theory: “rational expectations” [Grossman 1981][Lucas 1972]
Predicting the CEO

Will Mr. Smith or Ms. Jones be the CEO of company X?
Pr(Mr. Smith)
$1 if Mr. Smith becomes CEO
$1
Pr(Ms. Jones)
$1 if Ms. Jones becomes CEO
Predicting CEO Outcomes

How will CEO affect stock prices?
$1 if Mr. Smith becomes CEO & stock price goes up
$1 if Mr. Smith becomes CEO & stock price goes down
$1 if Ms. Jones becomes CEO & stock price goes up
$1 if Ms. Jones becomes CEO & stock price goes down

Alternatively,
1 share of stock, if Mr. Smith becomes CEO
1 share of stock, if Ms. Jones becomes CEO
CEO Decision Market

Should company X hire Mr. Smith or Ms. Jones as CEO?
Pr(Mr. Smith)
$1 if Mr. Smith
becomes CEO
$1 if Mr. Smith becomes
CEO & stock price goes up
Pr(stock up|Mr. Smith)
Which one
is higher?
$1
Pr(Ms. Jones)
$1 if Ms. Jones
becomes CEO
Pr(stock up|Ms. Jones)
$1 if Ms. Jones becomes
CEO & stock price goes up
Pr(stock up|Mr. Smith)=Pr(Mr. Smith & Stock up)/Pr(Mr. Smith)
CEO Decision Market

Conditional market
$1 if stock price goes up | Mr. Smith becomes CEO
$1 if stock price goes up and Mr. Smith becomes CEO
$0 if stock price goes down and Mr. Smith becomes CEO
called off if Mr. Smith does not become CEO
$1 if stock price goes up | Mr. Jones becomes CEO
Decision Markets Give E(O|C)
Choices






Outcomes
FED money policy
Next president
Health care regulation
School vouchers

Who is CEO
Which ad agency





GDP per capita
War deaths
Lifespan
School test scores
Stock price
Product sales
[Source: Hanson]
Does money matter?
Head to Head Comparison
 2003 NFL Season
 Football prediction markets



NewsFutures (play $)
Tradesports (real $)
Online football forecasting
competition




probabilityfootball.com
Contestants assess probabilities for
each game
Quadratic scoring rule
~2,000 “experts”
[Servan-Schreiber et. al. 2004]
Results:

Play money and real money
performed similarly


6th and 8th respectively
Markets beat most of the
~2,000 contestants

Average of experts came 39th
Desired Properties of Prediction Markets and
Other Information Aggregation Mechanisms

Liquidity


Truthfulness


There are as few constraints as possible on the form of bets
that people can use to express their opinions.
Computational tractability


Participants reveal their information honestly and immediately.
Expressiveness


People can find counterparties to trade whenever they want.
The process of operating a market should be computationally
manageable.
Can handle situations where ground truth is not available
Desired Properties of Prediction Markets and
Other Information Aggregation Mechanisms

Liquidity


There are as few constraints as possible on the form of bets
that people can use to express their opinions.
Computational tractability


Participants reveal their information honestly and immediately.
Expressiveness


People can find counterparties to trade whenever they want.
Truthfulness


(Use automated market makers)
The process of operating a market should be computationally
manageable.
Can handle situations where ground truth is not available
Truthfulness: Manipulation Concerns I

Can forward looking traders get more profit by bluffing
in prediction markets?
Truthfulness: Manipulation Concerns I

Can forward looking traders get more profit by bluffing in
prediction markets?

Conditionally independent signals:
Truthful betting is the only equilibrium

Independent signals:
No finite equilibrium that involves truthful betting, but it’s
possible to change the mechanism so that bluffing is
discouraged
[Chen et. al.
Truthfulness: Manipulation Concerns II

Manipulate market price to influence decision making
How to make prediction markets to be manipulation
resistant is an open question.

Manipulation in Intrade

Expressiveness and Computational
Complexity: Combinatorial Prediction Markets

Things people can express today




Democrat wins the election (with probability 0.55)
No bird flu outbreak in US before 2011
Horse A will win the race
Things people can not express (very well) today



Democrat wins the election if he/she wins both Florida and
Ohio
Oil price increases & A Democrat wins & Recession in 2009
Horse A beats Horse B
Expressiveness and Computational
Complexity: Combinatorial Prediction Markets
A beats C
A or B will be at
position 1.
USC wins a third round game.
USC beats Wisconsin if they meet.
Obama wins
Florida and Ohio
Chen et. al. EC’07, EC’08, STOC’08
When there is no ground truth

No prediction market in theory can handle it now, but in
practice some are in use

E.g. New product development
When there is no ground truth

We still have hope

Peer prediction [Miller et. al. 2005]




Proper scoring rule
Comparing with peer
Strong common knowledge of common prior assumption
Bayesian truth serum [Prelec 2004]


Ask for an answer and a prediction
Reward answers that are more common than collectively predicted
In short




Prediction market is an example in which competitive
agents interact indirectly through the market mechanism
to achieve some collaborative goal.
It’s a centralized mechanism.
Is the bigger problem information elicitation and
aggregation?
How to approach the problem when we do not have full
rationality, do not need absolute truthfulness, and do not
have ground truth?