Yearly Archives: 2008

fun, yootleoffer

Fred, Fran, and baby makes three

14 Comments

Two mathematicians Fred and Fran were having a baby girl, their first child! They sought the perfect name, a name that would simultaneously reflect togetherness, relationships, and individuality in their burgeoning family. Day and night they debated, rejecting name after name. Finally, they had it! The perfect name!

They named her Erin.

Why?

[Yootleoffer: 1 Yootle for first correct response.]

  • 2008/06/18 Addendum: Fred and Fran both study set theory.

  • 2008/07/27 Addendum: It turns out I didn’t need the 6/18 hint-addendum: commenters had already chimed in with correct answers but, due to a combination of mechanical and pilot error, I didn’t realize it.

    So, … drum roll please…
    the winner is… John! His is the first correct response. Commenter d is also correct with a more succinct and mathematical explanation. Dennis is close but not quite complete. So I’ll award John 2 yootles, d 1 yootle, and Dennis 1/2 yootle. John and d please let me know your contact info to claim your bounty.

    Dennis asks what a yootle is worth. A yootle is a quantified “thanks, I owe you one”. So it’s worth a return favor from me, someone who trusts me, someone who trust someone who trust me, etc.

    Bonus challenge: come up with a family of four with the same property and reasonable names (necessarily of eight letters each).

  • 2008/08/13 Addendum: The bonus round winner is… aj! He hacked up a script and discovered one of apparently many possible “perfectly” named families of four. Details are in the comments of this post. Thanks aj!
economics, events, prediction markets, research

Call for Papers and Participation: Workshop on Prediction Markets: Chicago, July 9 2008

I am happy to announce the following prediction market workshop and solicit submissions and participants.


=======================================================================
Call for Contributions and Participation

Third Workshop on Prediction Markets

http://betforgood.com/events/pm2008/index.html

Afternoon of July 9, 2008
Chicago, Illinois

In conjunction with the
ACM Conference on Electronic Commerce (EC’08)

SUBMISSIONS DUE May 23, 2008
=======================================================================

We solicit research contributions, system demonstrations, and
participants for the Third Workshop on Prediction Markets, to be held
in conjunction with the Ninth ACM Conference on Electronic Commerce
(EC’08). The workshop will bring together researchers and
practitioners from a variety of relevant fields, including economics,
finance, computer science, and statistics, in both academia and
industry, to discuss the state of the art today, and the challenges
and prospects for tomorrow in the field of prediction markets.

A prediction market is a financial market designed to elicit a
forecast. For example, suppose a policymaker seeks a forecast of the
likelihood of an avian flu outbreak in 2009. She may float a security
paying $1 if and only if an outbreak actually occurs in 2008, hoping
to attract traders willing to speculate on the outcome. With
sufficient liquidity, traders will converge to a consensus price
reflecting their collective information about the value of the
security, which in this case directly corresponds to the probability
of outbreak. Empirically, prediction markets often yield better
forecasts than other methods across a diverse array of settings.

The past decade has seen a healthy growth in the field, including a
sharp rise in publications and events, and the creation of the Journal
of Prediction Markets. Academic work includes mechanism design,
experimental (laboratory) studies, field studies, and empirical
analyses. In industry, several companies including Eli Lilly, Corning,
HP, Microsoft, and Google have piloted internal prediction
markets. Other companies, including ConsensusPoint, InklingMarkets,
InTrade, and NewsFutures, base their business on providing public
prediction markets, prediction market software solutions, or
consulting services. The growth of the field is reflected and fueled
by a wave of popular press articles and books on the topic, most
prominently Surowiecki’s “The Wisdom of Crowds”.

Workshop topics
===============

The area of prediction markets faces challenges regarding how best
to design, deploy, analyze, implement, and understand prediction
markets. One important research direction is designing mechanisms for
prediction markets, especially for events with a combinatorial outcome
space. Another notable issue is manipulation in prediction
markets. Understanding the effect of manipulation is especially
important for prediction markets to find their way to assist
individuals and organizations in making critical decisions. Moreover,
how to implement market mechanisms that not only are easy to use but
also facilitate information aggregation has been an important problem
for practitioners. Prediction markets face social and political
obstacles including antigambling laws and moral and ethical concerns,
both real and constructed.

Submissions of abstracts for research contributions from a rich set
of empirical, experimental, and theoretical perspectives are
invited. Topics of interest at the workshop include, but are not
limited to:

* Mechanism design
* Game-theoretic analysis of mechanisms, behaviors, and dynamics
* Decision markets
* Combinatorial prediction markets
* Market makers for prediction markets
* Manipulation and prediction markets
* Order matching algorithms
* Computational issues of prediction markets
* Liquidity and thin markets
* Laboratory experiments
* Empirical analysis
* Prediction market modeling
* Industry and field experience
* Simulations
* Policy applications and implications
* Internal corporate applications
* Legal and ethical issues

Submissions of summaries for demonstrations on prediction market
systems are invited. Systems of interest at the workshop include, but
are not limited to:

* Implemented combinatorial prediction markets
* Mature systems and commercial products of market mechanisms
* Research prototypes on prediction markets
* Other collective prediction systems

Submission instructions
=======================

Research contributions should report new (unpublished) research
results or ongoing research. We request an abstract not exceeding one
page for every research contribution.

For system demonstrations, a summary of up to two pages including
technical content to be demonstrated is requested. Please indicate if
the demonstration requires network access.

Research contributions and system demonstrations should be submitted
electronically to the organizing committee at pm2008@umich.edu no
later than midnight Hawaii time May 23, 2008.

At least one author of each accepted research contribution and
system demonstration will be expected to attend and present or
demonstrate their work at the workshop.

Important dates
===============

May 23, 2008: Submissions due midnight Hawaii Time

May 30, 2008: Notification of accepted research contributions and
system demonstrations

July 9, 2008: Workshop date

Organizing committee
====================

Yiling Chen, Yahoo! Inc
David Pennock, Yahoo! Inc
Rahul Sami, University of Michigan
Adam Siegel, Inkling Markets

More information
================

For more information or questions, visit the workshop website:
http://betforgood.com/events/pm2008/index.html

or email the organizing committee: pm2008@umich.edu

advertising, economics, events, research

Call for Papers and Participation: Workshop on Ad Auctions: Chicago, July 8-9 2008

I am happy to announce the following ad auctions workshop and solicit submissions and participants.


=======================================================================
Call for Papers

Fourth Workshop on Ad Auctions
http://research.yahoo.com/workshops/ad-auctions-2008/

July 8-9, 2008
Chicago, Illinois, USA

SUBMISSIONS DUE MAY 11, 2008

In conjunction with the
ACM Conference on Electronic Commerce (EC’08)
=======================================================================

We solicit submissions for the Fourth Workshop on Ad Auctions, to be
held July 8-9, 2008 in Chicago in conjunction with the ACM Conference
on Electronic Commerce. The workshop will bring together researchers
and practitioners from academia and industry to discuss the latest
developments in advertisement auctions and exchanges.

In the past decade we’ve seen a rapid trend toward automation in
advertising, not only in how ads are delivered and measured, but also
in how ads are sold. Web search advertising has led the way, selling
space on search results pages for particular queries in continuous,
dynamic “next price” auctions worth billions of dollars annually.

Now auctions and exchanges for all types of online advertising —
including banner and video ads — are commonplace, run by startups and
Internet giants alike. An ecosystem of third party agencies has grown
to help marketers manage their increasingly complex campaigns.

The rapid emergence of new modes for selling and delivering ads is
fertile ground for research from both economic and computational
perspectives. What auction or exchange mechanisms increase advertiser
value or publisher revenue? What user and content attributes
contribute to variation in advertiser value? What constraints on
supply and budget make sense? How should advertisers and publishers
bid? How can both publishers and advertisers incorporate learning and
optimization, including balancing exploration and exploitation? How do
practical constraints like real-time delivery impact design? How is
automation changing the advertising industry? How will ad auctions and
exchanges evolve in the next decade? How should they evolve?

Papers from a rich set of empirical, experimental, and theoretical
perspectives are invited. Topics of interest for the workshop include
but are not limited to:

* Web search advertising (sponsored search)
* Banner advertising
* Ad networks, ad exchanges
* Comparison shopping
* Mechanism and market design for advertising
* Ad targeting and personalization
* Learning, optimization, and explore/exploit tradeoffs in ad placement
* Ranking and placement of ads
* Computational and cognitive constraints
* Game-theoretic analysis of mechanisms, behaviors, and dynamics
* Matching algorithms: exact and inexact match
* Equilibrium characterizations
* Simulations
* Laboratory experiments
* Empirical characterizations
* Advertiser signaling, collusion
* Pay for impression, click, and conversion; conversion tracking
* Campaign optimization; bidding agents; search engine marketing (SEM)
* Local (geographic) advertising
* Contextual advertising (e.g., Google AdSense)
* User satisfaction/defection
* User incentives and rewards
* Affiliate model
* Click fraud detection, measurement, and prevention
* Price time series analysis
* Multiattribute and expressive auctions
* Bidding languages for advertising

We solicit contributions of two types: (1) research contributions,
and (2) position statements. Research contributions should report new
(unpublished) research results or ongoing research. The workshop
proceedings can be considered non-archival, meaning contributors are
free to publish their results later in archival journals or
conferences. Research contributions can be up to ten pages long, in
double-column ACM SIG proceedings format:
http://www.acm.org/sigs/publications/proceedings-templates
Position statements are short descriptions of the authors’ view of how
ad auction research or practice will or should evolve. Position
statements should be no more than five pages long. Panel discussion
proposals and invited speaker suggestions are also welcome.

The workshop will include a significant portion of invited
presentations along with presentations on accepted research
contributions. There will be time for both organized and open
discussion. Registration will be open to all EC’08 attendees.

The first three workshops on sponsored search auctions successfully
attracted a wide audience from academia and industry working on
various aspects of web search advertising. Following the footsteps of
the previous workshops, the Fourth Workshop on Ad Auctions strives to
be a venue that helps address challenges in the broader field of
online advertising, by providing opportunities for researchers and
practitioners to interact with each other, stake out positions, and
present their latest research findings. While the first three
workshops focused on web search advertising, we have broadened the
scope this year to include auctions and exchanges for any form of
online advertising.

Submission Instructions
=======================

Research contributions should report new (unpublished) research
results or ongoing research. The workshop’s proceedings can be
considered non-archival, meaning contributors are free to publish
their results later in archival journals or conferences. Research
contributions can be up to ten pages long, in double-column ACM SIG
proceedings format:
http://www.acm.org/sigs/publications/proceedings-templates
Positions papers and panel discussion proposals are also welcome.

Papers should be submitted electronically using the conference
management system:
http://www.easychair.org/conferences/?conf=adauctions2008
no later than midnight Hawaii time, May 11, 2008. Authors should also
email the organizing committee ( adauctions2008@yahoogroups.com ) to
indicate that they have submitted a paper to the system.

At least one author of each accepted paper will be expected to attend
and present their findings at the workshop.

Important Dates
===============

May 11, 2008 Submissions due midnight Hawaii time
a. Submit to:
http://www.easychair.org/conferences/?conf=adauctions2008
b. Notify adauctions2008@yahoogroups.com
May 23, 2008 Notification of accepted papers
June 8, 2008 Final copy due

Organizing Committee
====================

Susan Athey, Harvard University
Rica Gonen, Yahoo!
Jason Hartline, Northwestern University
Aranyak Mehta, Google
David Pennock, Yahoo!
Siva Viswanathan, University of Maryland

Program Committee
=================

Gagan Aggarwal, Google
Animesh Animesh, McGill University
Moshe Babaioff, Microsoft
Tilman Borgers, University of Michigan
Max Chickering, Microsoft
Chris Dellarocas, University of Maryland
Ben Edelman, Harvard University
Jon Feldman, Google
Jane Feng, University of Florida
Slava Galperin, A9
Anindya Ghose, New York University
Kartik Hosanagar, University of Pennsylvania
Kamal Jain, Microsoft
Jim Jansen, University of Pennsylvainia
Sebastien Lahaie, Yahoo!
John O. Ledyard, Caltech
Ying Li, Microsoft
Ilya Lipkind, A9
Preston McAfee, Yahoo!
Chris Meek, Microsoft
John Morgan, University of California Berkeley
Michael Ostrovsky, Stanford University
Abhishek Pani, Efficient Frontier
Martin Pesendorfer, London School of Economics
David Reiley, Yahoo!
Tim Roughgarden, Stanford University
Catherine Tucker, Massachusetts Institute of Technology
Rakesh Vohra, Northwestern University

More Information
================

For more information or questions, visit the workshop website:
http://research.yahoo.com/workshops/ad-auctions-2008/

or email the organizing committee:
adauctions2008@yahoogroups.com

commentary, prediction markets

A historic MayDay: The US government’s call for help on regulating prediction markets

6 Comments

May 1, 2008 could signal a turning point for the prediction markets industry.*

Yesterday, the US Commodity Futures Trading Commission (CFTC) issued a request for public comments as they mull over the legal and regulatory status of prediction markets.

I read the Concept Release in detail, and I am happy to report that it is a careful, thoughtful, even scholarly document that reflects a solid understanding of the goals of prediction markets, and that appears to signal a real willingness on the part of the CFTC to consider reasonable options and arguments.

In short, this development leaves the optimist in me dreaming of a day in the not so distant future when US companies can try out some truly innovative products.

It’s not often that an industry in its infancy cries out for more government oversight. But the CFTC is certainly preferable to the gambling Gestapo.

Anyone who desires to see more prediction markets in the US, please let the CFTC know what you think!

*Or not.
commentary, prediction markets, probability

Reporting prediction market prices

3 Comments

Reuters recently ran a story on political prediction markets, quoting prices from intrade and IEM. (Apparently the story was buzzed up to the Yahoo! homepage and made the Drudge Report.)

The reporter phrased prices in terms of the candidates’ percent chance of winning:

Traders … gave Democratic front-runner Barack Obama an 86 percent chance of being the Democratic presidential nominee, versus a 12.8 percent for Clinton…

…traders were betting the Democratic nominee would ultimately become president. They gave the Democrat a 59.1 percent chance of winning, versus a 48.8 percent chance for the Republican.

The latter numbers imply an embarrassingly incoherent market, giving the Democrats and Republicans together a 107.9% chance of winning. This is almost certainly the result of a typo, since the Republican candidate on intrade has not been much above 40 since mid 2007.

Still, typos aside, we know that the last-trade prices of candidates on intrade and IEM often don’t sum to exactly 100. So how should journalists report prediction market prices?

Byrne Hobart suggests they should stick to something strictly factual like "For $4.00, an investor could purchase a contract which would yield $10.00" if the Republican wins.

I disagree. I believe that phrasing prices as probabilities is desirable. The general public understands “percent chance” without further explanation, and interpreting prices in this way directly aligns with the prediction market industry’s message.

When converting prices to probabilities, is a journalist obligated to normalize them so they sum to 100? Should journalists report last-trade prices or bid-ask spreads or something else?

My inclination is that bid-ask spreads are better. Something like "traders gave the Democrats between a 22 and 30 percent chance of winning the state of Arkansas". These will rarely be inconsistent (otherwise arbitrage is sitting on the table) and the phrasing is still relatively easy to understand.

Avoiding this (admittedly nitpicky) dilemma is another advantage of automated market makers like Hanson’s. The market maker’s prices always sum to exactly 100, and the bid, ask, and last-trade prices are one and the same. Auction-type mechanisms like intrade’s can also be designed better so that prices are automatically kept consistent.

fun, probability

A freakonomist takes on Big Weather and, … stumbles

4 Comments

It seems that even D.I.Y. freakonomists aren’t sure how to judge probability forecasts.

In measuring precipitation accuracy, the study assumed that if a forecaster predicted a 50 percent or higher chance of precipitation, they were saying it was more likely to rain than not. Less than 50 percent meant it was more likely to not rain.

That prediction was then compared to whether or not it actually did rain…

prediction markets, yahoo

New York Post Video: Gambling on Politics

Two New York Post video reporters came to Yahoo!’s midtown NYC office last Friday to interview me for a piece they were producing on intrade‘s political prediction markets. The video is now up on NYPOST.COM and in the embedded player below. The reporters were friendly and professional — thankfully they cut out most of my word-fumbling moments — and the end result is an entertaining, polished, informative video geared toward newbies. My own role came out at least not terrible.

If you look carefully, you’ll see subtle product placement of the Yahoo! Election Dashboard, which aggregates a ton of election numbers including intrade prices. You can also see short clips of the conference room, whiteboard scribbles, ylogo, and cubes at our Y! Research NYC office.

See also: Chris Masse’s comments

economics, prediction markets

Crowdsourcing meets crowd wisdom

3 Comments

I met Lukas Biewald at CI Foo [1 2 3 4 5 6]. Lukas is involved in a fascinating startup called Dolores Labs that helps crowdsource your problem to Amazon’s Mechanical Turk. Read his manifesto.

As an experiment, they hired Turkers to label a sample of news items about Barack Obama and Hillary Clinton as either positive or negative for each of the candidates. As it turns out, every news source was pro-Obama except ABC News, with Digg being the pro-est of the pro-Obama camp.

They then plotted changes in news sentiment alongside the price of Obama’s intrade contract:

News sentiment and intrade price for Obama vs Clinton Feb-March 2008

Visually, there appears to be a correlation and news sentiment may actually be the leading indicator between the two, however it would be great to see statistical confirmation, if it’s even possible with such a small sample.

I sent Lukas some poll data and search buzz data that we’ve been collecting for the Yahoo! Election Dashboard. I’ll post an update if anything interesting results from lining up all four signals.

advertising, commentary, gambling, yahoo

Gambling advertising legal silliness

10 Comments

Google AdSense ads on intrade.comThe absurdity of gambling laws in the US leads to such silliness as:

  • In 2007, Google, Microsoft, and Yahoo! paid millions in penalties for placing gambling ads, something they haven’t done since they were told to stop in 2004.

  • Yahoo! can quote prices from intrade, but can’t link to intrade.

  • Google can’t advertise for intrade/tradesports, but can place AdSense ads on intrade.com and tradesports.com. In other words, Google can’t sell eyeballs to gambling sites, but can sell eyeballs on gambling sites.
explainer, prediction markets, research

The right way to implement a multi-outcome prediction market: Linear programming

9 Comments

There are many examples of multi-outcome prediction markets, for example election markets with more than two candidates, or sports championship markets with dozens of teams.

What is the best way to implement a multi-outcome prediction market?

The simplest way is to effectively ignore the fact that there are multiple outcomes, breaking up the market into a bunch of separate binary markets, one for each outcome. Each outcome-market is an independent instrument with its own order flow and processing.

This seems to be the most common approach, taken by for example intrade, IEM, racetracks, and most financial exchanges. IMHO, it’s the wrong way, for three reasons.

  1. Splitting up a market can hurt liquidity. In a split market, there are effectively two ways to do everything (e.g., buy outcome 1 equals sell outcomes 2 through N), so traders may not see the best price for what they want to do, and orders may not fill at the best price available. There may even be orders that together constitute an agreeable trade, yet are stuck waiting in separate queues.
  2. A split market may also slow information propagation. Price changes in one outcome do not directly affect prices of other outcomes; it’s left to arbitrageurs to propagate logical implications.
  3. Finally, a naïve implementation of a split market may limit traders’ leverage, forcing them set aside more money than necessary to complete a set of trades. For example, on IEM, short selling one share at $0.99 requires that you have $1 in your account, even though the most you could possibly lose in this transaction is $0.01. The reason is that to short sell on IEM you must first buy the bundle of all outcomes for $1, then sell off the outcome that you don’t want.

IEM has possibly the worst implementation, suffering from all three problems.

Intrade’s implementation is slightly better: they at least handle leverage correctly.

Newsfutures is smarter still.1 They generate phantom bids to reflect the redundant ways to place bets. For example, if there are bids for outcomes 2 through N that add up to $0.80, they place a phantom ask on outcome 1 for $0.20. A trader who accepts the ask, buying outcome 1 for $0.20, actually sells outcomes 2 through N behind the scenes, an entirely equivalent transaction. Chris Hibbert has a more elaborate methodology for eking out as much liquidity as possibly using phantom bids, an approach he has implemented plans to implement in his Zocalo platform.

Yet phantom bids are a band-aid that cannot entirely heal a fractured market. Still missing is the ability to trade bundles of outcomes in a single transaction.

For example, consider the US National Basketball Association championship market, with 30 teams. A split market (possibly with phantom bids) works great for betting on individual teams one at a time, but is terribly cumbersome for betting on groups of teams. For example, betting that a Western conference team will win requires 15 separate transactions. A common fix is to open yet another market in each popular bundle, however this limits choice and exacerbates all three problems above.

Bundling is especially useful with interval bets. For example, consider this bet on the peak price of gasoline through September 2008, broken up into intervals $3-$3.25, $3.25-$3.40, etc. In order to bet that gas prices will peak between, say, $3.40 and $4.30, you must buy all six outcomes spanning the interval, one at a time. (Moreover, you must sum the six outcome prices manually to compute a price quote.)

Fortunately, there is a trading engine that solves all three problems above and also allows bundle bets…

It’s linear programming!

Bossaerts et al. call it combined value trading. Baron & Lange, Lange & Economides and Peters et al. call it a parimutuel call market. Fortnow et al. and Chen et al. describe it in the context of combinatorial call markets.

Whatever you call it, the underlying principle is relatively straightforward, and it seems inherently the right way to implement a multi-outcome market. Yet I’ve rarely seen it done. The only example I know of is the now defunct economic derivatives markets run by Longitude, Goldman Sachs, and Deutsche Bank.

The set up of the linear program is as follows. Each order is associated with a decision variable x that ranges between 0 and 1, encoding the fraction of the order that the auctioneer can accept.2 There is one constraint per outcome that ensures that the auctioneer never loses money across all outcomes. The choice of objective function depends on the auctioneer’s goals, but something like maximizing the fill fraction makes sense.

Once the program is set up, the auctioneer solves for the x variables to determine which orders to accept in full (x=1), which to accept partially (0<x<1), and which to reject (x=0). The program can be solved either in batch mode, after waiting to collect a number of orders, or in continuous mode immediately as new orders arrive. Batch mode corresponds to a call market. Continuous mode corresponds to a continuous auction, a generalization of the continuous double auction mechanism of the stock market.

Each order consists of a price, a quantity, and an outcome bundle. Traders can just as easily bet on single outcomes, negations of outcomes, or sets of outcomes (e.g., all Western Conference NBA teams). Every order goes into the same pool of liquidity no matter how it is phrased.

Price quotes are queries to the linear program of the form “at what price p will this order be accepted in full?” (I believe that bounds on the dual variables of the LP can be interpreted as bid and ask price quotes.)

Lange & Economides and Peters et al. devise clever ways to make prices unique rather than bid-ask ranges, by injected a small subsidy to seed the market at the onset.

Note that Hanson’s market scoring rules market maker also elegantly solves all the same problems as the LP formulation, including handling bundle bets. However, the market maker requires a patron to subsidize the market, while the LP auctioneer formulation is budget balanced — that is, can never lose money.

Also note that I am not talking about a combinatorial-outcome market here. In this post, I am imagining that the number of outcomes is tractable — small enough so that we can explicitly list, store, and compute across all of the outcomes. A true combinatorial-outcome market, on the other hand, has an exponentially large number of outcomes making it impossible to even list them all explicitly, and forcing all calculations to operate on an implicit representation of outcomes, for example Boolean combinations of base events.

1Apparently worked out in conjunction with Brian Galebach, a mathematician and Newsfutures fan extraordinaire who runs the prediction contest probabilitysports.com.
2Alternatively, the variables can range between 0 and q, where q is the quantity of shares ordered.