Category Archives: prediction markets

computer science, economics, people, politics, prediction markets, probability, research, science, technology, trends, yahoo

A toast to the number 303: A redemptive election night for science, and The Signal

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The night of February 15, 2012, was an uncomfortable one for me. Not a natural talker, I was out of my element at a press dinner organized by Yahoo! with journalists from the New York Times, Fast Company, MIT Tech Review, Forbes, SF Chronicle, WIRED, Reuters, and several more [1]. Even worse, the reporters kept leading with, “wow, this must a big night for you, huh? You just called the election.”

We were there to promote The Signal, a partnership between Yahoo! Research and Yahoo! News to put a quantitative lens on the election and beyond. The Signal was our data-driven antidote to two media extremes: the pundits who commit to statements without evidence; and some journalists who, in the name of balance, commit to nothing. As MIT Tech Review billed it, The Signal would be the “mother of all political prediction engines”. We like to joke that that quote undersold us: our aim was to be the mother of all prediction engines, period. The Signal was a broad project with many moving parts, featuring predictions, social media analysis, infographics, interactives, polls, and games. Led by David “Force-of-Nature” Rothschild, myself, and Chris Wilson, the full cast included over 30 researchers, engineers, and news editors [2]. We confirmed quickly that there’s a clear thirst for numeracy in news reporting: The Signal grew in 4 months to 2 million unique users per month [3].

On that night, though, the journalists kept coming back to the Yahoo! PR hook that brought them in the door: our insanely early election “call”. At that time in February, Romney hadn’t even been nominated.

No, we didn’t call the election, we predicted the election. That may sound like the same thing but, in scientific terms, there is a world of difference. We estimated the most likely outcome – Obama would win 303 Electoral College votes, more than enough to return him to the White House — and assigned a probability to it. Of less than one. Implying a probability of more than zero of being wrong. But that nuance is hard to explain to journalists and the public, and not nearly as exciting.

Although most of our predictions were based on markets and polls, the “303” prediction was not: it was a statistical model trained on historical data of past elections, authored by economists Patrick Hummel and David Rothschild. It doesn’t even care about the identities of the candidates.

I have to give Yahoo! enormous credit. It took a lot of guts to put faith in some number-crunching eggheads in their Research division and go to press with their conclusions. On February 16, Yahoo! went further. They put the 303 prediction front and center, literally, as an “Exclusive” banner item on Yahoo.com, a place that 300 million people call home every month.

The Signal 303 prediction "Exclusive" top banner item on Yahoo.com 2012-02-16

The firestorm was immediate and monstrous. Nearly a million people read the article and almost 40,000 left comments. Writing for Yahoo! News, I had grown used to the barrage of comments and emails, some comic, irrelevant, or snarky; others hateful or alert-the-FBI scary. But nothing could prepare us for that day. Responses ranged from skeptical to utterly outraged, mostly from people who read the headline or reactions but not the article itself. How dare Yahoo! call the election this far out?! (We didn’t.) Yahoo! is a mouthpiece for Obama! (The model is transparent and published: take it for what it’s worth.) Even Yahoo! News editor Chris Suellentrop grew uncomfortable, especially with the spin from Homepage (“Has Obama won?”) and PR (see “call” versus “predict”), keeping a tighter rein on us from then on. Plenty of other outlets “got it” and reported on it for what it was – a prediction with a solid scientific basis, and a margin for error.

This morning, with Florida still undecided, Obama had secured exactly 303 Electoral College votes.

New York Times 2012 election results Big Board 2011-11-07

Just today Obama wrapped up Florida too, giving him 29 more EVs than we predicted. Still, Florida was the closest vote in the nation, and for all 50 other entities — 49 states plus Washington D.C. — we predicted the correct outcome back in February. The model was not 100% confident about every state of course, formally expecting to get 6.8 wrong, and rating Florida the most likely state to flip from red to blue. The Hummel-Rothschild model, based only on a handful of variables like approval rating and second-quarter economic trends, completely ignored everything else of note, including money, debates, bail outs, binders, third-quarter numbers, and more than 47% of all surreptitious recordings. Yet it came within 74,000 votes of sweeping the board. Think about that the next time you hear an “obvious” explanation for why Obama won (his data was biggi-er!) or why Romney failed (too much fundraising!).

Kudos to Nate Silver, Simon Jackman, Drew Linzer, and Sam Wang for predicting all 51 states correctly on election eve.

As Felix Salmon said, “The dominant narrative, the day after the presidential election, is the triumph of the quants.” Mashable’s Chris Taylor remarked, “here is the absolute, undoubted winner of this election: Nate Silver and his running mate, big data.” ReadWrite declared, “This is about the triumph of machines and software over gut instinct. The age of voodoo is over.” The new news quants “bring their own data” and represent a refreshing trend in media toward accountability at least, if not total objectivity, away from rhetoric and anecdote. We need more people like them. Whether you agree or not, their kind — our kind — will proliferate.

Congrats to David, Patrick, Chris, Yahoo! News, and the entire Signal team for going out on a limb, taking significant heat for it, and correctly predicting 50 out of 51 states and an Obama victory nearly nine months prior to the election.

Footnotes

[1] Here was the day-before guest list for the February 15 Yahoo! press dinner, though one or two didn’t make it:
-  New York Times, John Markoff
-  New York Times, David Corcoran
-  Fast Company, EB Boyd
-  Forbes, Tomio Geron
-  MIT Tech Review, Tom Simonite
-  New Scientist, Jim Giles
-  Scobleizer, Robert Scoble
-  WIRED, Cade Metz
-  Bloomberg/BusinessWeek, Doug MacMillan
-  Reuters, Alexei Oreskovic
-  San Francisco Chronicle, James Temple

[2] The extended Signal cast included Kim Farrell, Kim Capps-Tanaka, Sebastien Lahaie, Miro Dudik, Patrick Hummel, Alex Jaimes, Ingemar Weber, Ana-Maria Popescu, Peter Mika, Rob Barrett, Thomas Kelly, Chris Suellentrop, Hillary Frey, EJ Lao, Steve Enders, Grant Wong, Paula McMahon, Shirish Anand, Laura Davis, Mridul Muralidharan, Navneet Nair, Arun Kumar, Shrikant Naidu, and Sudar Muthu.

[3] Although I continue to be amazed at how greener the grass is at Microsoft compared to Yahoo!, my one significant regret is not being able to see The Signal project through to its natural conclusion. Although The Signal blog was by no means the sole product of the project, it was certainly the hub. In the end, I wrote 22 articles and David Rothschild at least three times that many.

computer science, economics, finance, fun, gambling, games, politics, prediction markets, probability, publication, research

Raise your WiseQ to the 57th power

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One of the few aspects of my job I enjoy more than designing a new market is actually building it. Turning some wild concept that sprung from the minds of a bunch of scientists into a working artifact is a huge rush, and I can only smile as people from around the world commence tinkering with the thing, often in ways I never expected. The “build it” phase of a research project, besides being a ton of fun, inevitably sheds important light back on the original design in a virtuous cycle.

In that vein, I am thrilled to announce the beta launch of PredictWiseQ, a fully operational example of our latest combinatorial prediction market design: “A tractable combinatorial market maker using constraint generation”, published in the 2012 ACM Conference on Electronic Commerce.

You read the paper.1  Now play the game.2 Help us close the loop.

PredictWiseQ Make-a-Prediction screenshot October 2012

PredictWiseQ is our greedy attempt to scarf up as much information as is humanly possible and use it, wisely, to forecast nearly every possible detail about the upcoming US presidential election. For example, we can project how likely it is that Romney will win Colorado but lose the election (6.2%), or that the same party will win both Ohio and Pennsylvania (77.6%), or that Obama will paint a path of blue from Canada to Mexico (99.5%). But don’t just window shop, go ahead and customize and buy a prediction or ten for yourself. Your actions help inform the odds of your own predictions and, crucially, thousands of other related predictions at the same time.

For example, a bet on Obama to win both Ohio and Florida can automatically raise his odds of winning Ohio alone. That’s because our market maker knows and enforces the fact that Obama winning OH and FL can never be more likely than him winning OH. After every trade, we find and fix thousands of these logical inconsistencies. In other words, our market maker identifies and cleans up arbitrage wherever it finds it. But there’s a limit to how fastidious our market maker can be. It’s effectively impossible to rid the system of all arbitrage: doing so is NP-hard, or computationally intractable. So we clean up a good bit of arbitrage, but there should be plenty left.

So here’s a reader’s challenge: try to identify arbitrage on PredictWiseQ that we did not. Go ahead and profit from it and, when you’re ready, please let me and others know about it in the comments. I’ll award kudos to the reader who finds the simplest arbitrage.

Why not leave all of the arbitrage for our traders to profit from themselves? That’s what nearly every other market does, from Ireland-based Intrade, to Las Vegas bookmakers, to the Chicago Board Options Exchange. The reason is, we’re operating a prediction market. Our goal is to elicit information. Even a completely uninformed trader can profit from arbitrage via a mechanical plug-and-chug process. We should reserve the spoils for people who provide good information, not those armed (solely) with fast or clever algorithms. Moreover, we want every little crumb of information that we get, in whatever form we get it, to immediately impact as many of the thousands or millions of predictions that it relates to as possible. We don’t want to wait around for traders to perform this propagation on their own and, besides, it’s a waste of their brain cells: it’s a job much better suited for a computer anyway.

Intrade offers an impressive array of predictions about the election, including who will win in all fifty states. In a sense, PredictWiseQ is Intrade to the 57th power. In a combinatorial market, a prediction can be any (Boolean) function of the state outcomes, an ungodly degree of flexibility. Let’s do some counting. In the election, there are actually 57 “states”: 48 winner-takes-all states, Washington DC, and two proportional states — Nebraska and Maine — that can split their electoral votes in 5 and 3 unique ways, respectively. Ignoring independent candidates, all 57 base “states” can end up colored Democratic blue or Republican Red. So that’s 2 to the power 57, or 144 quadrillion possible maps that newscasters might show us after the votes are tallied on November 6th. A prediction, like “Romney wins Ohio”, is the set of all outcomes where the prediction is true, in this case all 72 quadrillion maps where Ohio is red. The number of possible predictions is the number of sets of outcomes, or 2 to the power 144 quadrillion. That’s more than a googol, though less than a googolplex (maybe next year). To get a sense of how big that is, if today’s fastest supercomputer starting counting at the instant of the big bang, it still wouldn’t be anywhere close reaching a googol yet.

Create your own league to compare your political WiseQ among friends. If you tell us how much each player is in for, we’ll tell you how to divvy things up at the end. Or join the “Friends Of Dave” (FOD) league. If you finish ahead of me in my league, I’ll buy you a beer (or beverage of your choice) the next time I see you, or I’ll paypal you $5 if we don’t cross paths.

PredictWiseQ is part of PredictWise, a fascinating startup of its own. Founded by my colleague David Rothschild, PredictWise is the place to go for thousands of accurate, real-time predictions on politics, sports, finance, and entertainment, aggregated and curated from around the web. The PredictWiseQ Game is a joint effort among David, Miro, Sebastien, Clinton, and myself.

The academic paper that PredictWiseQ is based on is one of my favorites — owed in large part to my coauthors Miro and Sebastien, two incredible sciengineers. As is often the case, the theory looks bulletproof on paper. But I’ve learned the hard way many times that you don’t really know if a design is good until you try it. Or more accurately, until you build it and let a crowd of other people try it.

So, dear crowd, please try it! Bang on it. Break it. (Though please tell me how you did, so we might fix it.) Tell me what you like and what is horribly wrong. Mostly, have fun playing a market that I believe represents the future of markets in the post-CDA era, a.k.a the digital age.

__________
1 Or not.
2 Or not.

advertising, computer science, economics, history, personal, prediction markets, yahoo

Turning in my Yahoo! badge

Last day: Turning in my Yahoo! badge after 8 or 10 years, depending how you countOn Thursday April 26, 2012, I resigned from Yahoo! after nearly 10 without actively changing jobs. Here is the full text of the goodbye letter(s) I sent. It’s the kind of long-winded last salvo that few people actually read, and now I’m foisting it upon you, dear reader, but I can’t help myself. Writing it brought back many wonderful memories and a tinge of sadness at the end of a truly amazing work environment for me, but I found the exercise rewarding. I really appreciate the many kind words and well wishes: some were poignant and immensely gratifying. The feeling is mutual. If nothing else, throughout my career I have had the great fortune of working with amazing people who are equal parts brilliant, effective, and nice, including my bosses, peers, reports, and students.

——– Original Message ——–
Subject: last Yodle (and last corny Yodle joke)
Date: Wed, 25 Apr 2012 16:44:31 -0400
From: David Pennock

After 8 wonderful years (almost 10 if you include Overture), it is with
very mixed emotions that I leave Yahoo!. My last day is tomorrow,
Thursday April 2526. You can reach me in plenty of ways and I hope you do:

[my email address]
+1-732-XXX-XXXX
Y!IM pennockd | facebook pennockd | twitter pennockd | linkedin
http://dpennock.com | http://blog.oddhead.com

I’ve grown to love this company (purple blood, yada yada) and one of the
deep ironies is that I have a feeling Scott Thompson may actually know
what he is doing and that maybe just maybe Yahoo!’s return to revenue
growth and good public perception will finally come (note I didn’t say
return to profitability — a steady $1 billion in cashmoney profit in
our pocket every year is very far from shabby). I plan to hold on to
some of my stock.

In the early 2000s Google was an amazing Bem+Wom story yet almost no one
(me included) had a clue how they would make money. In 2002, Gary Flake
introduced me to Overture, a company already making hundreds of millions
on search, and suddenly it was clear. I joined Gary in what became
Overture Research and later, under Usama Fayyad’s protective wing, the
inception of “Yahoo! Research Labs”. When Gary left, we hired Prabhakar
and Ron. The rest is history. Andrei, Andrew, Raghu, Ravi, Ricardo,
Preston, Duncan. An absolutely amazing place that was my pleasure to
watch grow and mature. I still remember the excitement of our first
offsite at Half Moon Bay to map out the future of the place.* I remember
a fateful week when Preston, Duncan, and David Reiley simultaneously
gave up their tenure to stay at Yahoo!.

From the beginning Prabhakar saw the importance of including social
science research in the mix for online media. In my little corner, where
we mixed computer science and economics (“algorithmic economics” we
called it), I believe we had enormous effect both internally and
externally. In 2007, Jeff MacKie-Mason, one of our Big Thinker lecturers
and now Dean of the School of Information at the University of Michigan,
wrote (ok, informally to me in email) that our group was “the most
exciting and successful group I’ve seen crossing the CS/Econ boundary”.
If imitation is the sincerest form of flattery, I believe we had a
significant positive impact on the growth in hiring in the social
sciences and in algorithmic economics at both Google and Microsoft. In
our group alone, we published more than 70 papers including at least two
award winners (Arpita just this year). We literally wrote the book
(chapters) on sponsored search and prediction markets. We co-founded the
Ad Auctions Workshop and NYCE Day. People who left often did
fantastically well, including Yiling Chen to Harvard, Mohammad Mahdian
to Google, and Dan Reeves to found his own successful startup Beeminder.
We filed dozens of patents (take that fb!). Former intern Nicolas
Lambert who is now a Stanford professor once told me he hoped to one day
say “it all started at Yahoo!”. I just left a Ph.D. student’s defense
whose three (!) weeks at Yahoo! were good for two chapters in his
thesis. We’ve had academic visitors leave after a week here and follow
up that they wanted to apply for a job — the environment was that great.

Inside Yahoo!, we worked on sponsored search (“squashing” and so much
more by the incomparable Sebastien Lahaie, who we recently discovered is
the central hub of research in New York), display ads, and UGC among
many topics. My passion has been in prediction (markets), and some of my
best memories have been trying to play product manager for a day (or a
couple months) for Predictalot and The Signal. Often it felt more like
operating a startup but with incredible advantages in resources, people,
and of course access to that monster traffic firehose. This was Yahoo!
at it’s best — marshaling talent from all over the globe in many
divisions and specialties to produce a product that no one had ever seen
before, and that no one including us even knew would work. One of the
saddest parts of departing now is leaving The Signal behind, an
incredible effort and in many ways our biggest and best, led by David
“force-of-nature” Rothschild and so many people behind it. Sadly, some
were let go and others are leaving on the own accord, and we’ll never
know what could have been in a counterfactual universe. Yet I believe
The Signal will live on in the good hands of those who remain, including
Chris Wilson, Alex, Ingemar, and the absolutely phenomenal Bangalore team.

By far the best part of working at Yahoo! was the people. It’s been my
pleasure to work with so many fantastic colleagues in Labs and
throughout the company. In the recent turmoil many in Labs have been, as
Preston said, “evaluated by the market”, and came out looking pretty
darn good, with calls, interviews, and offers from the best companies
(Facebook, Google, Microsoft) and universities. Early on we set a goal
to always hire above the mean, and I truly believe we did that. (Having
been here from the beginning, you can see where that leaves me in this
incredible crowd.) It’s a cliche but a true one: I am only as good as
the people working with me, and I’ve truly been blessed with amazing
colleagues, bosses, employees, postdocs, and interns. To Sebastien,
Arpita, Giro, and David Rothschild, plus Mridul, Navneet, Sudar, Arun,
Shrikant, Kim, Chris, Janet, Ron, Michael and dozens more and everyone
who has come before, from Preston & Prabhakar on down, I can’t thank you
enough and I owe you almost everything.

Goodbye for now,
Dave

* For history buffs, these were the people at the initial Yahoo!
Research offsite: Prabhakar Raghavan, Dennis DeCoste, David Pennock,
Omid Madani, Shyam Kapur, Andrew Tomkins, Winton Davies, Ravi Kumar,
Bernard Mangold, Ron Brachman, Marc Davis, Michael Mahoney, Kevin Lang,
Seung-Taek Park, and Dan Fain.

** I also remember the first few days of Yahoo! Research New York in
2005, with just Ron, John, and I. It’s amazing to see what we have
become since.

*** An even more arcane note of history: the Overture control room made
a cameo as NASA Mission Control in James Cameron’s 2003 movie Ghosts of
the Abyss. I am on somewhere on the cutting room floor trying to muster
that awestruck look one gets upon seeing alien life for the first time.

——– Original Message ——–
Subject: one more thing
Date: Thu, 26 Apr 2012 11:20:01 -0400
From: David Pennock

I’ll abuse my final act of spam to add one more thing. For those of you
remaining, you’re in good hands with Ron. I believe he can do something
special with Labs. In case you’re not familiar with his background, Ron
is frighteningly smart (Princeton undergrad, Harvard Ph.D.), was a
pioneer in artificial intelligence, wrote a seminal book on Knowledge
Representation, served as President of AAAI, the main AI society, ran
research groups at Bell Labs & AT&T, and is a highly organized, fair,
diligent manager who listens actively, gets things done, and, in
addition is a genuinely nice person. Best of luck to everyone.

Next post: A dream job come true.

computer science, economics, events, nyc, people, prediction markets, research

Prediction Market PowWow at Yahoo! Research New York, August 2011

I am incredibly lucky. Last August, I spent three days straight thinking almost exclusively about one topic: prediction markets, mostly algorithms. Even better, I was in great company: eleven incredible visitors from across the country took time out of their busy schedules to join me at Yahoo! Research NYC in an impromptu “prediction market powwow”: Yiling Chen, Sanmay Das, Lance Fortnow, Nicolas Lambert, Abe Othman, Mike Ruberry, Rahul Sami, Florian Teschner, Jenn Wortman Vaughn, Christof Weinhardt, and Lirong Xia. (Plus fellow Yahoos Miro Dudik, Sebastien Lahaie, and David Rothschild.) It’s amazing to have a job that allows this kind of time for research and blue-sky thinking: thanks Yahoo!. It’s humbling to have such stellar colleagues to work with: thanks everyone who came. It’s also wonderful to see “the kids” (former interns and postdocs) doing so well: Rahul now has tenure at U Michigan, Yiling is a professor at Harvard, Jenn is a professor at UCLA, and Nicolas is a professor at Stanford. (Lirong: You’re next!)

Here are our notes and here is a photo:

Prediction Market Powwow Yahoo! Research Aug 2011

fun, gambling, law, prediction markets, sports, startups, technology

Slipjockey: A marketplace for buying and selling Las Vegas bet slips

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In late 2010, I began talking to a very early-stage startup named Slipjockey, based in Salt Lake City. When we first started corresponding, Slipjockey was little more than a good idea coupled with some very basic technology and passionate co-founders. In the time since, Slipjockey has taken appropriate steps to bring their concept to market, including securing a favorable legal opinion and filing a patent for their technology.

The core concept of Slipjockey is ingenious. It’s a marketplace for buying and selling Las Vegas bet slips. The process starts when someone makes a bet at a licensed Nevada race and sports book. If he or she wants to sell the bet slip for whatever reason — suppose the predicted team is winning in a landslide at halftime and the slip has doubled in value — they can log onto Slipjockey and list it for sale. Another Slipjockey user may agree to buy it. The buyer takes ownership of the bet slip and he or she can keep it or resell it again to another Slipjockey user, etc. The final owner of any bet slip is paid in full directly from the sports book that originally issued the ticket.

Real-time trading on Slipjockey is similar to the action on betting exchanges like Betfair. The key difference is that all wagers must originate from a licensed Nevada race and sports book where gambling is legal.

The Slipjockey business concept grew from the notion that handicappers should have an option other that win, lose, or push. Slipjockey provides that fourth option by enabling handicappers to terminate their outcome risk, locking in a gain or avoiding a total loss prior to the end of the event. With the growth in live betting (aka “in-running betting”) around the world, and in Las Vegas courtesy of Cantor Fitzgerald, it’s clearly an option that people want.

Initially, Slipjockey is focused on launching with coverage of US football, tennis, and golf before expanding into other sports.

I’ve spoken mainly with Ryan Eads and his brother Rory, two of the co-founders. They are smart, well spoken, and tireless entrepreneurs. I have every expectation that, to the extent this idea has wings — and I believe it does — they will make it fly.

The first question you’re likely to ask is: is this legal? Indeed, that’s the first question I asked Ryan. As a pre-condition to launching, he secured a legal opinion from a former Nevada Gaming Control Board attorney that says, in effect, that because bets originate in Las Vegas and are ultimately paid out in Las Vegas, the Slipjockey exchange is legal. The attorney’s opinion is just that: an opinion, and not a guarantee. But it is convincing and credible. Certainly Slipjockey users are safe.

Currently, Slipjockey is inviting users to participate in a soft launch for trading National Football League games. To participate, create a profile at www.slipjockey.com and send an email to info@slipjockey.com. Mention that you read my blog post and I’m sure they’ll send you an invitation containing all the details if they have spots remaining.

computer science, economics, events, nyc, prediction markets, research

Two upcoming NYC-area CS-econ events: AMMA & NYCE Day

  1. The Second Conference on Auctions, health more about Market Mechanisms and Their Applications (AMMA) is next Monday and Tuesday August 22-23, generic view 2011, web at CUNY in midtown manhattan. The program, including contributed talks on school choice, prediction markets, advertising, and market design, and invited talks by market designer extraordinaire Peter Cramton and private company stock exchange SecondMarket (where millionaires buy Facebook), look to be excellent. Hope to see you there!
  2. The fourth annual New York Computer Science and Economics Day (NYCE Day) is Friday, September 16, 2011, at NYU. You have until next Friday August 26 to submit a short talk or poster. The goal of the meeting is to bring together researchers in the larger New York metropolitan area (read: DC-Boston-Chicago) with interests in computer science, economics, marketing, and business, and a common focus in understanding and developing the economics of Internet activity.
commentary, people, personal, prediction markets

On Intrade CEO John Delaney’s death

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A few words on the tragic death last May of John Delaney, the founder and CEO of prediction market company Intrade. John died near the peak of Mount Everest, climbing toward one of his life’s dreams and leaving behind a wife and three children, including one born only days before he died that he never met.

John founded Tradesports, a pre-cursor to Intrade, in 2000. Eventually, the non-sports contracts on Tradesports where spun off as Intrade, and Tradesports was shut down in 2008, in hopes of obtaining U.S. regulatory approval. I remember marveling at the technology, featuring ajax-ian magic like push updates — new bids appeared and filled bids disappeared live in a flash of color — well before its time, before we even knew what to call it.

The prediction market community embraced John, and John them. John was happy to take academics’ quixotic market ideas — like combinatorial markets, decision markets, merger markets, tax markets, or search engine markets — and float them on Tradesports or Intrade, and share back data for academic studies. I remember when we learned a Director at Intrade would speak at the first Prediction Markets Summit in 2005, we were thrilled to hear from a pioneer and innovator: one of the “big guns”. Chris Hibbert asked, “isn’t Tradesports the largest prediction market in the world?” It was hard to say: in a way, yes, it was and still is the largest market widely identified with the adjective ‘prediction’, but of course it depends how you define it: does Betfair count? Vegas? Stock options? If I recall, John himself spoke remotely at the second PM summit in New York.

Intrade became the prototypical example of a prediction market, mentioned in almost every academic paper on the subject. In 2008, Betfair, a goliath to Intrade’s David in terms of revenue and profit, got so annoyed they lashed out and sent the following attack on Intrade and defense of their own service dubbed Betfair Predicts (now shuttered):

InTrade’s election charts are republished frequently—despite continuing
problems with market manipulation.

Betfair is the world’s largest commercial prediction market with $33
Billion per year flowing through its exchange and is well known for
integrity and advanced technology…

I don’t believe I met John in person, but he and I emailed a bit, and beyond being whip smart and a fantastic entrepreneur, John was simply an incredibly nice guy. He kept repeating, at the end of nearly every email, that I must come to London so we could meet and have a beer. Talking to others, it seems I am far from alone in this standing offer from John. On the original prediction market mailing list, John Delaney was always the peacemaker: always diplomatic and rising about some surprisingly testy exchanges. He always spoke to raise the prominence of the field as a whole, ahead of his own interests with Intrade, not only believing but acting on his belief that “a rising tide lifts all boats”.

John didn’t seem like the type to seek out risk for the simple thrill of it; rather, he took calculated risks in business and life to progress. His success at work and at home attest to this. In hindsight, it’s easy to say he calculated wrong in attempting to climb Everest, but especially among prediction market proponents we know that decisions cannot be evaluated in hindsight. Decisions must be judged based on the information available at the time the decision is made. My guess is that John knew the risks and felt the climb was a gamble worth taking in an effort to achieve a long-standing goal and to accomplish a feat few others on the planet can claim.

John, you will be sorely missed, but your legacy lives on at Intrade, in the prediction market community, among your family and friends, and in the business world, sadly and suddenly now missing one of it’s great entrepreneurs with a spirit of adventure.

advertising, artificial intelligence, computer science, economics, events, finance, incentives, prediction markets, probability, publication, research

2011 ACM Conference on Electronic Commerce and fifteen other CS conferences in San Jose

If you’re in the Bay Area, come join us at the 2011 ACM Conference on Electronic Commerce, June 5-9 in San Jose, CA, one of sixteen conferences that comprise the ACM Federated Computing Research Conference, the closest thing we have to a unified computer research conference.

The main EC’11 conference includes talks on prediction markets, crowdsourcing, auctions, game theory, finance, lending, and advertising. The papers span a spectrum from theoretical to applied. If you want evidence of the latter, look no further than the roster of corporate sponsors: eBay, Facebook, Google, Microsoft, and Yahoo!.

There are also a number of interesting workshops and tutorials in conjunction with EC’11 this year, including:

Workshops:

  • 7th Ad Auction Workshop
  • Workshop on Bayesian Mechanism Design
  • Workshop on Social Computing and User Generated Content
  • 6th Workshop on Economics of Networks, Systems, and Computation
  • Workshop on Implementation Theory

Tutorials:

  • Bayesian Mechanism Design
  • Conducting Behavioral Research Using Amazon’s Mechanical Turk
  • Matching and Market Design
  • Outside Options in Mechanism Design
  • Measuring Online Advertising Effectiveness

The umbrella FCRC conference includes talks by 2011 Turing Award winner Leslie G. Valiant, IBM Watson creator David A. Ferrucci, and CMU professor, CAPTCHA co-inventor, and Games With a Purpose founder Luis von Ahn.

Hope to see many of you there!

gambling, games, prediction markets, research, review, startups

Crowdpark: Taking Facebook and now Florida by storm

Crowdpark logoCrowdpark is an impressive, well-designed prediction market game that’s already attracted 500,000 monthly active users on Facebook, the 11th fastest growing Facebook app in April.

It’s a dynamic betting game with an automated market maker, not unlike Inkling Markets in functionality (or even Predictalot minus the combinatorial aspect). What stands out is the flashy UI, both literally and figuratively. The look is polished, slick, refreshing, and richly drawn. It’s also cutesy, animation-happy, and slow to load. Like I said, Flash-y in every way. The game is well integrated into Facebook and nicely incorporates trophies and other social rewards. Clearly a lot of thought and care went into the design: on balance I think it came out great.

Crowdpark is a German company with an office in San Francisco. In addition to their Facebook game, they have German and English web versions of their game, and white-label arrangements with gaming companies. They launched in English just last December.

Crowdpark’s stunning growth contrasts with decidedly more mixed results on this side of the Atlantic. I wonder how much of Crowdpark’s success can be attributed to their German roots, their product, their marketing, or other factors?

Crowdpark has an automated market maker they call “dynamic betting” that I can’t find any technical details about [1]. Here’s their well-produced video explanation:

They say it’s “patent pending”, though my colleague Mohammad Mahdian did some nice reverse engineering to show that, at least in their Facebook game, they’re almost certainly using good-old LMSR. Here is a graph of Crowdpark’s market maker price curve for a bet priced at 1%:

Crowdpark's automated market maker price curve

Here is the raw data and the fit to LMSR with b=20,000.

risk   to win (CP)   to win (LMSR)
1 91 91.079482
2 181 181.750593
5 451 451.350116
10 892 892.847929
20 1747 1747.952974
50 4115 4115.841760
100 7535 7535.378665
200 13019 13019.699483
500 23944 23944.330406
600 26594 26594.687310
700 28945 28945.633048
800 31059 31059.076097
900 32979 32979.512576
1000 34740 34740.000000

Still, there’s a quote buried in the video at 0:55 that caught my attention: “you’re current profit is determined by the fluctuation of the odds”.

There’s only one market maker that I know of where the profit fluctuates with the odds, and that’s my own dynamic parimutuel market, which by coincidence recently went from patent pending to inventor cube delivered. 🙂

David Pennock's dynamic parimutuel market (DPM) patent cube - 4/2011

With every other market maker, indeed almost every prediction market, the profit is fixed at the time of the bet. Add to that the fact that Crowdpark bought a majority stake in Florida horse racing circuit Saratoga Racing Inc. and plans to operate all bets exclusively through their system, leads me to wonder if they may have some kind of parimutuel variant, the only style of betting that is legal in the US.

Of course, it may be that I simply misinterpreted the video.

[1] The technical exec at Crowdpark seems to be Aleksandar Ivanov. I found a trade press paper on (internal) prediction markets he wrote in 2009 for the Journal of Business Forecasting.

artificial intelligence, computer science, economics, finance, fun, gambling, prediction markets, probability, review, sports, startups

There’s a new oracle in town

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Cantor Gaming mobile device for in-running bettingLast January, a few friends and I visited the sportsbook at the M Casino in Las Vegas, one of several sportsbooks now run by Cantor Gaming, a division of Wall Street powerhouse Cantor Fitzgerald. Traditional sportsbooks stop taking bets when the sporting event in question begins. In contrast, Cantor allows “in-running betting”, a clunky phrase that means you can bet during the event: as touchdowns are scored, interceptions are made, home runs are stolen, or buzzers are beaten. Cantor went a step further and built a mobile device you can carry around with you anywhere in the casino to place your bets while watching games on TV, drink in hand. (Cantor also runs spread-betting operations in the UK and bought the venerable Hollywood Stock Exchange prediction market with the goal of turning it into a real financial exchange; they nearly succeeded, obtaining the green light from the CFTC before being shut down by lobbyists, er, Congress.)

Back to the device. It’s pretty awesome. It’s a Windows tablet computer with Cantor’s custom software — pretty well designed considering this is a financial firm. You can bet on the winner, against the spread, or on one-off propositions like whether the offensive team in an NFL game will get a first down, or whether the current drive will end with a punt, touchdown, field goal, or turnover. The interface is pretty nice. You select the type of bet you want, see the current odds, and choose how much you want to bet from a menu of common options: $5, $10, $50, etc. You can’t bet during certain moments in the game, like right before and during a play in football. When I was there only one game was available for in-running betting. Still, it’s instantly gratifying and — I hate to use this word — addictive. Once my friend saw the device in action, he instantly said “I’m getting one of those”.

When I first heard of Cantor’s foray into sports betting, I assumed they would build “betfair indoors”, meaning an exchange that simply matches bettors with each other and takes no risk of its own. I was wrong. Cantor’s mechanism is pretty clearly an intelligent automated market maker that mixes prior knowledge and market forces, much like my own beloved Predictalot minus the combinatorial aspect. Together with their claim to welcome sharps, employing a market maker means that Cantor is taking a serious risk that no one will outperform their prior “too much”, but the end result is a highly usable and impressively fun application. Kudos to Cantor.

P.S. Cantor affectionately dubbed their oracle-like algorithm for computing their prior as “Midas”, proving this guy has a knack for thingnaming.