The basic game play is simple: select a prediction type, customize it, and invest points on it. Yet you’ll never run out of odds to explore: there are hundreds of millions of predictions you can make. The odds on each update continuously based on other players’ predictions and the on-court action.

Predictalot is a Yahoo! App, so you can play it at apps.yahoo.com or you can add it to your Yahoo! home page. I have to admit, it’s an incredible feeling to play a game I helped design right on the Yahoo! home page.

That’s all you need to get started. If you’re curious and would like a peek under the hood, read on: there’s some interesting technology hidden in the engine.

Background and Details

Predictalot is a true combinatorial prediction market of the sort academics like us and Robin Hanson have been dreaming about since early in the decade. We built the first version during an internal Yahoo! Hack Day. Finally, we leveraged the Yahoo! Application Platform to quickly build a public version of the game. (Note that anyone can develop a YAP app that’s visible to millions — there’s good sample code, it supports YUI and OpenSocial, and it’s easy to get started.) After many fits and starts, late nights, and eventually all nights, we’re proud and excited to go live with Predictalot version 1.0. I can’t rave enough about the talent and dedication of the research engineers who gave the game a professional look and feel and production speed, turning a pie-in-the-sky idea into reality. We have many features and upgrades in mind for future versions, but the core functionality is in place and we hope you enjoy the game.

In the tournament, after the play-in game, the 64 top college basketball teams play 63 games in a single elimination tournament. So there are 2 to the power 63 or 9.2 quintillion total possible outcomes, or ways the entire tournament can unfold. Predictalot implicitly keeps track of the odds for them all. To put this in perspective, it’s estimated that there are about 10 quintillion individual insects on Earth. Of course, for all practical purposes, we can’t store 9.2 quintillion numbers, even with today’s computers. Instead, we compute the odds for any outcome on the fly by scanning through the predictions placed so far.

A prediction is a statement, like Duke will win in the first round, that will be either true or false in the final outcome. In this case, the prediction is true in exactly half, or 2 to the power 62 outcomes. (Note this does not mean the odds are 50% — remember the outcomes themselves are not all equally likely.) In theory, Predictalot can support predictions on any set of outcomes. That’s 2 to the power 2 to the power 63, or more than a googol predictions. For now, we restrict you to “only” hundreds of millions of predictions categorized into thirteen types. Computing the odds of a prediction precisely is too slow. Technically, the problem is #P-hard: as hard as counting SAT and harder than the travelling salesman problem. So we must resort to approximating the odds by randomly sampling the outcome space. Sampling is a tricky business — equal parts art and science — and we’re still actively exploring ways to increase the speed, stability, and accuracy of our sampling.

Because we track all possible outcomes, the predictions are automatically interconnected in ways you would expect. A large play on Duke to win the tournament instantly and automatically increases the odds of Duke winning in the first round; after all, Duke can’t win the whole thing without getting past the first round.

With 9.2 quintillion outcomes, Predictalot is to our knowledge the largest prediction market built, testing the limits of what the wisdom of crowds can produce. Predictalot is a game, and we hope it’s fun to play. We’d also like to pave the way for serious use of combinatorial prediction market technology.

Why did Yahoo! build this? Predictalot is a smarter market, letting humans and computers each do what they do best. People enter predictions in simple terms they understand like how one team fares against another. The computer handles the massive yet methodical number crunching needed to combine all the pieces together into a coherent overall prediction of a complex event. Markets like Predictalot, WeatherBill, CombineNet, and Internet advertising systems, to name a few, represent the evolution of markets in the digital age, empowering users with extreme customization. More and more, matching buyers with sellers — the core function of markets — requires sophisticated algorithms, including machine learning and optimization. Predictalot attempts to illustrate this trend in an entertaining way.

David Pennock
Mani Abrol, Janet George, Tom Gulik, Mridul Muralidharan, Sudar Muthu, Navneet Nair, Abe Othman, Daniel Reeves, Pras Sarkar

First off, I cheated. I went to sleep in a hotel room rather than hack all through the night. (Even in college I woke up at 4am rather than pull an all nighter.) Still, I made decent progress on some pet projects including combinatorial betting. Daniel, Sharad, and Winter from Yahoo! Research New York participated for real, working through the night. Returning in the morning showered and caffeinated to greet the sleepwalkers was a little surreal. A number of ex-Yahoos joined the festivities including David Yang, Mor Naaman, and Chad Dickerson. (Havi joked that Yahoo! is like finishing school for entrepreneurs. If you count Yahoo! capture and releases like Mark Cuban and Paul Graham, the spreading influence is enormous.)

Clay Shirky kicked off the event. He’s a fantastic speaker — watch his talk here. His punch line — that successful communities like facebook, twitter, flickr, and wikipedia start small and cohesive (as opposed to large and fragmented: see Yahoo! 360) — was aimed perfectly at the many founders and foundreamers in the audience. There were speakers from Mint and foursquare and tutorials on the Yahoo! Application Platform, Yahoo! Query Language (the most popular service), Yahoo! TV widgets, and more. There was a round of Ignite NYC, a barrage of twenty-slides-in-five-minutes talks, some educational (geek’s guide to patents), some charitable (aid to South America), some hilarious (spaceman from outerspace), some thought provoking (makerbot 3d printers), and many all of the above (meta mechanical turk; the Emoji translation of Moby Dick). Watch the Ignite talks here.

A bunch of small touches made the event memorable, including a steampunk-themed hacking hall complete with retroRed Victorian couches, portraits of hackers through history, funky tweet-streaming sculptures, chalk drawings of old patents, power cords dangling from hanging bird cages, and a guitarhero-foosball corner. The food was tasty and at times eccentric, like the hot dog stand and toppings bar under a rainbow umbrella, ice cream cart, and old-fashioned popcorn machine. There was plenty of beer, coffee, red bull, sliders, and cookies, and even (gasp) vegan fare, salmon, and salad.

I give the event an A for style (decor, food) and content (talks, hacks, organization). The one sour note was the wireless — certainly a key ingredient for a good hack day — which began flaky and ended slow but acceptable.

I attended the YAP tutorial and created a rudimentary application. I was pleasantly surprised how simple the process was — the documentation and sample code are great. You can get the hello world app (complete with social hooks) running and add some ajax magic within minutes.

By far one of the coolest sights was the MakerBot Industries 3D printer in action. It sucks in plastic wire, melts it, and deposits it in perfect formation to produce coins, busts, parts for itself, or almost anything in the thingiverse. For Hack Day, the device printed news headlines in peanut butter on toast. We met an nyc resistor who was working on a conveyer belt mechanism for his own MakerBot printer, and he invited us to craft night at their shared hackspace in Brooklyn (a place that would be heaven for my dad and brother; Sharad, Jake, Daniel, and Bethany went to check it out).

I missed the tutorial on Yahoo! TV widgets but I’d like to learn more. They are now in most major TV brands including Sony, Samsung, and LG — millions of sets around the world in the coming months. (The Sony won editor’s choice in the Sept 2009 issue of Wired magazine; the Samsung and LG rated close behind. The sole TV reviewed without Yahoo! Widgets, a Panasonic, was ridiculed for is clunky Viera Cast online interface.) If you’re an internet video startup, like my friend, you need a widget channel. Personally, I’d love to see a sports game tracker that highlights pivotal moments by monitoring in-game betting odds.

Footnote: Two Yahoos made a humorous video (that’s both self-promotional and -deprecating) on what people in Times Square think ‘hacker’ means:

See Paul Tarjan and Christian Heilmann for real definitions.

The 24-Hour Hackathon begins Friday afternoon. We encourage you to play around with Yahoo!’s Open Platforms and APIs like YAP, YQL, YUI, TVWidgets, our Social APIs, and more. And of course, feel free to use other APIs, developer tools and whatever software/hardware floats your boat…

At the end of the 24 hours, the hackers will have the chance to debut their hack and winners will be awarded with some enviable prizes…

And of course we will keep you well fed and hydrated throughout the two days. There will also be sleeping areas in case you want to take a nap.

Previous: the what and why of Open Hack.

I told the audience that they could learn more by searching for “cftc yahoo google” in their favorite search engine, showing the Yahoo! Search results with MidasOracle’s coverage at the top.²

It turns out that was poor advice. 63.7% of the audience probably won’t find what they’re looking for using that search.³

If some search engines don’t surface the MidasOracle post, I’m hoping they’ll find this.

And back to the effort to guide the CFTC: I hope other people and companies will join. The CFTC’s request for help itself displays a clear understanding of the science and practice of prediction markets and a real willingness to listen. The more organizations that speak out in support, the greater chance we have of convincing the CFTC to take action and open the door to innovation and experimentation.

And there’s no doubt that digital representations of social ties add value. Facebook does improve people’s lives.¹

The puzzle, and one of the key challenges facing companies like Facebook, Google, and Yahoo!., is how social media can make money. So far the evidence is most users won’t pay directly, which leaves ideas like virtual goods, community marketplaces, app stores, and, of course, advertising. Unfortunately, although we know great ways to advertise to people searching, and decent ways to advertise to people viewing content, it’s less clear how to advertise to people communicating.

P&G’s Ted McConnell puts it bluntly:

What in heaven’s name made you think you could monetize the real estate in which somebody is breaking up with their girlfriend?

Riffing off of this quote, Wired asks the $15 billion question: Is social advertising an oxymoron?:

So, what if social media and advertising just don’t mix?

SocialMedia.com, a social advertising startup, begs to differ (hat tip to Cong Yu), reacting to the same provocative McConnell quote. Their answer:

Advertisers only pay when users volunteer to say something about the brand to their friends.

Indeed, this sort of paid version of Bem+Wom (“BEtter Mousetrap + Word Of Mouth”) — more on this in the next post — is one of the first things people think of when pondering how to monetize a social network. But can it work well and if so, how?

It’s a great resource for students to learn about the area and find meaty research problems. There’s also a chance for graduate students to earn $5000 in seed funding, work with Yahoo! Research scientists and data, and attend a summit of like-minded students and scientists.

The challenges cover search, machine learning, data management, information extraction, economics, social science, statistics, multimedia, and computational advertising.

Here’s the list of challenges from the algorithmic economics group, my group. We hope it provides a clear picture of the goals of our group and the areas where progress is most needed.

We look forward to supporting students who love a challenge and would like to join us in building the next-generation Internet.

On January 13, my company named Carol Bartz, a self-avowed math nerd and former punch-card carrying member of her college computer club, as its CEO. In her own words:

I was a real nerd. I love, love, love, love math. Back in the late ’60s, math meant being a teacher if you were a woman. I wasn’t interested in teaching. Then I took my first computer course. It was crazy. It was like math, only more fun. I switched to computer science.

Exactly one week later, on January 20, my country turned over executive control to Barack Obama, a CrackBerry addicted comic book geek. In his inauguration speech, Obama vowed to “restore science to its rightful place”, “wield technology’s wonders”, and even addressed “non-believers” — wording that in any sane universe should be entirely unremarkable, yet in ours appears to represent an unprecedented milestone.

I can’t recall a two-week span filled with so much geek pride and cautious optimism.

Back to the Carol Bartz quote. Reading it brings a smile to my face. It also reminds me of my mom, who, convinced it was her only option, taught middle school for a few years before returning to medical school to pursue her passion, enjoying a successful career as one of the first women radiologists.

I highly recommend Bartz’s essay, which mixes biography with prescience and insight. Bartz describes how technology and the Internet are transforming collaboration and improving productivity, at the same time ushering in an era of information overload, email bankruptcy, and misuse of the extra time technology affords. Remarkably, she wrote about these things in 1997!

It’s amazing to think how things have changed since 1997. My own first web experience, courtesy Mosaic, came in 1994, the same year Yahoo! was founded. In 1996, PayPal predecessor and public company First Virtual wrote their own keystroke-sniffing malware as a stunt to bolster their urgent call to “NEVER TYPE YOUR CREDIT CARD NUMBER INTO A COMPUTER”. Ebay was founded in 1995, PayPal in 1998. In 1997, Friendster had neither come nor gone, and Facebook CEO Mark Zuckerberg was 13.

Yet Bartz’s words seem more relevant than ever today.

The paper describes how web search engines may be used to monitor and predict flu outbreaks. We studied four years of data from Yahoo! Search together with data on flu outbreaks and flu-related deaths in the United States. All three measures rise and fall as flu season progresses and dissipates, as you might expect. The surprising and promising finding is that web searches rise first, one to three weeks before confirmed flu cases, and five weeks before flu-related deaths. Thus web searches may serve as a valuable advance indicator for health officials to spot the onset of diseases like the flu, complementary to other indicators and forecasts.

On November 11, the New York Times broke a story about Google Flu Trends, along with an unusual announcement of a pending publication in the journal Nature.

I haven’t read the paper, but the article hints at nearly identical results:

Google … dug into its database, extracted five years of data on those queries and mapped it onto the C.D.C.’s reports of influenzalike illness. Google found a strong correlation between its data and the reports from the agency…

Tests of the new Web tool … suggest that it may be able to detect regional outbreaks of the flu a week to 10 days before they are reported by the Centers for Disease Control and Prevention.

To the reporter’s credit, he interviewed Phillip and the article does mention our work in passing, though I can’t say I’m thrilled with the way it was framed:

The premise behind Google Flu Trends … has been validated by an unrelated study indicating that the data collected by Yahoo … can also help with early detection of the flu.

giving (grudging) credit to Yahoo! data rather than Yahoo! people.

The story slashdigged around the blogomediasphere quickly and thoroughly, at one point reaching #1 on the nytimes.com most-emailed list. Articles and comments praise how novel, innovative, and outside-of-the-box the idea is. The editor in chief of Nature praised the “exceptional public health implications of [the Google] paper.”

I’m thrilled to see the attention given to the topic, and the Google team deserves a huge amount of credit, especially for launching a live web site as a companion to their publication, a fantastic service of great social value. That’s an idea we had but did not pursue.

In the business world, being first often means little. However in the world of science, being first means a great deal and can be the determining factor in whether a study gets published. The truth is, although the efforts were independent, ours was published first — and Clinical Infectious Diseases scooped Nature — a decent consolation prize amid the go-google din.

Update 2008/11/24: We spoke with the Google authors and the Nature editors and our paper is cited in the Google paper, which is now published, and given fair treatment in the associated Nature News item. One nice aspect of the Google study is that they identified relevant search terms automatically by regressing all of the 50 million most frequent search queries against the CDC flu data. Congratulations and many thanks to the Google/CDC authors and the Nature editors, and thanks everyone for your comments and encouragement.

At dinner the night before,¹ the organizers agreed that we were nervous because we weren’t at all nervous. Karin and Renee from the New York Academy of Sciences had taken care of almost everything, leaving little for us to fret about. It turned out we were right to not worry and wrong to worry about not worrying: indeed Karin, Renee, and NYAS were absolutely fantastic, orchestrating every detail of the event flawlessly, from technology to catered breaks. The venue itself is gorgeous — a well laid-out space in a modern building in the World Trade Center complex with stunning views² and a number of nice touches, from an alcove with a computer station to check email to a subtle gradient in the wallpaper that slowly pixilates as your gaze moves from the center toward the side of the room. I came away incredibly impressed with NYAS and delighted to become a member.

Muthu provides an excellent summary of the event, divided into before and after lunch. Read that first and then come back here for my additional thoughts/notes:

1. Costis gave us mostly bad news. He summarized some of his award winning work with Christos Papadimitriou and Paul Goldberg proving that computing equilibrium behavior in almost any moderately complex game may be beyond the reach of our computers,³ let alone our brains. As a saying goes, “if your laptop can’t find it, then neither can the market” [attribution: Kamal Jain?]. Still, all may not be lost. These results, as is the nature of computational complexity results, say only that some games are extremely hard to solve, not all games or even most games. Since nature is not adversarial (Murphy’s Law aside), it may be the case that among games that arise in the real world that we care about, a number of them can be solved for equilibrium. The problem is defining what “realistic” means in this context: an almost impossibly fuzzy task. Costis did end with some positive results, showing that anonymous games can be solved efficiently. Anonymous games crop up in realistic situations, for example in analyzing traffic, where only the quantity of cars near you matters and not the identity of the drivers inside.




2. Asim described a sophisticated Bayesian model well suited for social network data that handles non-existant links — meaning the lack of connection between two people, by far the most common situation — much better than previous approaches. The approach is good for digging deeply into a small data set but at least for now has difficulty with moderately large amounts of data. (To get results in a reasonable amount of time, Asim had to down sample his already fairly modest sized corpus.) The talk didn’t help me overcome my bias that Bayesian methods ala UAI often don’t work well at Internet scales without modification.




3. Susan gave a fantastic and energetic talk. She advocates economic models of online advertising that include more sophisticated users, as opposed to typical models that assume users scan from the top of the page down in a precise sequence. She went further to claim that users may actually choose their search engine based on the quality of the ads. Personally, I’m a bit skeptical about that, though I do agree that there is an indirect effect: search engines with better paying ads can afford to buy more traffic and improve their algorithmic search more. Susan highlighted the enormous shift in mindset required between economic theory and practice when just computing the mean of a data stream can take weeks (though this is changing with tools like Hadoop that can bring such computations down to hours or minutes as Sebastien confirms).




4. Someone asked Tuomas why his expressive commerce company CombineNet uses first-price auctions instead of VCG pricing. He listed four of what he said were dozens of reasons on top of Rothkopf’s thirteen and Ausubel and Milgrom’s list. In fact he went further to say that as far as he knew no real auction anywhere in the world has ever used true VCG pricing for anything more complicated than selling a single good at a time.




5. For those not familiar, a rump session is open to anyone to speak briefly on any relevant topic. As it turns out, in part because brevity forces clarity, and in part because editorial filtering overweights mediocrity, the rump session is often the most interesting part of a conference. The “NYCE rump” session was no exception, with topics spanning ad auctions, reputation, Internet routing, and user generated content. Ivy Li proposed a clever scheme whereby eBay sellers are motivated to reward buyers for honest feedback. Sebastien presented work with Sihem and I on an expressive bidding language for online advertising with fast allocation and pricing algorithms, with the goal of moving the industry toward an open standard. Sampath Kannan on leave at NSF had encouraging news on the funding front, laying out his vision for CS theory funding with an explicit call for proposals at the boundary of CS and economics.




6. I think we did a good job of attracting a diversity of speakers and participants, with talks ranging from computational complexity to Bayesian models of social networks, with academia and industry represented, and with CS, economics, and business backgrounds represented.

Hope to see many folks there.