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!

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.

Take a look at the middle right of the second page: it says “Play exciting games and win big” and features… Predictopus! That’s the Indian spinoff of Predictalot, the combinatorial prediction game I helped invent.

Predictopus has nearly 70,000 users and counting, and this ad certainly won’t hurt.

Yahoo!!!

BTW, I grabbed these images from an amazing site called Press Display, which I discovered via the New York Public Library.

Times of India Mumbai edition
30 Mar 2011

Times of India Mumbai edition
30 Mar 2011

Also, congrats India, and thanks! I nearly doubled my virtual bet with the victory:

Predictalot v0.3 is easier to play. We whittled down the ‘Make Prediction’ process from four steps to just two. Even if you don’t want to wager, with one click come check out the projected odds of nearly any crazy eventuality you can dream up.

Please connect to facebook and/or twitter to share your prediction prowess with your friends and followers. You’ll earn bonus points and my eternal gratitude.

The odds start off at our own prior estimate based on seeds and (new this year) the current scores of ongoing games, but ultimately settle to values set by “the crowd” — that means you — as predictions are bought and sold.

For the math geeks, Predictalot is a combinatorial prediction market with over 9 quintillion outcomes. Prices are computed using an importance sampling approximation of a #P-hard problem.

What kind of information can we collect that a standard prediction market cannot? A standard market will say that Texas A&M is unlikely to win the tournament. Our market can say more. Yes, A&M is unlikely to reach the Final Four and even more unlikely to win apriori, but given that they somehow make it to the semifinals in Houston, less than a two hour drive from A&M’s campus, their relative odds may increase due to a home court advantage.

Here’s another advantage of the combinatorial setup. A standard bookmaker would never dare to offer the same millions of bets as Predictalot — they would face nearly unlimited possible losses because, by tradition, each bet is managed independently. By combining every bet into a single unified marketplace, we are able to limit the worst-case (virtual) loss of our market maker to a known fixed constant.

I’m thrilled to report that Predictalot had an Indian makeover, launching as Predictopus* for the ICC Cricket World Cup. The Yahoo! India team did an incredible job, leveraging the idea and some of the code base of Predictalot, yet making it their own. Predictopus is not a YAP — it lives right on the Yahoo! Cricket website, the official homepage for the ICC Cricket World Cup. They’re also giving away Rs 10 lakhs — or about $22,000 if my calculations are correct — in prizes. Everything is bigger in India, including the crowds and the wisdom thereof. It will be great to see the game played out on a scale that dwarfs our college basketball silliness in the US.

The Y! India team reused some of the backend code but redid the frontend almost entirely. To adapt the game to cricket, among other chores, we had to modify our simulation code to estimate the starting probabilities that any team would win against any other team, even in the middle of a game. (How likely is it for India to come back at home from down 100 runs with 10 overs left and 5 wickets lost? About 25%, we think.) These starting probabilities are then refined further by the game-playing crowds.

It’s great to see an experiment from Labs grow into a full-fledged product run by a real product team in Yahoo!, a prime example of technology transfer at its best. In the meantime, we (Labs) are still gunning for a relaunch of Predictalot itself for March Madness 2011, the second year in a row. Stay tuned.

2011/02/24 Update: An eye-catching India-wide ad campaign for predictopus is live, including homepage, finance, movies, OMG, answers, mail, everywhere! Oh, and one of the prizes is a Hyundai.

In their current call for proposals, they explicitly ask for “I2b. Tools for facilitating collective intelligence”.

These are grants of up to US$150,000 with opportunity for more later I believe. The deadline is December 3, 2010! Good luck and (not so) happy Thanksgiving to anyone working on one of these proposals. I’m glad to help if I can.

The deadline for another US government program has passed, but should yield interesting results and may lead to future opportunities. In August, the Intelligence Advanced Research Projects Activity (IARPA, the intelligence community’s DARPA), which “invests in high-risk/high-payoff research programs” in military intelligence, solicited proposals for Aggregative Contingent Estimation, or what might be called wisdom-of-crowds methods for prediction:

The ACE Program seeks technical innovations in the following areas:

• Efficient elicitation of probabilistic judgments, including conditional probabilities for contingent events.
• Mathematical aggregation of judgments by many individuals, based on factors that may include past performance, expertise, cognitive style, metaknowledge, and other attributes predictive of accuracy.
• Effective representation of aggregated probabilistic forecasts and their distributions.

The full announcement is clear, detailed, and well thought out. I was impressed with the solicitors’ grasp of research in the field, an impression no doubt bolstered by the fact that some of my own papers are cited . Huge hat tip to Dan Goldstein for collating these excerpts:

The accuracy of two such methods, unweighted linear opinion pools and conventional prediction markets, has proven difficult to beat across a variety of domains.² However, recent research suggests that it is possible to outperform these methods by using data about forecasters to weight their judgments. Some methods that have shown promise include weighting forecasters’ judgments by their level of risk aversion, cognitive style, variance in judgment, past performance, and predictions of other forecasters’ knowledge.³ Other data about forecasters may be predictive of aggregate accuracy, such as their education, experience, and cognitive diversity. To date, however, no research has optimized aggregation methods using detailed data about large numbers of forecasters and their judgments. In addition, little research has tested methods for generating conditional forecasts.

² See, e.g., Tetlock PE, Expert Political Judgment (Princeton, NJ: Princeton University Press, 2005), 164-88; Armstrong JS, “Combining Forecasts,” in JS Armstrong, ed., Principles of Forecasting (Norwell, MA: Kluwer, 2001), 417-39; Arrow KJ, et al., “The Promise of Prediction Markets,” Science 2008; 320: 877-8; Chen Y, et al., “Information Markets Vs. Opinion Pools: An Empirical Comparison,” Proceedings of the 6th ACM Conference on Electronic Commerce, Vancouver BC, Canada, 2005.

³ See, e.g., Dani V, et al., “An empirical comparison of algorithms for aggregating expert predictions,” Proc. 22nd Conference on Uncertainty in Artificial Intelligence, UAI, 2006; Cooke RM, ElSaadany S, Huang X, “On the performance of social network and likelihood-based expert weighting schemes,” Reliability Engineering and System Safety 2008; 93:745-756; Ranjan R, Gneiting T, “Combining probability forecasts,” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2010; 72(1): 71-91.

[Examples:]

• Will the incumbent party win the next presidential election in Country X?
• Will the incumbent party win the next presidential election in Country X?
• When will Country X hold its next parliamentary elections?
• How many cell phones will be in use globally by 12/31/11?
• By how much will the GDP of Country X increase from 1/1/11 to 12/31/11?
• Will Country X default on its sovereign debt in 2011?
• If Country X defaults on its sovereign debt in 2011, what will be the growth rate in the Eurozone in 2012?

Elicitation – Advances Sought
The ACE Program seeks methods to elicit judgments from individual forecasters on:

• Whether an event will or will not occur
• When an event will occur
• The magnitude of an event
• All of the above, conditioned on another set of events or actions
• The confidence or likelihood a forecaster assigns to his or her judgment
• The forecaster’s rationale for his or her judgment, as well as links to background information or evidence, expressed in no more than a couple of lines of text
• The forecaster’s updated judgments and rationale

The elicitation methods should allow prioritization of elicitations, continuous updating of forecaster judgments and rationales, and asynchronous elicitation of judgments from more than 1,000 geographically-dispersed forecasters. While aggregation methods, detailed below, should be capable of generating probabilities, the judgments elicited from forecasters can but need not include probabilities.

Challenges include:

• Some forecasters will be unaccustomed to providing probabilistic judgments
• There has been virtually no research on methods to elicit conditional forecasts
• Elicitation should require a minimum of time and effort from forecasters; elicitation should require no more than a few minutes per elicitation per forecaster
• Training time for forecasters will be limited, and all training must be delivered within the software
• Rewards for participation, accuracy, and reasoning must be non-monetary and of negligible face value (e.g., certificates, medals, pins)

Book of Odds is, in their own words, “the missing dictionary, one filled not with words, but with numbers – the odds of everyday life.”

I use their words because, frankly I can’t say it better. The creators are serious wordsmiths. Their name itself is no exception. “Book of Odds” strikes the perfect chord: memorable and descriptive with a balance of authority and levity. On the site you can find plenty of amusing odds about sex, sports, and death, but also odds about health and life that make you think, as you compare the relative odds of various outcomes. Serious yet fun, in the grand tradition of the web.

I love their mission statement. They seek both to change the world — by establishing a reliable, trustworthy, and enduring new reference source — and to improve the world — by educating the public about probability, uncertainty, and decision making.

By “odds”, they do not mean predictions.

Book of Odds is not in the business of predicting the future. We are far too humble for that…

Odds Statements are based on recorded past occurrences among a large group of people. They do not pretend to describe the specific risk to a particular individual, and as such cannot be used to make personal predictions.

In other words, they report how often some property occurs among a group of people, for example the fraction all deaths caused by vending machines, not how likely you, or anyone in particular, are to die at the hands of a vending machine. Presumably if you don’t grow enraged at uncooperative vending machines or shake them wildly, you’re safer than the 1 in 112,000,000 stated odds. A less ambiguous (but clunky) name for the site would be “Book of Frequencies”.

Sometimes the site’s original articles are careful about this distinction between frequencies and predictions but other times less so. For example, this article says that your odds of becoming the next American Idol are 1 in 103,000. But of course the raw frequency (1/number-of-contestants) isn’t the right measure: your true odds depend on whether you can sing.

Their statement of What Book of Odds isn’t is refreshing:

Book of Odds is not a search-engine, decision-engine, knowledge-engine, or any other kind of engine…so please don’t compare us to Google™. We did consider the term “probability engine” for about 25 seconds, before coming to our senses…

Book of Odds is never finished. Every day new questions are asked that we cannot yet answer…

A major question is whether consumers want frequencies, or if they want predictions. If I had to guess, I’d (predictably) say predictions — witness Nate Silver and Paul the Octopus. (I’ve mused about using *.oddhead.com to aggregate predictions from around the web.)

The site seems in need of some SEO. The odds landing pages, like this one, don’t seem to be comprehensively indexed in Bing or Google. I believe this is because there is no natural way for users (and thus spiders) to browse (crawl) them. (Is this is a conscious choice to protect their data? I don’t think so: the landing pages have great SEO-friendly URLs and titles.) The problem is exacerbated because Book of Odds own custom search is respectable but, inevitably, weaker than what we’ve become accustomed to from the major search engines.

Book of Odds launched in 2009 with a group of talented and well pedigreed founders and a surprisingly large staff. They’ve made impressive strides since, adding polls, a Yahoo! Application, an iGoogle gadget, regular original content, and a cool visual browser that, like all visual browsers, is fun but not terribly useful. They’ve won a number of awards already, including “most likely company to be a household name in five years”. That’s a low-frequency event, though Book of Odds may beat the odds. Or have some serious fun trying.

At least one person.

Paul Graham wins the award for the most prescient parenthetical statement inside a footnote ever.

In footnote 14 of Chapter 5 (p. 228) of Graham’s classic Hackers and Painters, published in 2004, Graham asks “If the the Mac was so great why did it lose?”. His explanation ends with this caveat, in parentheses:

And it hasn’t lost yet. If Apple were to grow the iPod into a cell phone with a web browser, Microsoft would be in big trouble.

Wow. Count me impressed.

To find this quote, search inside the book for “ipod cell”.

To get into a 2004 midset, look here and here.

More people are playing Predictalot today than Mafia Wars or Zynga Poker… On Yahoo!, that is.

In fact, Predictalot is the #1 game app on Yahoo! Apps by daily count. By monthly count, we are 5th and rising.

A prediction is being made about every three minutes.

Come join the fun.

Yahoo! Lab’s own Predictalot market is now live and waiting for you to place almost any prediction your heart desires about the World Cup in South Africa.

Here are some terribly useful things you can learn this time around. All numbers are subject to change, and that’s kind of the point:

• There’s a 37% chance Brazil and Spain will both make it to the final game; there’s only a 15% chance that neither of them will make it
• There’s is a 1 in 25 chance Portugal will win the cup; 1 in 50 for Argentina
• 42.92% chance that a country that has never won before will win
• 19.07% chance that Australia will advance further than England
• 65.71% chance that Denmark, Italy, Mexico and United States all will not advance to Semifinals
• Follow Predictalot on twitter for more

If you think these odds are wrong, place your virtual wager and earn some intangible bragging rights. You can sell your prediction any time for points, even in the middle of a match, just like the stock market.

There are millions of predictions available, yet I really believe ours is the simplest prediction market interface to date. (Do you disagree, Leslie?) We have an excellent conversion rate, or percent of people who visit the site who go on to place at least one prediction — for March Madness, that rate was about 1 in 5. One of our main goals was to hide the underlying complexity and make the app fast, easy, and fun to use. I personally am thrilled with the result, but please go judge for yourself and tell us what you think.

In the first version of Predictalot, people went well beyond picking the obvious like who will win. For example, they created almost 4,000 “three-dimensional” predictions that compared one team against two others, like “Butler will advance further than Kentucky and Purdue”.

If you’re not sure what to predict, you can now check out the streaming updates of what other people are predicting in your social circle and around the world:

Also new this time, you can join a group and challenge your friends. You can track how you stack up in each of your groups and across the globe. We now provide live match updates right within the app for your convenience.

If you have the Yahoo! Toolbar (if not, try the World Cup toolbar), you can play Predictalot directly from the toolbar without leaving the webpage you’re on, even if it’s Google.

Bringing Predictalot to life has been a truly interdisciplinary effort. On our team we have computer scientists and economists to work out the market math, and engineers to turn those equations into something real that is fast and easy to use. Predictalot is built on the Yahoo! Application Platform, an invaluable service (open to any developer) that makes it easy to make engaging and social apps for a huge audience with built-in distribution. And we owe a great deal to promotion from well-established Yahoo! properties like Fantasy Sports and Games.

We’re excited about this second iteration of Predictalot and hope you join us as the matches continue in South Africa. We invite everyone to join, though please do keep in mind that the game is in beta, or experimental, mode. (If you prefer a more polished experience, check out the official Yahoo! Fantasy Sports World Soccer game.) We hope it’s both fun to play and helps us learn something scientifically interesting.

Read more here, here, and here.

Or watch a screencast of how to play: