Oddhead Blog

Sunday I returned from a trip to Bangalore, India, where I gave a talk on “The Automated Economy” about how computers can and should take over the mechanical aspects of economic activity, optimizing and learning from data in the way people cannot, with detailed case studies in online advertising and prediction markets. You can read the abstract, watch archive video of the talk, view my talk slides, browse the official pictures of the event, or see my personal pictures of the trip.

Some say everything’s bigger in Texas (most vociferously Texans). They haven’t been to India. My talk is part of Yahoo!’s Big Thinkers India series — four talks a year from (so far) Yahoo! Research speakers. If the Thinking isn’t Big, the crowds certainly are — the events can draw close to 1000 attendees from, apparently, all over India. Duncan Watts says its the largest crowd he’s spoken too; me too. This time they disallowed Yahoo! employees to attend the main event and the hotel ballroom still filled to capacity.

Here is a linked-up version of my journal entry for the trip, a kind of windy and winded thank you letter to Bangalore. If you’re not interested in personal details, you might skip to Thoughts on Bangalore.

Getting there

The Philadelphia airport international terminal is dead empty. I breeze through security — the only one in line. I’m inside security two hours early thinking that either the recession is still in full force or traveling internationally on a Monday night out of Philadelphia is the best ever. Maybe not. Get on plane. Wait two hours on tarmac. Apparently a two hour layover isn’t enough leeway on international flights. Miss my connecting flight in Frankfurt by a few minutes. Team up with a fellow passenger in the same boat. We are rebooked via Dubai. Fly directly over Bagdad. Dubai is an impressive airport. Endless terminals lined with upscale shopping. Packed with Asians, Europeans at midnight and beyond. From there, Emerites Air to Bangalore. Only 9 hours behind schedule. Sneezing fits begin after 28 hours of airplane air.

Day 0: Yahoo! internal practice talk

Driver right there outside baggage claim, nice guy. Takes me to hotel. Over an hour. Traffic. Time for shower, NeilMed nasal rinses (bottled water), Sudafed, but not sleep. Call home. Yahoo! Messenger with Voice doesn’t roll off the tongue like ‘Skype’, but it rocks. Super clear and dirt cheap. Lauren and the girls are so sweet. Miss them. To Yahoo! office. Meet Anita, Mani. Time for Yahoo! internal version of Big Thinkers talk. Nose is still running. Drips and wipes during my talk. Talk goes well but I run out of time for prediction market section and this seems what people are most interested in. I’m glad I had the practice run to work out the kinks and rebalanced the talk. Back to hotel. Call home again for a recharging dose of home. I missed Ashley’s graduation from pre-school: she did great: they sang six songs and she knew them all. She was dressed up in a yellow cap and gown. I’m upset I had to miss such an adorable milestone but am proud of my little girl (and dismayed she is rapidly becoming not so little!). More NeilMed. Room service. (Called “private dining” here — sounds illicit.) Sleep! For a few hours at least. Wake up in the middle of the night since it’s NY daytime. Finally get back to sleep again.

Day 1: Meetings

Hard to wake up at 9am = midnight. Shower. Feel 1000% better. Driver takes me to the Yahoo! office. It’s in a complex with Microsoft, Google, Target, Dell, and many other US brands. Once you’re inside it’s like every other Yahoo! office except the food — built essentially to corporate spec. Meet with Anita, Raghu, and Rajeev: go over PR angles and they brief me on the media interviews. These guys and gal are on top of things. Meet with Mani and her team: great group. Skip intern pizza talks because I can’t eat cheese, going for the cafeteria instead. Mistake. Order a veggie grill thinking that since it’s grilled, it’s cooked enough. I only take a few bites of this before thinking it’s too risky. I eat some bread and Indian mixtures. Not sure what the culprit is but something doesn’t sit well in my stomach. Give prediction markets portion of my talk to a few interested people in labs. Very sharp group. Meet with Dinesh and Sachin, their intern, and one other. Interesting work. Meet with Chid and Preeti on Webscope. Back to hotel. Call Lauren. Good to hear her voice. Ashley wants to say hi. She’s so adorable. She finds it hilarious that I am about to have dinner while she is eating breakfast. I can hear her laughing uncontrollably at the thought. Sarah says hi too and even ends our conversation without prompting with a “bye, love you”. I go down to the restaurant for dinner. Have a chicken Indian dish with paratha (is it lachha paratha?) bread. Spicy (sweat inducing) yet so delicious. The bread is fantastic — round white with flaky layers. Back to room. TV. CNN. CNBC. ESPN. Hard to sleep. There is an incredible thunderstorm with torrents of rain. I open my balcony door briefly to catch its power. I find out later that monsoon season is just beginning. I also find out that it rained so hard and so long that the roads flooded to the point of becoming impassible. In fact, Anita, the Bangalore PR lead, had a near-disastrous experience in the rapidly flooding streets on her way home and had to turn back and check into a hotel before going home briefly in the morning and then back to Yahoo! for our am meeting. Finally get to sleep.

Day 2: My talk!

Hard to wake up at 8:30am too. Talk’s today! Nerves begin. Media interviews are first! Even worse. Turns out they went fine. Two nice/sharp reporters, especially the second one who really knows her stuff and spoke to us (Rajeev and I) for 1.5 hours. She’s especially interested in the prediction market stuff since that is something new. She may write two articles (for Business World India). Lunch, then a bit of time to rest and freshen up. Stomach is not doing well. Pepto to the rescue. Back down to lobby. They take my picture in the courtyard. Then into the ballroom. Miked. Soundchecked. They accept a final last minute change to my slides: hooray! Room starts filling. 100 people. 200. 300. Now 500. It’s time to start! Rajeev gives a very nice intro. I walk up the stairs onto the stage. I’m miked, in lights, speaking in front of 500 people expecting a Big Thinker. Here I go! “Four score and seven years…” Ha ha. Actually: “Thanks Rajeev, and thanks everyone for your time and attention. I am happy and honored to be here. I’m going to talk about trends in automation in the economy…”

65 minutes later “Thank you very much.” Applause. I think it went well: one of my better talks. I covered everything, including the prediction market stuff. It turns out, like at Yahoo!, and like the journalists, the audience is more interested in prediction markets than advertising. Lots of questions. Some I follow, some I can’t parse the words, others I hear the words but just don’t understand. I do my best. Several people mention they follow my blog: gratifying. After the official Q&A session ends, there is a line up of folks with questions or comments and business cards. It’s the closest I’ll ever be to a rock star. A handful of people wait patiently around me while I try to get to everyone. Eventually the PR folks rescue me and take me to a “high tea” event with Yahoo! Bangalore execs and some recruiting targets. Relief and euphoria kick in. It’s over. I talk with a number of people. I make my exit. Private dining. Call home. Lauren has explained to Ashley that I am on the other side of the world, so when she has the sun, I have the moon. So I can hear Ashley asking in the background, “does Daddy have the moon?” I do. She can’t stop laughing. A repeat of game 6 of the Stanley Cup is on Ten Sports India. I watch it, getting psyched for Game 7. I check online for Ten Sports schedule. Game 7 will be on at 5:30am! I can’t miss that! Set my alarm. Try to sleep. Can’t sleep. Try to sleep. Can’t sleep. Try with TV on. Can’t sleep. Try with TV off. Can’t sleep. Finally fall asleep… Alarm!

Day 3a: Penguins win the Stanley Cup!

Really hard to wake up at 5:30am. Actually maybe not quite as hard since it’s 8pm in my head. Game on! Nerves are racked up. Can’t sit down: bad luck. Pacing. No score first period. Tons of commercials, all for Ten Sports programming: wrestling, cricket, tennis. Every commercial repeats three times. Is period two coming? Yes, it’s back on! Pens score first! Fist pumping and muted cheering. Can they really do this? No sitting rule in full effect. Pacing. Pens score again! Talbot second goal. Wow, is this real? Can it be? Don’t think about it yet. Don’t celebrate to soon. Plenty of time left. Period two end at 2-0. Unbelievable. All the same commercials come back, three times each. Period three begins. Stand up. Pace. Clock ticks. Pens are playing too defensive: not taking shots, just throwing the puck out of their zone. This isn’t good. Detroit is getting tons of chances. Fleury is awesome. Five minutes left. I let myself think about winning the cup. Mistake! Detroit scores! It’s 2-1! Nerves are ratcheted up beyond ratcheting. I think about it all slipping away. How awful that would feel. If Detroit ties it up, imagine the let down, the blown opportunity. Clock ticks. More chances. More saves. More defense. It’s working! Detroit pulls their goalie. Pressure. Final seconds. Faceoff in our zone. Detroit wins control. Shot. Rebound. Right to a Red Wing — Nick Lidstrom — in perfect position. He shoots. Fleury swings around. He saves it! It’s over! Pens win the Cup! Super fist pumping, jumping around, dancing, muted cheering. They did it! How amazing it feels after last year’s loss to the same team. After falling behind 2-0 and 3-2 in the series. They came back! A delicious payback with the same but opposite script as last year: a two goal lead cut in half in the waning minutes, a flurry of attempts at the end including a few-inch miss of the tying goal in the last seconds. These guys are young and have the potential to rule hockey for several years if they’re lucky. Mario Lemieux is on the ice. How sweet. Twice as player, now as owner, the one who saved hockey in Pittsburgh. What a year for Pittsburgh sports! Two nail biter games, two comebacks, two championships. City of Champions again. Too bad the Pirates have no shot to join them in a trifecta. Back to sleep.

Day 3b: Sightseeing

Phone rings at 11am — my driver is here. Off to do some whirlwind sightseeing. Everyone here who finds out I have a day off recommends I leave Bangalore — Bangalore is just not that nice, nothing really to see, they say. They all recommend Mysore, 3.5 hours away, but that is too far for my comfort level given that my flight is late tonight and it’s supposed to thunderstorm. We start with some souvenir shopping on “MG Road”. My driver takes me to a store and waits in the car outside. I walk in an instantly there are people greeting me and showing me things. One aggressive man takes over and remains my “tour guide” through the whole store. The fact that I reward his aggressiveness by following along and eventually buying stuff will only bolster him to do more of the same in the future. Annoying but clearly it works. I do negotiate him down, but I leave still feeling I didn’t bargain hard enough and with a bit of distaste in my mouth that I fueled and validated the pushy tactics. Next we drive past parliament and the courthouse. Impressive, large, old buildings. But I can just gaze and take photos from the car — can’t go inside. Next we drive past Cubbon Park — tree lined paths and flower gardens in center city. Next is ISKCON temple. But it’s closed. So one more round of shopping at a place called Cottage Industries. I’m wary given the last experience, but go anyway. This one is better. Again one person escorts me around but I feel less pressure. Plus I’m more prepared to say no and negotiate harder. I leave with what seems like a fair amount of value in goods. I recommend Cottage Industries to future visitors: more professional, more familiar (items have price tags), lower pressure, greater variety, and higher quality than at least the first shop I visited. Now we’ve killed enough time and the ISKCON temple is open. It’s a giant Hare Krishna temple. The parking lot is full. I tell the driver it’s ok — we don’t need to go. He says “you go, you go”. “Ok” I say. We drive around again to the same full parking lot. The attendant waves at us to leave, blowing a whistle. My driver is talking to him. They are talking quite heatedly. The attendant in his official looking uniform is waving us on vigorously. Although I can’t understand the words, he is clearly telling us the lot is full and we must leave immediately — we are holding up traffic. My driver is getting more insistent. They are yelling back and forth. I have no idea what he says but it works. The guard let’s us in. Meanwhile another car sees our success and tries to argue his way in too but to no avail. I ask my driver what he said: he simply replies “don’t talk”. Indeed once we’re in, there is an empty spot. We put all my bags in my suitcase in the trunk and cover my backpack. We take off our shoes and my driver leads me to the temple. He knows the back entrance and is guiding me to cut in front of lines everywhere. We walk past the main attraction: the altar with some people on the floor worshiping. Then the line weaves past a gift shop of course: I buy a crazy looking book (Easy Journey to Other Planets). We need to kill some time. We go to the gardens again to walk around. We walk into the public library. Most books are in English. Most seem old and worn. The attendant says the library is 110 years old. We start walking through the garden but I am paranoid about mosquitoes/malaria so we turn around early to return to the car. We go to UB City where I meet Rajeev. It’s a thoroughly modern office tower half owned by Kingfisher of Kingfisher Airlines. The building is full of high-end shopping like almost any upscale western mall with all the same brands. Here is the Apple Store. Here is Louis Vuitton. We have dinner at an Italian restaurant that could be anywhere in the western world, owned by an Italian expat. The only seating is outside and I remain worried about mosquitoes but don’t see any. The food is good and the conversation is good. This place is the closest I’ve seen of the future of Bangalore. In the center of town, a gorgeous building filled with gleaming shops and tantalizing restaurants and bars, with apartments and condos within walking distance, and a palm-tree-lined street leading to the central town circle and the park. As Rajeev says, though, whereas New York has hundreds of similar scenes, Bangalore has one. For now.

Thoughts on Bangalore

Bangalore is a city of jarring contradictions, a hard-to-fathom mix of modernity and poverty. Signs with professional logos and familiar brands are set askew on dilapidated shacks and garages lining the road. While many live on dollars and day and others beg, the majority are smartly dressed (men invariably in button-down shirts), have mobile phones, and are intelligent and friendly. There are gleaming office towers indistinguishable from their western counterparts, yet a strong rain can flood the roads to the point of become impassible for hours and day-long blackouts aren’t uncommon. Many billboards are in English, sporting familiar brands and messages. Others, like sexy stars promoting a Bollywood film, are entirely familiar, English or not. Others are impenetrable. Still another advertises a phone number to learn why Obama quoted the Koran.

BMWs and Toyotas join bikes, motorcycles, pedestrians, aging trucks and buses, and colorful open-air motorized rickshaws in a sea of disorganized line-ignoring sign-ignoring traffic. People drive here the way New Yorkers walk sidewalks: weaving past one another in a noisy self-organized tangle that somehow — mostly — works. You can eat outside in a restaurant bar next to upscale shops, a fountain, and smiling yuppies, yet worry that a malaria-infected mosquito lurks nearby or that a washed vegetable will turn a western-coddled stomach deathly ill. When two people ride a motorcycle, as is common, only the driver wears a helmet — the passenger clinging on behind does not: new and old rules on display atop a single vehicle. And the traffic. Oh, the traffic. Roads are clogged nearly every hour of every day. My Saturday of sightseeing was as bad or worse than weekday rush hour. The extent of congestion itself illustrates Bangalore’s two faces: so many people with youth (India is one of the youngest countries in the world), energy, purpose, and the means and intelligence to accomplish it overtaxing a primitive infrastructure. Buildings are going up according to western specs, but under old-time rules where corruption reins and bribery is an accepted fact of life by even the western-educated aspirational class (about 20% and growing, according to Rajeev).

Thoughts on Yahoo! Labs Bangalore

The folks I met are impressive. Rajeev has done a great job hiring talented, driven folks. Mani’s group of research engineers is fantastic. One is headed to Berkeley for grad school and asks great questions about CentMail. Another proposes an attack on Pictcha. Another (Rahul Agrawal) has read up deeply on prediction markets, including Hanson’s LMSR.

Thoughts on the Yahoo! Big Thinkers India program

The whole event was organized to precision. Anita, the PR lead, was incredible. I especially appreciated the extra “above and beyond” touches like having someone pick up Yahoo! India schwag for my family and send it to my hotel after I forgot: so nice. Raghu, who arranged the media interviews, is supremely organized and on top of his game. The fact that the event draws such a large crowd shows that there is great thirst for events like this in Bangalore. I’m not sure whose idea it is, but it’s a brilliant one: great marketing and great for recruiting.

Thank you Bangalore

In sum, thanks to the people of Bangalore for a fascinating and rewarding trip. Thanks to Rahul at the travel desk whose instant replies about the driver arrangements calmed my nerves on the stressful day of my departure. Thanks to the Yahoo! folks who arranged and organized my talk, and the Yahoo! Labs members for seeding an exceptional science organization. Thanks to my driver who got me everywhere — including into full parking lots, back entrances, and fronts of lines — with efficiency, safety, and a smile (when I tipped him, I tried to think wwsd: what would Sharad do). Thanks to those who attending my talk and whom I met afterward: it’s gratifying and invigorating to see your level of interest and enthusiasm (and your numbers). And thanks Bangalore chefs for keeping any stomach upset relatively mild and brief.

At the airport on the way out, the flight is overbooked and they are offering close to US$1000 plus hotel to leave tomorrow. Not a chance. It’s been fun and an adventure but my nerves are on high and I miss my family: it’s time to make the 20+ hour journey home.

Surely no legislature would craft an entire bill just to outlaw one person’s as-yet unprofitable small business?

So when Nick Jenkins, founder of Betcha.com, calls a proposed Washington State law the “Kill Betcha.com Act”, certainly he must be exaggerating, paranoid or both, right?

Actually not. I read the bill, and indeed its sole purpose seems to be to redefine gambling so as to make Jenkins’s company illegal.

First some background. About two years ago, Jenkins, a crazybrilliant lawyer turned entrepreneur, started Betcha.com in Washington State, where gambling is illegal. Betcha.com was a peer-to-peer betting site with a huge caveat: losers didn’t have to pay if they didn’t want to. Because they weren’t necessarily risking money, they weren’t gambling. The Washington State authorities were not amused; they raided the company and jailed Jenkins & co, even extraditing them to Louisiana over seventy cents.

Then, in February 2009, the tide began to turn. Reversing a lower-court decision, the Washington State Court of Appeals vindicated Jenkins and his business model: Honor-based betting was not gambling. The former lawyer had done his homework well and, sure enough, was right all along.

So, some Washington State politicians decided to put the honor back into gambling. They proposed to redefine a bet as risking money on the understanding that you “will or may receive something of value” if you’re right, adding in the crucial two new words “or may”. So far, two attempts to pass the revised wording have stalled, keeping hope alive for an eventual resurrection of Betcha.com.

Apparently Washington State has a history of extreme positions on gambling, including outlawing writing about or linking to gambling websites, despite the standard hypocrisies of supporting state lotteries, horse racing, and Indian casinos.

Jenkins’s new mission is to keep the governor who turned him over to Louisiana authorities off the Supreme Court should Obama nominate her. Christine Gregoire couldn’t have gained a more tenacious and law-savvy enemy.

Thank you Nick Jenkins for continuing to fight when most would have given up long ago. Your hard work and sacrifice brings desperately needed clarity to gambling laws and paves the way for US gamers to someday get the products they want.

I wrote an essay on “new media” for an entrepreneur friend in February 2004. (My friend launched a new air sports league and .tv channel, hence the emphasis on sports near the end.) I decided to take my own advice and relinquish control. Here it is, with minor re-touches marked and links added. Most of the points remain applicable in 2009. If anything, I’m a little disappointed that, five years later, we haven’t made more progress toward “everything over IP, everywhere”. Sure, Hulu is nice but I still pay obscene amounts to send text messages and watch The Terminator over proprietary pipes.

‘Digital’ means everything and nothing at once. And that’s the point. Music is digital. Movies are digital. Books, news, commentary, communication, ideas, and sexuality are all digital. Even money is digital. Characterizing something as digital conveys no information precisely because most anything can and will be digital. From television to telecom, from Hollywood to Madison Avenue, the transition to digital will take down giants and crown new kings.

Why does digital matter to media? There are three reasons: convergence, copying, and control.

Convergence. Because all content and communication are digital, the delivery mechanism no longer matters. You don’t need a TV to watch television programs. You don’t need a phone to talk to a friend. You don’t need a fax to get faxes or a CD player to hear CDs. All you need is a machine that understands digital and a communications system that carries digital. Today’s best devices for understanding and communicating digital are, respectively, the computer and the Internet. That’s all you need. Tomorrow’s TVs may look and feel and act much like today’s TVs, but rest assured they will be computers in disguise, and they will be connected to the Internet. There’s no inherent reason why Friends should be watched on Thursdays at 8pm on NBC interspersed with commercials. It can, should, and will be watched at the viewer’s leisure, uninterrupted. There is no reason that the biggest “television” phenomenon of 2008 won’t be seen on Yahoo!, for example. [In hindsight, this example was wildly optimistic -- and YouTube/2020 now seems more likely -- though in 2008 viewers flocked to Yahoo! for the Olympics, the election, and short-form video.] Notions of channels and schedules will be virtually meaningless. We already see this happening with DVRs like TiVo, and the blurring will continue with computer/TVs providing access to movies, music, your photo album, weather, news, and the Web. Cable, phone, and satellite companies are providing Internet access. Internet portals and Internet providers are delivering phone calls, movies, TV shows, [radio,] and email all over the same wires [and wavelengths].

There is now, and will continue to be, fierce opposition to convergence from established players. Cable companies objected vehemently to allowing local stations onto satellite TV. Broadcast networks fear TiVo. The Recording Industry Association of America (RIAA) is in a state of panic panicked, suing everyone in sight, including their own customers. Lobbying and lawmaking will slow convergence, but the changes are all but inevitable. While the RIAA and groups like it scramble to rearrange deck chairs on the Titanic, opportunists are busy building entirely new ships.

Copying and Control. Once a piece of media content—whether it is a song, a movie, or an article in a scientific journal—is converted into digital ones and zeros, it can be copied (perfectly) and distributed at almost zero cost. Given the decentralized nature of the Internet and the vagaries of international law, once a piece of content escapes there is almost no reining it in. Current media business models rely on tight controls. Control of scheduling. Control of delivery and distribution. Control of store shelves. Control of artists and content creators. Control of consumers’ attention. But digital content resists nearly all attempts at control. Software and hardware copy-protection schemes are hacked or circumvented. High-quality analog copies of digital content are simply impossible to stop. Artists can self-publish their work and distribute it worldwide. Consumers can suddenly find content that’s not broadcast at primetime or placed at eye level in the store.

Note that digital does not mean the end of marketing, influence, and celebrity. Capturing the public’s interest and attention are still necessary. A self-published song does not magically attract listeners. Talent, personality, advertising, branding, and social forces will still play large roles in driving media success in the digital era. But convergence means that any number of players can provide the marketing and distribution needed, breaking current oligopolies, and almost certainly benefiting artists and consumers alike. Successful business models for the next generation of media companies must address the loss of control on all three fronts: content, artists, and consumers. Content will be copied. Artists will self-publish and shop for marketing services. Consumers will view what they want when they want to.

The New Business of New Media

Media is certainly not dead. Certain aspects will probably never change. People yearn for good stories, for entertainment, for escapism, for information. People flock to charisma and celebrity. People communicate insatiably. From a business perspective, there is undeniable value in having and holding the attention of a number of people.

Although the face of tomorrow’s media is impossible to predict, certain sectors are poised to benefit enormously from the emergence of digital, or are at least less susceptible to its problems.

Here are some winning strategies:

Embrace convergence. Convergence offers almost limitless flexibility in delivering and customizing content. Sports fans can watch an event from any camera, watch real-time animated renderings allowing absolute viewer control, interact with video games with parallel story lines, or chat with other fans. News broadcasts can allow viewers to examine any topic to any depth. Toys can react to signals embedded in Saturday morning cartoons. Consumers can create customized “channels” delivering content tailored to their needs and whims. Companies that capture the voicexyz-over-Internet market will be big winners in the new-media world.

Embrace copying. There is no doubt that a large part of the business value of media lies in its ability to influence (usually via advertising), which in turn benefits most from widespread adoption. For a business built on influence, free and unfettered copying should be encouraged rather than litigated. Not everything has to be free. In some cases, people will pay to get content faster. Live events are the most obvious situation where copies are less valuable than originals. People may pay for live feeds of sporting events, for example. In many cases, people will pay for higher-quality content, for example higher-resolution movies or better-sounding music. For example, with a good digital rights management system, pristine digital copies might be sold for a small premium, even while slightly tarnished analog copies (which are essentially unstoppable) proliferate. People may pay a premium for convenience, anonymity, quality assurance, or to obtain versions stripped of commercial messages. Clearly delineated commercials are a problem in a world where time shifting and copying are prevalent: people will simply skip commercials. So commercial messages must be embedded directly in the content, using product placement or endorsements.

Real-time gambling offers a natural source of revenue for sporting events and other live events. Real-time gambling is spreading quickly throughout the UK and Europe, where it is well regulated and taxed. Real-time gambling offers a situation where live feeds are essential, and copies less damaging. In fact, wide dissemination of copies could be valuable as a marketing device to drive interest in the live events and concurrent gambling services.

The lingo of prediction markets varies widely.

The same “thing” might be called an information market, idea future, virtual stock market, financial market, securities market, event market, binary option, betting exchange, bookmaker, market in uncertainty, or gambling/wagering. Only recently has the name prediction market emerged with some sort of consensus.

To place a prediction in the market, you might do any of the following:

[bid/buy/bet on/back] the “yes” [security/contract/coupon/future/outcome] at [price/probability/fractional odds/decimal odds/moneyline] X

Predicting something won’t happen gets even uglier. You might:

[ask/short sell yes/buy no/buy bundle & sell yes/bet against/lay] at [price/probability/fractional odds/decimal odds/moneyline] X

For example, InklingMarkets uses the “short sell yes” variation:

So what is the clearest language for prediction markets?

A good guiding principle in this regard is KISS: Keep It Simple Stupid. Or, in more grandiose terms, Occam’s razor. All else being equal, one should choose the simplest and most straightforward option.

By this measure, it seems that betting lingo wins hands down. It’s vastly simpler to say “I bet $10 that Obama will lose” than to say “I short sell three shares of Obama at price 67″. The former is more direct and intuitive. Almost everyone understands what it means to place a bet, including subtleties like risk, uncertainty, and competition. On the other hand, even avid stock traders get tripped up by the concept of selling short.

Every prediction can be stated as: “I bet that outcome O will/won’t happen; I’ll risk $X to win $Y”. Betting for things and against things is symmetric. There is no need to short sell, buy bundles first, etc.

Yet most prediction markets don’t KISS, going with financial terminology instead, reflected even in the name itself. Why? I believe it’s because of the legal and social stigma attached to gambling. It’s a shame that such considerations force vendors to make the technology harder to understand and more complicated to use.

The Kelly criteria is a money management strategy for gamblers and investors. The strategy says that, when faced with a positive-expectation bet, you should invest a fraction of your budget that is proportional to your expected profit. The more your expect to gain, the more you should risk, but you never risk your entire budget.

The Kelly strategy is optimal in several senses: (1) it minimizes your “doubling time”, or the time it takes to go from having X dollars to having 2X dollars; (2) it minimizes the time it takes to achieve any given level of wealth; (3) it maximizes your long-run wealth.

(It turns out that the Kelly strategy is equivalent to maximizing a logarithmic utility function.)

A key reason the Kelly strategy is optimal is that it is very careful to never take you completely bankrupt: you spend only a fraction of your money, always reserving a bit for tomorrow, however small. This is sound advice when dealing with real money. (Aside: this all assumes you have a strict budget cap, which is not entirely realistic: you can almost always borrow at least some amount, even in today’s economy.)

But what about maximizing your virtual “wealth” inside a play-money game like NewsFutures, InklingMarkets, HubDub, or MediaPredict? The problem is not quite the same, precisely because you cannot really go bankrupt. Almost every game offers an option to “recharge” your account if you go bust. Even if the option is not explicit, you can always just abandon your account and start a new one with a fresh initial bankroll they typically give to new players.

So what is the Kelly criteria for play money? What is the optimal strategy that minimizes your doubling time when you’re always allowed to recharge back to a fixed starting value any time you go bankrupt? The answer is not obvious to me, so I’m crowdsourcing the problem: can readers derive the right rule?

My only conjecture is that it might become optimal to go “all in” on every single bet. But I’m not sure. [Update: I've convinced myself this is not optimal. Imagine two sequential bets, the first with minuscule expected profit and the second with huge expected profit: surely you should not go "all in" on the first.]

Note that finding the optimal solution may not just help you win more bragging rights in online games. There is a fascinating sports betting site called CentSports that gives everyone ten real cents to start with. If you can turn that ten cents into twenty dollars, they’ll cut you a check. Moreover, if you ever go to zero, they’ll restore you right back to ten cents. In other words, the system works just like play-money games except the potential for profit is real. So another way to phrase the challenge question is: what strategy in CentSports minimizes the time it takes you to go from ten cents to twenty dollars?

Casino floors from Macau to Mississippi look eerily similar. The slot machine seas. The table game islands. The high-limit oases. The restaurants, shows, buffets. The colorful currency. The slot machines. The excruciating check-in lines. Minimum bet forced scarcity. The bleeping beeping slot machines.

The games themselves are for the most part the same that people have played for centuries, with rare exceptions. People flock to the games they already know: blackjack, craps, baccarat. Is this a matter of making gamblers comfortable wherever they go, luring them into a wallet-emptying rhythm? Have casinos evolved to perfection, like sharks? It seems ironic that gamblers who clearly exhibit risky behavior only want to deal with games that are known and familiar. Is there room for innovation in casino gambling? Is this a fat satiated industry resting on its laurels ready for a spark of creativity to ignite a shakeup, or a smart, precisely tuned machine already operating at full throttle in optimized mode, thank you very much?

For example, innovation in slot machine design seems to involve replacing spinning wheels with LCD screens that display in gorgeous 3D detail… spinning wheels. The greatest advance in poker technology has been the hole-card camera, enabling more engaging television coverage.

Outside of the casino, companies like betfair and twinspires are shaking up their respective industries. Why do casinos seem to be standing still?

I’d love to see an experimental marketplace where people play and invent new gambling games, and where breakout winners move on to trials in the “big leagues”. Would it ever fly? Would gamblers bother to play, or are they by and large unimaginative creatures of habit?

P.S. Did I mention that the woblomo deadline is midnight Hawaii time?

Several people have asked me what exactly is a combinatorial prediction market? Before giving a formal definition, I’ll start with an example.

Combinatorial Madness

Think of March Madness, the anything-can-and-often-does tournament among the top 64 NCAA Men’s College Basketball teams. The “madness” of the games is rivaled only by the madness of fans competing to pick the winners. In Las Vegas, you can bet on many things, from individual games to the overall champion to more exotic “propositions” like which conference of teams will do best. Still, each gambling venue defines in advance exactly what you are allowed to bet on, offering an explicit list of usually no more than a few thousand choices.

So here is my informal definition: a combinatorial market is one where users can construct their own bets by mixing and matching options in myriad ways, sort of like ordering a Wendy’s hamburger. (Or highly customized insurance).

The Details

Now I’ll try for a more precise definition.

Just to set the vocabulary straight, outcomes are all possible things that might happen: for example all five candidates in an election, all 30 teams in an NBA Championship market, all 3,628,800 (or 10!) finish orderings in a ten-horse race, or all 9.2 quintillion March Madness tournament results. Among the outcomes, in the end one and only one of them will actually occur; traders try to predict which.

Bids express what outcome(s) traders think will happen. Bids also contain the risk-reward ratio the trader is willing to accept: the amount she expects to win if correct and the amount she is willing to lose if incorrect.

There are two reasons why we might call a market “combinatorial”: either the bids are combinatorial or the outcomes are combinatorial. The latter poses a much harder computational problem. I’ll start with the former.

1. Combinatorial bids. A combinatorial bid or bundle bid is a concise expression representing a collection or set of outcomes, for example “a Western Conference team will win the NBA Championship”, encompassing 15 possible outcomes, or “horse A will finish ahead of horse B” in a ten-horse race, encoding 1,814,400, or half, of the possible outcomes. Yoopick, our experimental sports prediction market on Facebook, features a type of combinatorial bidding called interval bidding. Traders select the range they think the final score difference will fall into, for example “Pittsburgh will win by between 2 and 11 points”. An interval bet is actually a collection of bets on every outcome between the left and right endpoints of the range.

   For comparison, a non-combinatorial bid is a bet on a single outcome, for example “candidate O will win the election”. The vast majority of fielded prediction markets handle only non-combinatorial bids.

   What are examples of combinatorial bids besides Yoopick? Abe Othman built an interval betting interface similar to Yoopick (he came up with it on his own, proving that great minds think alike) to predict when the new CMU computer science building will finish construction. Additional examples include Bossaerts et al.’s concept of combined value trading and the parimutuel call market mechanism [Baron & Lange, Lange & Economides, Peters et al.].

2. Combinatorial outcomes. The March Madness scenario is an example of combinatorial outcomes. The number of outcomes (e.g., 9.2 quintillion) may be so huge that we could never hope to track every outcome explicitly inside a computer. Instead, outcomes themselves are defined implicitly according to some counting process that involves enumerating every possible combination of base objects. For example, the outcome space could be all n! possible finish orderings of an n-horse race. Or all 2ⁿ combinations of n binary events. In both cases, the number of outcomes grows exponentially in the number of base objects n, quickly becoming unimaginably large as n grows.

   A market with combinatorial outcomes is almost nonsensical without allowing combinatorial bids as well, since individual outcomes are like microbes on a needle on a cruise ship of hay in a universe-sized sea. No one wants to bet on these minuscule possibilities one at a time. Instead, traders bet on high-level properties of outcomes, like “Duke will advance further than UNC”, that encode sets of outcomes. Here are some example forms of combinatorics and corresponding bidding languages that seem natural:

   • Boolean betting. Outcomes are combinations of binary events. Bids are phrased in Boolean logic. So if base objects are “Democrat will win in Alabama”, “Democrat will win in Alaska”, etc. for all fifty US states, and outcomes are all 2⁵⁰ possible ways the election might swing across all 50 states, then bids may be of the form “Democrat will win in Ohio and Florida, but not Virginia”, or “Democrat will win Nevada if they win California”, etc. For further reading, see Hanson’s paper on combinatorial market makers and our papers on the computational complexity of Boolean betting auctioneers and market makers.
   • Tournament betting. This is the March Madness example and a special case of Boolean betting. See our paper on tournament betting market makers.
   • Permutation betting. Outcomes are possible finish orderings in a horse race. Bids are properties of orderings, for example “Horse B will finish ahead of horse D”, or “Horse B will finish between 3rd and 7th place”. See our papers on permutation betting auctioneers and market makers.
   • Taxonomy betting. Base objects are (discretized) numbers arranged in a taxonomy, for example web site page views organized by topic, subtopic, etc. Outcomes are all possible combinations of the numbers. Bets can be placed on the range of any number in the taxonomy, for example page views of a sports web site, page views of the NBA subsection of the web site, etc. Coming soon: a paper on taxonomy betting led by Mingyu Guo at Duke.

   We summarize some of these in a short article on Combinatorial betting and a more detailed book chapter on Computational aspects of prediction markets.

Auctioneer versus market maker

So far, I’ve only talked about the form of bids from traders. Next I’ll discuss the actual mechanics of the marketplace, or how bids are processed. How does the market operator decide which bids to accept or reject? At what prices?

I’ll focus on two major possibilities: either the market operator acts as an auctioneer or he acts as an automated market maker.

An auctioneer only matches up willing traders with each other — the auctioneer never takes on any risk of his own. This is how most financial exchanges like the stock market operate, and how intrade and betfair operate. (A call market is a special case where the auctioneer collects many bids over a period of time, then processes them all together in a single batch.)

An automated market maker will quote a price for any bet whatsoever. Even lone traders can place their bet with the market maker as long as they accept the price, greatly enhancing liquidity. The liquidity comes at a cost though: an automated market maker can and often does lose money, though clever pricing algorithms can guarantee that losses won’t mount beyond a fixed amount set in advance. Hanson’s logarithmic market scoring rule market maker is far and away the most popular for prediction markets, and for good reason: it’s simple, has nice modularity properties, and behaves well in practice. We catalog a number of bounded-loss market makers in this paper. The dynamic parimutuel market used in the (now closed) Yahoo! Tech Buzz Game can be thought of as another type of automated market maker.

A market with combinatorial outcomes almost requires a market maker to function smoothly. When traders have such a mind-boggling array of choices, the chances that two or more of their bets will exactly counter each other seems remote. If trades are rarely filled, then traders won’t bother bidding at all, causing a no-chicken-no-egg spiral into failure.

One the other hand, a market maker allows anyone to get a price quote at any time on any bet, no matter how convoluted or specific, even if no other traders had thought about that particular possibility. Thus interacting with a combinatorial market maker can be highly satisfying: propose any obscure proposition, click “accept price”, and your bet is placed: no doubt and no waiting.

I’ll discuss one more technicality. An auctioneer must decide whether bids can be partially filled, giving traders both less risk and less reward than they requested, in the same ratio. This makes sense. If I’m willing to risk $100 to win $200, I’d almost surely risk $50 to win $100 instead. Allowing partial fills greatly simplifies life for the auctioneer too. If bids are divisible, or can be filled in part, the auctioneer can use efficient linear programming algorithms; if bids are indivisible, the auctioneer must use integer programming algorithms that may be intractable. For more on the divisible/indivisible distinction, see Bossaerts et al. and Fortnow et al. Allowing divisible bids seems the logical choice in most scenarios, since the market functions better and most traders won’t mind.

The benefits of combinatorial markets

Why do we need or want combinatorial markets? Simply put, they allow for the collection of more information, the life-blood of every prediction market. Combinatorial outcomes allow traders to assess the correlations among base objects, not just their independent likelihoods, for example the correlation between Democrats winning in Ohio and Pennsylvania. Understanding correlations is key in many applications, including risk assessment: one might argue that the recent financial meltdown is partly attributable to an underestimation of correlation among firms and securities and the chances of cascading failures.

Although financial and betting exchanges, bookmakers, and racetracks are modernizing, turning their operations over the computers and moving online, their core logic for processing bids hasn’t changed much since auctioneers were people. For simplicity, they treat all bets like apples and oranges, processing them independently, even when they are more like hamburgers and cheeseburgers. For example, bets on a horse “to win” and “to finish in the top two” are managed separately at the racetrack, as are options to buy a stock at “strike price 30″ and “strike price 20″ on the CBOE. In both cases it’s a logical truism that the first is worth less than the second, yet the market pleads ignorance, leaving it to traders to enforce consistent pricing.

In a combinatorial market, a bet on “Duke will win the tournament” automatically increases the odds on “Duke will win in the first round”, as it logically should. Mindless mechanical tasks like this are handled automatically, by algorithms that are far better at it anyway, freeing up traders for the primary task a prediction market asks them to do: provide information. Traders are free to express their information in whether form they find most natural, and it all flows into the same pool of liquidity.

I discuss the benefits of combinatorial bids further in this post, including one benefit I don’t mention here: smarter accounting.

The disadvantages of combinatorial markets

I would argue that there is virtually no disadvantage to allowing combinatorial bids. They are more flexible and natural for traders, and they eliminate redundancy and thus concentrate liquidity (again I refer the reader to this previous post). Allowing indivisible combinatorial bids can cause computational problems, but as I argue above, divisible bids make more sense anyway.

On the other hand, there can be disadvantages to markets with combinatorial outcomes. First, trader attention and liquidity may be severely fractured, since there are nearly limitless things to bet on.

Second, and perhaps more troublesome, running an auctioneer with combinatorial outcomes is computationally intractable (specifically, NP-hard, or as hard as solving SAT) and running a market maker is even harder (specifically, #P-hard, as hard as counting SAT), meaning that the amount of time needed to run is proportional to the number of outcomes, exponential in the number of objects.

It gets worse. Even if we place strict limits on what types of bets traders can make, the market may still be infeasible to run. For example, even if all bets are pairwise, like “Horse B will finish ahead of horse D”, the auctioneer and market maker problems for permutation betting remain NP-hard and #P-hard, respectively. Likewise, Boolean betting remains hard even if the most complicated bet allowed is joining two events, like “E will happen and F will not” [see Chen et al. and Fortnow et al.].

How to build one

Now for some good news: in some cases, fast algorithms are possible. If all bets are subset bets of the form “Horse A will finish in position 1,2, or 10″ or “Horse B,C, or E will finish in position 3″, then permutation betting with an auctioneer is feasible (using a combination of linear programming and maximum matching), even though the corresponding market maker problem is #P-hard. If all bets are of the form “Team B will advance to round k”, tournament betting with a market maker is feasible (using Bayesian network inference). Taxonomy betting with a market maker is feasible (using dynamic programming).

Finally, even better news: fast market maker approximation algorithms are not only possible and practical, they work without limiting what people can bet on, fulfilling the almost magical promise I made at the outset of constructing any bet you can imagine on the fly. Approximation works because people like to bet on things that have a decent chance of happening, say between a 1% and 99% chance. Standard sampling algorithms, including importance sampling and MCMC, are good at approximating prices for such reasonable events. For the extreme (e.g., 1-in-a-billion) events, sampling may fail, so the market maker will have to round off in its own favor to be safe.

Wrapping up, in my mind, the best way to implement a combinatorial-outcome prediction market is as follows:

• Use a market maker. Without one, traders are unlikely to find each other in the sea of choices. Specifically, use Hanson’s LMSR market maker.
• Use an approximation algorithm for pricing. Importance sampling seems to work well. MCMC is another possibility. See Appendix A of this paper.
• The interface is absolutely key, and the aspect I’m least qualified to opine on. I think WeatherBill, Yoopick, and WhenWillWeMove point in the right direction.

I glossed over a number of details. For example, care must be taken for the market maker to always round approximations in its own favor to avoid opening itself up to arbitrage attacks. Another difficulty is how to implement smart accounting to allow traders maximum leverage when they place many interrelated bets. The assumption that traders could lose all their bets is far too conservative — they might have bets that provably cannot simultaneously lose — but may serve as a reasonable starting point in practice.

We just added a new feature to Yoopick designed especially for Frenchmen Chris and Emile and citizens of nineteen other countries to place their swagor* on how many Olympic medals they think their country will win.

We’ve argued that the Yoopick interface is useful for predicting almost any kind of number, and since medal count is indeed a number, we thought we’d give it a try.

Besides, Lance told us it would be a good idea.

Sign up, play, enjoy, and don’t forget to tell us what you think!

Thanks,
Sharad Goel
David Pennock
Dan Reeves

* Scientific wild-ass guess, on record

The 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.

Mathematicians, third graders, and talkative defense department computers alike all know that there is an infallible way to play tic tac toe. A competent player can always force at least a tie against even the most savvy opponent.

In the July issue of Science, artificial intelligence researchers from the University of Alberta announced they had cracked the venerable game of checkers in the same way, identifying an infallible strategy that cannot lose.¹

It doesn’t matter if the strategy is unleashed against a bumbling novice or a flawless grandmaster, it can always eke out at least a tie if not a win. In other words, any player adopting the strategy (a computer, say) makes for the most flawlessy grandmasterest checkers player of all time, period.

The proof of correctness is a computational proof that took six years to complete and was twenty-seven years in the making.

Tic tac toe and checkers are examples of deterministic games that do not involve dice, cards, or any other randomizing element, and so “leave nothing to chance”. In principle, every deterministic game, including chess, has a best possible guaranteed outcome² and a strategy that will unfailingly obtain it. For chess, even though we know that an optimal strategy exists, the game is simply too complex for any kind of proof — by person or machine — to unearth it as of yet.

The UofA team’s accomplishment is significant, marking a major milestone in artificial intelligence research. Checkers is probably the first serious, popular game with a centuries-long history of human play to be solved, and certainly the most complex game solved to date.

Next stop: Poker

Meanwhile, the UofA’s poker research group is building Poki, a computer player for Texas Hold’em poker. Because shuffling adds an element of chance, poker cannot be solved for an infallible strategy in the same way as chess or checkers, but it can in principal still be solved for an expected-best strategy. Although no one is anywhere near solving poker, Poki is probably the world’s best poker bot. (A CMU team is also making great strides.)

Poki’s legitimate commercial incarnation is Poker Academy, a software poker tutor. An unauthorized hack of Poker Academy [original site taken down; see 2006 archive.org copy] may live an underground life as a mechanical shark in online poker rooms. (Poki’s creators have pledged not to use their bot online unidentified.)

Poker web sites take great pains to weed out bots — or at least take great pains to appear to be weeding out bots. Then again, some bot runners take great pains to avoid detection. This is a battle the poker web sites cannot possibly win.