Category Archives: gambling

mad scientist geek with test tube & lab coat

Casinos for geeks

Gambling has been mocked as “a tax on the mathematically challenged”. Gamblers are stereotyped as losers in life. Casinos reinforce this by literally kicking out people who display too much intelligence. They ban card counting and people who simply win too much. They don’t allow computers or Internet connections in the sports book to block out information. They emphasize familiarity over innovation, cementing their appeal to habitual gamblers over geeks.

floating dice 2 and 5In the eyes of a casino, a sharp is indistinguishable from a cheat. More than boring, this seems fundamentally unfair and unsustainable, inviting disguise. It also turns off a wealthy, influential, and game-loving segment of the population.

What I want: A casino by geeks for geeks that celebrates innovation, encourages cleverness, welcomes gadgets and wifi, and fosters hacking, outwit, and outplay.

At least one casino has seen the light. The M Casino in Las Vegas is parterning with Cantor Fitzgerald to support in-game sports betting with few rules and caps, inviting sharps to, more or less, “bring it on”.

…gamblers can bet on the game even during play, accepting ever-changing point-spreads and odds. They can invest money on a Knicks foul shot going through the hoop or a Dodger getting to first base — contending with ever-evolving odds.

More critically, bettors can create hedges while jumping in and out of positions. But instead of buying into the fast-breaking moves of Microsoft, they’re betting on the Mariners’ impending fortunes.

This form of wagering is new to Las Vegas but old-hat in other markets.

unlike in most other casinos, laptop computers are welcome.

…”The M wants [sharp bettors] to be there,” believes [professional sports-bettor Steve] Fezzik. “They want your information, and that’s a progressive attitude.

Kudos to M for taking a chance on a more interesting future and to Cantor for making it happen.

What Cantor is debuting may not be a whole lot more than betfair indoors, but it’s a long overdue start. Here’s to hoping we see even more innovation, including smarter and more expressive markets.

It’s official: More people are playing Predictalot than Mafia Wars

It’s true.

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.

predictalot most popular game app on yahoo 2010-06-12

Predictalot for World Cup: Millions of predictions, stock market action

I just left the 2010 ACM Conference on Electronic Commerce, where six (!) out of 45 papers were about prediction markets.

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:

Predictalot recent activity screenshot 2010-06-11 18:45

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. 😉

playing predictalot from the yahoo! toolbar

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:

Let the madness begin

Sixty-five men’s college basketball teams have been selected. Tomorrow there will be sixty-four. Half of the remaining teams will be eliminated twice every weekend for the next three weekends until only one team remains.

On April 5th, we will know who is champion. In the meantime, it’s anybody’s guess: any of 9.2 quintillion things could in principle happen.

At Predictalot it’s your guess. Make almost any prediction you can think of, like Duke will win go further than both Kansas and Kentucky, or the Atlantic Coast will lose more games than the Big East. There’s even the alphabet challenge: you pick six letters that include among them the first letters of all four final-four teams.

Following Selection Sunday yesterday, the full range of prediction types are now enabled in Predictalot encompassing hundreds of millions of predictions about your favorite teams, conferences, and regions. Check it out. Place a prediction or just lurk to see whether the crowd thinks St. Mary’s is this year’s Cinderella.

Come join our mad science experiment where crowd wisdom meets basketball madness. We’ve had many ups and down already — for example sampling is way trickier than I naively assumed initially — and I’m sure there is more to come, but that’s part of what makes building things based on unsolved scientific questions fun. Read more about the technical details in my previous posts and on the Yahoo! Research website.

And for the best general-audience description of the game, see the Yahoo! corporate blog.

Update: Read about us on the New York Times and VentureBeat.

You can even get your fix on Safari on iPhone!

Dave playing Predictalot on iPhone

Below is a graph of our exponential user growth over the last couple days. Come join the stampede!

graph of YAP installs for Predictalot

Review of Fortune’s Formula by William Poundstone: The stranger-than-fiction tale of how to invest

What is a better investment objective?

  1. Grow as wealthy as possible as quickly as possible, or
  2. Maximize expected wealth for a given time period and level of risk

The question is at the heart of a fight between computer scientists and economists chronicled beautifully in the book Fortune’s Formula by Pulitzer Prize nominee William Poundstone. (See also David Pogue’s excellent review.*) From the book’s sprawling cast — Claude Shannon, Rudy Giuliani, Michael Milken, mobsters, and mob-backed companies (including what is now Time Warner!) — emerges an unlikely duel. Our hero, mathematician turned professional gambler and investor Edward Thorp, leads the computer scientists and information theorists preaching and, more importantly, practicing objective #1. Nobel laureate Paul Samuelson (who, sadly, recently passed away) serves as lead villain (and, to an extent, comic foil) among economists promoting objective #2 in often patronizing terms. The debate sank to surprisingly depths of immaturity, hitting bottom when Samuelson published an economist-peer-reviewed article written entirely in one-syllable words, presumably to ensure that his thrashing of objective #1 could be understood by even its nincompoop proponents.

Objective #1 — The Kelly criterion

Objective #1 is the have-your-cake-and-eat-it-too promise of the Kelly criterion, a money management formula first worked out by Bernoulli in 1738 and later rediscovered and improved by Bell Labs scientist John Kelly, proving a direct connection between Shannon-optimal communication and optimal gambling. Objective #1 matches common sense: who wouldn’t want to maximize growth of wealth? Thorp, college professor by day and insanely successful money manager by night, is almost certainly the greatest living example of the Kelly criterion at work. His track record is hard to refute.

If two twins with equal wealth invest long enough, the Kelly twin will finish richer with 100% certainty.

The Kelly criterion dictates exactly what fraction of wealth to wager on any available gamble. First consider a binary gamble that, if correct, pays $x for every $1 risked. You estimate that the probability of winning is p. As Poundstone states it, the Kelly rule says to invest a fraction of your wealth equal to edge/odds, where edge is the expected return per $1 and odds is the payoff per $1. Substituting, edge/odds = (x*p – 1*(1-p))/x. If the expected return is zero or negative, Kelly sensibly advises to stay away: don’t invest at all. If the expected return is positive, Kelly says to invest some fraction of your wealth proportional to how advantageous the bet is. To generalize beyond a single binary bet, we can use the fact that, as it happens, the Kelly criterion is entirely equivalent to (1) maximizing the logarithm of wealth, and (2) maximizing the geometric mean of gambles.

Investing according to the Kelly criterion achieves objective #1. The strategy provably maximizes the growth rate of wealth. Stated another way, it minimizes the time it takes to reach any given aspiration level, say $1 million, or your desired sized nest egg for retirement. If two twins with equal initial wealth were to invest long enough, one according to Kelly and the other not, the Kelly twin would finish richer with 100% certainty.

Objective #2

Objective #2 refers to standard economic dogma. Low-risk/high-return investments are always preferred to high-risk/low-return investments, but high-risk/high-return and low-risk/low-return are not comparable in general. Deciding between these is a personal choice, a function of the decision maker’s risk attitude. There is no optimal portfolio, only an efficient frontier of many Pareto optimal portfolios that trade off risk for return. The investor must first identify his utility function (how much he values a dollar at every level of wealth) in order to compute the best portfolio among the many valid choices. (In fact, objective #1 is a special case of #2 where utility for money is logarithmic. Deriving rather than choosing the best utility function is anathema to economists.)

Objective #2 is straightforward for making one choice for a fixed time horizon. Generalizing it to continuous investment over time requires intricate forecasting and optimization (which Samuelson published in his 1969 paper “Lifetime portfolio selection by dynamic stochastic programming”, claiming to finally put to rest the Kelly investing “fallacy” — p.210). The Kelly criterion is, astonishingly, a greedy (myopic) rule that at every moment only needs to worry about figuring the current optimal portfolio. It is already, by its definition, formulated for continuous investment over time.

Details and Caveats

There is a subtle and confusing aspect to objective #1 that took me some time and coaching from Sharad and Dan to wrap my head around. Even though Kelly investing maximizes long-term wealth with 100% certainty, it does not maximize expected wealth! The proof of objective #1 is a concentration bound that appeals to the law of large numbers. Wealth is, eventually, an essentially deterministic quantity. If a billion investors played non-Kelly strategies for long enough, then their average wealth might actually be higher than a Kelly investor’s wealth, but only a few individuals out of the billion would be ahead of Kelly. So, non-Kelly strategies can and will have higher expected wealth than Kelly, but with probability approaching zero. Note that, while Kelly does not maximize expected (average) wealth, it does maximize median wealth (p.216) and the mode of wealth. See Chapter 6 on “Gambling and Data Compression” (especially pages 159-162) in Thomas Cover’s book Elements of Information Theory for a good introduction and concise proof.

Objective #1 does have important caveats, leading to legitimate arguments against pure Kelly investing. First, it’s often too aggressive. Sure, Kelly guarantees you’ll come out ahead, but only if investing for “long enough”, a necessarily vague phrase that could mean, well, infinitely long. (In fact, a pure Kelly investor at any time has a 1 in n chance of losing all but 1/n of their wealth — p.229) The guarantee also only applies if your estimate of expected return per dollar is accurate, a dubious assumption. So, people often practice what is called fractional Kelly, or investing half or less of whatever the Kelly criterion says to invest. This admittedly starts down a slippery slope from objective #1 to objective #2, leaving the mathematical high ground of optimality to account for people’s distaste for risk. And, unlike objective #2, fractional Kelly does so in a non-principled way.

Even as Kelly investing is in some ways too aggressive, it is also too conservative, equating bankruptcy with death. A Kelly strategy will never risk even the most minuscule (measure zero) probability of losing all wealth. First, the very notion that each person’s wealth equals some precise number is inexact at best. People hold wealth in different forms and have access to credit of many types. Gamblers often apply Kelly to an arbitrary “casino budget” even though they’re an ATM machine away from replenishment. People can recover nicely from even multiple bankruptcies (see Donald Trump).

Some Conjectures

Objective #2 captures a fundamental trade off between expected return and variance of return. Objective #1 seems to capture a slightly different trade off, between expected return and probability of loss. Kelly investing walks the fine line between increasing expected return and reducing the long-run probability of falling below any threshold (say, below where you started). There are strategies with higher expected return but they end in ruin with 100% certainty. There are strategies with lower probability of loss but that grow wealth more slowly. In some sense, Kelly gets the highest expected return possible under the most minimal constraint: that the probability of catastrophic loss is not 100%. [Update 2010/09/09: The statements above are not correct, as pointed out to me by Lirong Xia. Some non-Kelly strategies can have higher expected return than Kelly and near-zero probability of ruin. But they will do worse than Kelly with probability approaching 1.]

It may be that the Kelly criterion can be couched in the language of computational complexity. Let Wt be your wealth at time t. Kelly investing grows expected wealth exponentially, something like E[Wt] = o(xt) for x>1. It simultaneously shrinks the probability of loss, something like Pr(Wt< T) = o(1/t). (Actually, I have no idea if the decay is linear: just a guess.) I suspect that relaxing the second condition would not lead to much higher expected growth, and perhaps that fractional Kelly offers additional safety without sacrificing too much growth. If formalized, this would be some sort of mixed Bayesian and worst-case argument. The first condition is a standard Bayesian one: maximize expected wealth. The second condition — ensuring that the probability of loss goes to zero — guarantees that even the worst case is not too bad.

Conclusions

Fortune’s Formula is vastly better researched than your typical popsci book: Poundstone extensively cites and quotes academic literature, going so far as to unearth insults and finger pointing buried in the footnotes of papers. Pounstone clearly understands the math and doesn’t shy away from it. Instead, he presents it in a detailed yet refreshingly accessible way, leveraging fantastic illustrations and analogies. For example, the figure and surrounding discussion on pages 197-201 paint an exceedingly clear picture of how objectives #1 and #2 compare and, moreover, how #1 “wins” in the end. There are other gems in the book, like

  • Kelly’s quote that “gambling and investing differ only by a minus sign” (p.75)
  • Louis Bachelier’s discovery of the efficient market hypothesis in 1900, a development that almost no one noticed until after his death (p.120)
  • Poundstone’s assertion that “economists do not generally pay much attention to non-economists” (p.211). The assertion rings true, though to be fair applies to most fields and I know many glaring exceptions.
  • The story of the 1998 collapse of Long-Term Capital Management and ensuing bailout is sadly amusing to read today (p.290). The factors are nearly identical to those leading to the econalypse of 2008: leverage + correlation + too big to fail. (Poundstone’s book was published in 2005.) Will we ever learn? (No.)

Fortune’s Formula is a fast, fun, fascinating, and instructive read. I highly recommend it.

__________
* See my bookmarks for other reviews of the book and some related research articles.

Psst: WeatherBill doesn’t know New Jersey is the new Florida: Place your bets now

Quantifying New York’s 2009 June gloom using WeatherBill and Wolfram|Alpha

In the northeastern United States, scars are slowly healing from a miserably rainy June — torturous, according to the New York Times. Status updates bemoaned “where’s the sun?”, “worst storm ever!”, “worst June ever!”. Torrential downpours came and went with Florida-like speed, turning gloom into doom: “here comes global warming”.

But how extreme was the month, really? Was our widespread misery justified quantitatively, or were we caught in our own self-indulgent Chris Harrisonism, “the most dramatic rose ceremony EVER!”.

This graphic shows that, as of June 20th, New York City was on track for near-record rainfall in inches. But that graphic, while pretty, is pretty static, and most people I heard complained about the number of days, not the volume of rain.

I wondered if I could use online tools to determine whether the number of rainy days in June was truly historic. My first thought was to try Wolfram|Alpha, a great excuse to play with the new math engine.

Wolfram|Alpha queries for “rain New Jersey June 200Y” are detailed and fascinating, showing temps, rain, cloud cover, humidity, and more, complete with graphs (hint: click “More”). But they don’t seem to directly answer how many days it rained at least some amount. The answer is displayed graphically but not numerically (the percentage and days of rain listed appears to be hours of rain divided by 24). Also, I didn’t see how to query multiple years at a time. So, in order to test whether 2009 was a record year, I would have to submit a separate query for each year (or bypass the web interface and use Mathematica directly). Still, Wolfram|Alpha does confirm that it rained 3.8 times as many hours in 2009 as 2008, already one of the wetter months on record.

WeatherBill, an endlessly configurable weather insurance service, more directly provided what I was looking for on one page. I asked for a price quote for a contract paying me $100 for every day it rains at least 0.1 inches in Newark, NJ during June 2010. It instantly spat back a price: $694.17.



WeatherBill rainy day contract for June 2010 in Newark, NJ

It also reported how much the contract would have paid — the number of rainy days times $100 — every year from 1979 to 2008, on average $620 for 6.2 days. It said I could “expect” (meaning one standard deviation, or 68% confidence interval) between 3.9 and 8.5 days of rain in a typical year. (The difference between the average and the price is further confirmation that WeatherBill charges a 10% premium.)

Below is a plot of June rainy days in Newark, NJ from 1979 to 2009. (WeatherBill doesn’t yet report June 2009 data so I entered 12 as a conservative estimate based on info from Weather Underground.)


Number of rainy days in Newark, NJ from 1979-2009

Indeed, our gloominess was justified: it rained in Newark more days in June 2009 than any other June dating back to 1979.

Intriguingly, our doominess may have been justified too. You don’t have to be a chartist to see an upward trend in rainy days over the past decade.

WeatherBill seems to assume as a baseline that past years are independent unbiased estimates of future years — usually not a bad assumption when it comes to weather. Still, if you believe the trend of increasing rain is real, either due to global warming or something else, WeatherBill offers a temptingly good bet. At $694.17, the contract (paying $100 per rainy day) would have earned a profit in 7 of the last 7 years. The chance of that streak being a coincidence is less than 1%.

If anyone places this bet, let me know. I would love to, but as of now I’m roughly $10 million in net worth short of qualifying as a WeatherBill trader.

Thank you Bangalore

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
”

David Pennock speaking at Yahoo! Big Thinkers India June 2009Audience at Yahoo! Big Thinkers India June 2009

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 and wwdd: what would Sharad or Dan 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.

Betcha's Saint Nick fights cops, courts, Congress… and wins (for now)

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.

An (old) essay on new media

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.

KISS prediction markets (lingo) goodbye

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:

InklingMarkets' explanation of short selling

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.