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By Rathan Haran, July 8, 2009 7:04 am

After about a week and half in southern California meeting with advisers and VCs,  I made the trip to Las Vegas with a few high school friends to spend a few days … well, you know, it’s Vegas.  I hadn’t been to Vegas in close to seven years, partly because … well, you know, it’s Vegas and partly because of I’m not a fan of playing games where it’s not in my favor to win.  This was a lesson learned after losing three paychecks in a row playing blackjack at Ceasars during my younger day frequents to Atlantic City.  Now I only play the Powerball Lottery when the jackpot is probabilistically in my favor to risk the dollar (roughly over $150M).

Blackjack was the only game I ever played at a casino, and of course, in typical sadistic gambling fashion, I found myself hovering around the tables, mentally urging people to double down on eleven and stand on dealer six.   And as I kept rattling off the most efficient plays that basic strategy mandates, I started noticing that every player was doing exactly what I was thinking, and the dealers were encouraging the novices to do the same.  I began to wonder in a game where a casino stands to take more when people play ineffectively, why would the house encourage their competition to play the most efficient blackjack strategies?  I started to wonder whether the casinos knew something about basic strategy, much like the Dread Pirate Roberts knew a little something about iocane powder in his battle of wits with the great Fazzini.

Basic strategy outlines how a player should act in a game of blackjack by providing a probabilistic guideline to standing, hitting, splitting, or doubling down based on the combination of cards that the person has and the one card the dealer is showing.  Playing basic strategy dramatically decreases the house’s edge, and the exact amount varies by the rules of each casino (you can calculate those variations here).   There are other ways to increase your odds in a casino, like counting cards or group play, but these ways are heavily frowned upon (read as broken bones) by casino pit bosses.    So why is basic strategy so accepted, and almost so loved by the casino community?  My guess is that the answer lies in the assumptions of basic strategy, which are so often overlooked by the “amateur experts” looking to turn a paycheck into a dream.

I’ve been pretty busy over the last few weeks, so I’ll save the second half of this posting for next week.  Until then, I’ll leave you with this memorable scene from The Princess Bride.

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By Rathan Haran, June 23, 2009 12:26 pm

Me at 7, with bigger guns

I haven’t watched WWF, or WWE, or Friday Night Smackdown since I was a kid (see right), but after reading Wired magazine’s article on Google vs. Facebook, I could not help but think about, in my opinion, the greatest wrestling match of all time.  This battle pitted the up and coming, wildly popular, eccentric and electric young superstar against the stalwart, power punching, mega-myth champion of the world.  Of course, I’m talking about the headliner at WrestleMania 6 where the Heavy Weight Champion of the World Hulk Hogan fought the Intercontinental Champ, The Ulllttiiimmmatteeeee Warrrrrioorrrrrrr!

Champion against champion, title for title, that’s what it’s all about.

Google and Facebook are waging their own war on shaping what the Internet’s future will look like.  They both have an underlying mission to share information, but their core approaches and visions of the web are very different.  Google has historically viewed the web as the great equalizer, the place where information can be accessed by anyone and everyone, and that information can be efficiently found by harnessing the power of cold, hard algorithms.  Facebook sees the web not as the source of information per say, but rather as the medium for which people can share information across their social net.  Instead of relying on complex math necessarily, Facebook puts the power of human sharing in the forefront of spreading information.

Both of these approaches have their place on the web.  What good is a platform to share information easily from the people that matter most if the people that matter the most can’t find the information in the first place, and vice verse?  In my mind, the bigger challenges lie in front of Facebook, because the future of sourcing information from hundreds of friends (if not thousands for the Facebook junkies “power users”) will come down to powerful ranking, grouping, sorting, and prioritizing algorithms, a space that Google has done very well in.

“So wha’cha gonna do brother … when the Hulkster (read as Google) comes for youuuu (read as Facebook)!”  Well, Facebook has been able to pull some ex-Googlers into their shop, to a tune of nearly 9% of their staff, and they have a virtual lock on the social network space (although I begin to worry about the hipness of it when my parent’s generation is “friending” me).  As difficult as it may seem, they may be putting together the pieces and the relationships to really challenge Google’s web dominance.  And maybe, just maybe, they’ll have enough to gorilla slam the powerhouse, avoid the leg-drop, and big splash their way to top, just like the greatest character wrestler of all time was able to do.  R.I.P. The Ultimate Warrior.

Bonus Footage:  Top Ultimate Warrior Promos Ever

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By Rathan Haran, June 9, 2009 10:20 am

What is risk?  When a lot of us hear this word, we automatically think that it has something to do with something bad happening.  What is risk management?  When a lot of us hear this phrase, we automatically think of “Along Came Polly.”  Risk and risk management almost always equates to incredibly awful downsides whether it be in our drive to work (car crashes), our retirement accounts (stock market crashes), or our health (heart crashes).

When we consider risk this way, we are putting unfair weight on the downside of what risk really is.  Risk is really the measure of the unexpected, and the unexpected can work in our favor as well as against us.  That means that even a crazy unexpected positive outcome, like winning the lottery, is also as much of a reality of risk as a plane crash (on island which travels through time for all the LOST fans out there).  Risk is saying that there is a range of outcomes that could happen and we don’t have a clue about what the hell is going to happen in the future.  The wider that range of outcomes, both good and bad, the more risky something is.

Peter L. Berstein, author of Against the Gods: The Remarkable Story of Risk, explains it pretty well.  He says by definition risk is a measure of the unknown, and because of that it is silly to presume and act as if we know what the future holds.  Risk management really is understanding that the future is uncertain, and preparing ourselves and our institutions to deal with the times when things are different from our expectations:

I was particularly intrigued by his comments about using optionality models in corporations as a way to value the option of waiting as an alternative strategy to acting, primarily when making decisions that you can not go back and change.  This is putting a value on the new information you can gain through the passage of time, simply by sitting back and waiting.  Most people, especially in the start-up space, say there is no time for waiting, release early and release often, iterate iterate iterate.  But what if the cost of this far out weighs the value of waiting?

Say your company is launching a new product, and you have to decide how to spend a $1 million dollar budget to advertise it’s awesomeness to the world.  Your marketing division comes to you with a proposal allocating dollars to buying Google Ad Words, a full-page ad in your industry’s top trade magazine, and a viral video campaign.  In passing they mention that the behavioral study of your existing customer base is going well, and the results should be ready in three six nine months, in time for the industry trade show.

We usually get a lot of information about how search engine marketing has the highest brand recall and  video has the best consumer retention rate and the top ten sites that have the exact demographic that we are targeting.  However this information doesn’t guarantee success; the future is completely unknown and its outcomes could range from the greatest advertising campaign of all time to the the most colossal failure destined to be top business school study material (Advertising Mismanagement:  A Case on (Insert Your Company Name Here).  But what is the value of waiting for more information to launch our advertising campaign, specifically our behavioral study?  What if spending $50,000 to finish up the study tells us exactly who to target, and we only need to spend $500,000 to reach them?  Wouldn’t that trade-off be awesome information to have?  This is possible by modeling the value of waiting to act on future information!  This would certainly help in trying to avoid “being too early,” something that venture capital firms often express concerns about.

So we know understand that risk is more than just danger, and really a representation of ranges (positive and negative) of what an outcome can be.  Risk management is really preparing ourselves for the range of outcomes that could happen, and better risk management would also involve valuing what a “wait and see” approach would be.  We do not know what the future holds, so it’s okay to make mistakes, and the sooner we realize that we can’t do anything about uncertainty (that’s not to say we can’t do anything to mitigate the impact of adverse situations), then the sooner we can be happy as a hippo.

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By Rathan Haran, May 17, 2009 12:51 pm

Has Facebook finally gotten it?  With the amount of personal data and interactions that they are able to collect, it should be a no-brainer for them to work on a recommendation framework.  With the advent of user-generated content, we are in information over-load at this point (just look at how many unread emails you have, or how many article feeds you end up ignoring).  The next big move needs to be a way to sort through all of this information and bubble up things that we care about as individuals.

Facebook is in a great place to do this because of their unique position in that people see a ton of value in declaring a lot about themselves over their platform (maybe a bit too much when you are “no longer in a relationship” – broken heart).   The popular a/s/l (age, sex, location) back in the day in AOL chat rooms was one of the first forms of public declaration on the web and this basic desire/need to share information about ourselves has not changed as the web has evolved.  Facebook has created a place where people want to do this, and that could be the most important and valuable thing they have to offer.

I recently met with some folks at Yahoo and noted a particularly interesting thing about users inputting accurate information.  There are tens of thousands registered users at Yahoo that have the zip code 12345.  Now the funny thing about this zip code is that it is for a small county in New York where there are 10 registered business and way less than 10,000 inhabitants!  A Yahoo user doesn’t care to share where they live because there is no good, compelling reason to do it, while a Facebook user wants to share that information because it is valuable to their social network.  I wonder how many people on Yahoo live in 90210.  (On a side note, I found this information on Wolfram Alpha which is a statistical gold mine for anyone who loves numbers.)

Since Facebook has already gotten people to buy into sharing their information, they would have to be crazy not to work with this information to provide better services, including personalized recommendations.  But alas, it appears that this feature is just a way to condense duplicate posts so users can’t spam their friends newsfeeds.  Another tease.  It’s pretty sad to say that the best analysis of online social interactions, and the most entertaining, has been Dateline NBC’s To Catch a Predator.  Yea, I’m sure you were just there to help with some homework.

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By Rathan Haran, May 13, 2009 12:04 pm

The story of Pandora and her box goes a little something like this.  Zeus gets pretty pissed off at this guy Prometheus for throwing fire in a game of RoShamBo, and in retaliation creates this woman Pandora to punish all of mankind (OK, I’m speculating about the game of Rock-Paper-Scissor, but I’d get pretty upset if I got beat by someone using their once in a lifetime throw of fire).  Pandora was given many seductive gifts from the gods and one in particular, the gift of curiosity, led her to open a box releasing all the awfulness into the world (including the credit crisis).  Realizing what she had just done, Pandora quickly slams the box shut, trapping only Hope inside … or maybe not.  After using Pandora.com, it is easy to see why President Obama sees so much hope in the world.

Pandora is an online, streaming music player where users can “customize” their own radio stations.  It’s absolutely brilliant since the only thing you really have to do is enter in a song or artist, and Pandora will automatically stream music that is similar to that song or artist.  Now, instead of spending hours customizing the perfect 80s party playlist (or mix tape/CD for the romantics out there), we can just tell Pandora what song fits our mood at the time and get hours of music delivered too us.  Best of all, if a song comes on that we’re not sure why it was there in the first place (Blame it on the Rain made it on all my mix tapes for some reason), we can easily skip it, and Pandora will exclude songs like it.

So how does Pandora do this?  Well, they’ve hired a team of music analysts who essentially measure each song on 100+ musical characteristics, an idea inspired by the Music Genome Project.  These characteristics, or metrics, make up the “genes” of a song, and their measurements are used to construct a song vector, a mathematical attempt to value the essence of a song.  The similarity of two songs is figured out by measuring the differences between all the musical characteristic of the two songs.  To do this well, Pandora uses a complex distance function, which is essentially saying “how far apart, or different, are these two songs.”  The shorter the distance, the more similar the songs are, and the more likely that song will be played next in your Pandora station.

This is a very powerful framework, but there is one important assumption that shouldn’t be overlooked, and could be a major drawback to implementing this particular recommendation engine.  That critical assumption is that we have identified every factor that captures the je ne sais quoi of a song, which for the non-French speaking means an intangible quality that makes something distinctive.  Do you smell the conundrum brewing?  How does one measure the intangible?  Can you find all the right factors to accurately describe Kris Allen’s performance of Kayne West’s Heartless?  Now while it might be next to impossible to figure out everything that makes a song click, it is very important that you catch the most influential ones in your recommendation model.  Failure to do this could get you voted off.

Pandora is doing a pretty damn good job recommending songs using this framework and they understand that there are a lot of factors that make a song a unique piece of work.  They have developed a framework where they have identified a lot of the measurable, tangible metrics, and have used them to effectively relate songs to each one another.  The next big step in recommendation models would be to understand how each individual values a song, what aspects are more important on a case by case basis, and eventually delivering a personalized, Rathan and Rathan’s Infinite Playlist just for me.

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By Rathan Haran, May 8, 2009 1:19 pm

Berkshire Hathaway had their annual shareholders’ meeting last Saturday (May 2), and Warren Buffett and Charlie Munger totally hated on “higher-order” mathematics used in finance.    Come on guys, what did little ol’ math do to you?  Math and modern portfolio theory were picked on by these investment gurus more than Arnold was picked on by the Gooch! Don’t worry math, I got your back.

The truth of the matter is while Mr. Buffet and Mr. Munger are right about Wall Street’s reliance on complex math, the real blame should be focused on the consultants and investment managers who hawked these models as the end-all, be-all, best thing since sliced bread.  This is one case where it is totally fine to shoot the messenger in the face, however, we shouldn’t abandon using math to help us make better decisions.  We just need to find a better translator, because the message has some very valuable insights.

The reason why we build financial models, or really any models, is to keep track of numerous and complex relationships, something that is very difficult to do in our heads.  The world does not move in simple, predictable ways and the real value in modeling frameworks is to find the best representation of how the world actually behaves.  Sometimes a simple relationship just doesn’t make sense; Mr. Buffet would surely agree that modeling investment growth as a simple linear change is not nearly a good as modeling it as an exponential change (there are a number of high school curriculum that consider this “higher math”).

The key is to fully understand and make transparent that as we increase complexities in models, we increase the number of things that can go wrong, and therefore decrease our certainty of performance.  Think back to our first calculator, which for a lot of us often doubled as our first watch (wicked).  Simple, easy, and reliable.  Now add in a 2.66Ghz Intel Processor, 8GB RAM, 320GB of Storage, and a super-fly, aluminum cased, glow in the dark keyboard.  We have a kick-ass laptop that let’s us do all sorts of things a whole lot better, but it’s not surprising that its average lifespan is somewhere around 2 – 4 years.  And when it goes, we lose everything (yes, even that awesome illegally downloaded music collection that was the envy of our less tech savvy and risk adverse friends).  The funny thing is that Casio can still multiply two five-digit numbers, even after 20+ years!  But that doesn’t make it better.

Unfortunately, the certainty of performance only really bothers us in the worst of times, like when our computers crash and the stock market collapses.  Now, just like backing up our hard-drives, there are ways that we can create more security around financial modeling.  A few things that come to mind are good stress testing frameworks (if your models can’t do this easily for you, then be very cautious with its results), putting good translators (i.e., people who get how the model works AND understand its limitations) in front of decision makers early and often, and moving to a risk-based incentive compensation model (a discussion for another time).

Modeling frameworks are very useful, but they shouldn’t be used as a reason to stop thinking about what we are doing.  The human element in analyzing data can never be replaced by a pure modeling framework.  We shouldn’t site blantent disregard of rational thought by high-paid consultants and star investment analysts as failures in mathematical modeling.  Because remember, when you point your finger at your model, there are three fingers pointing back at you … wait for it  …. wait for it … okay, you got it, cool.