Facebook's "revenue model"

A really interesting article from Businessweek on the purported revenue model behind Facebook. One of the more insightful articles I have read recently.

Statistical models and ivory towers

Increasingly, business analytics and the use of advanced statistics in business decision making is making big surges. Companies from WalMart to FedEx to Netflix have demonstrated how they can build a sustainable business model on the foundations of good data and solid analysis of that data. To analyze the data, people with both the right statistical background and training as well as the required business acumen are critical. In other words, smart people. And this is usually one of the barriers to organizations making the transition from the pre-analytics to the post-analytics space.

Ever so often, the people who push analytics within an organization come from an angle of intellectual superiority. "I can do math better than you and therefore I am right and better than you" is the mindset that many such practitioners bring to the field. This often results in resistance and sometimes, downright hostility to what the "statistical ones" are recommending, from the rest of the organization. Statistical practitioners often end up plowing a lonely furrow in organizations. And then one day, when the implicit sponsorship that got them into that position goes away, so follow the statistical modelers out of the organization. The feeling when they leave is one of profound disappointment and disillusionment on the side of the modelers, and profound relief and also some good old schadenfreude on the side of the old organization hands. How can this situation be averted? How can people who are obviously so intelligent and well-educated prevent making fools of themselves because they failed to fit into an organization?

A few pieces of advice:
1. Be there to "solve the problem" vs "showcase your smarts"
It is important to keep in mind why smart people are hired by organizations. It is usually to solve some business problem or the other. It is not because the organization suddenly discovered that they needed show ponies to come out and parade their smarts. So the first advice to the smart ones is to focus on fixing organizational challenges, i.e. focus on what they are hired for and build their credibility. Once credibility is built up, it becomes infinitely easier to take on work that is more intellectually stimulating and challenging.

2. Simplify your communication around the solution
Smart people often have the ability to get into really deep thinking about the work that they are involved in. Deep thinking indeed is required to fix many of the more difficult problems that companies and society is faced with. However deep thinking around communications is counter-productive. Human beings are simple creatures and usually favour clean narratives over complex ones. Keep the communication around the solution simple and crisp - you may need to get rid of some of the fancy footwork to get there but the trade-off is usually worth it.

3. Be open to idea "give and take"
Finally, approach idea sharing with positive intent. Ideas usually get better when they are critiqued by other people. Valuable perspectives come to light and unrecognized (by the idea creator) weaknesses are called out. Smart people tend to have a bias towards thinking "my way or the highway". This not only prevents ideas from realizing their full potential but also destroy the buy-in that is required from stakeholders. Buy-in is the oxygen that ideas need to survive and grow and developing the political savvy and getting that buy-in is always critical.

The Judgment Deficit - a real-wordliness deficit

I usually don't use my blog to take on or pick apart published pieces - my aim with the blog is to create a diversity of ideas and viewpoints to the reader. There is plenty of intelligent writing in the Web that is thought-provoking and worth bringing to the attention of readers interested in the general ideas of statistics and machine learning. But I came across this learning recently that - I have to admit - caused a fair amount of angst and therefore an urge-to-act. This was the Judgment Deficit by Amar Bhide, a professor of Finance at Tufts University.

The journal article from HBS talks about how machines or computers can make decisions in certain types of situations and human judgment needs to come in at other places. Fair enough. The article then bemoans the recent Great Recession and lays part of the blame on statistical models used in Finance. Specifically the author says
In recent times, though, a new form of centralized control has taken root: mechanistic decision making based on top-down statistical models and algorithms. This has been especially true in finance, where risk models have replaced the judgments of thousands of individual bankers and investors, to disastrous effect.This kind of thinking is not only delusional but also dangerous. (Another part of the article that didn't necessarily get me singing from the rooftops was the lengthy encomium heaped on the economics of Freidrich Hayek, the libertarian economist and the founder of the famous Austrian Economists school. I am still not clear how is that related to the topic at hand.)

The fundamental reason why banks took the risks they took were because there were incentives to do so and there was not enough of an appreciation of the downside. Bankers thought the spiral of rising home-prices, the ability to take the assets off balance sheet and maintain minimal capital reserves, was an unending one and were unable to either spot the inevitable edge of the cliff or were too late to pull back once they spotted it. Also the desire to have these activities as unregulated as possible (to allow free pursuit of profit, or to make 'markets efficient' as Wall Street would argue) led to a number of opacities (about risk) developing in the system which lead to situations where high-schools in Norway were exposed to the collapse of Bear Stearns. So lets not put the blame on top-down statistical models and algorithms. If the alternative that Bhide suggests, having manual underwriters take more of the decisions, were to have happened, I am not sure whether the conclusions reached by these underwriters would have been any different. Apart from a few economists, fund managers and people like Nourini and Taleb (who have made an image of themselves as Cassandras of Doom and therefore have to say anything to maintain that image), nobody - let me say that again - nobody saw this edifice collapsing. No one thought house prices in the US would ever come down. Everyone (human beings and computers alike) were victims of the rear-view mirror bias, i.e. expecting that the future would play out exactly as the past.

So let's go a little bit easy on computers, statistical models and automated decision making.

Part 2/3 of disaster estimation - Understanding the expected monetary loss

In part 1 and part 1b of this series, we reviewed some of the ways in which disaster estimation modelers go about estimating the probability of occurence of a catastrophic event. The next phase is the estimation of expected dollar losses when the catastrophe does take place. What would be some of the impacts on the economic activity within a region and how widespread would be the impacts?

This is where the 'sexiness' of model building techniques meets the harsh realities of extensive ground work and data gathering. When a disaster does occur, the biggest disruptions are usually to life and property. Then there are additional longer term impacts to the economic activity of the region and this is driven both directly by the damage to life and property and also indirectly by the impacts to business continuity and ultimately by the confidence that consumers and tradespeople alike continue to have about doing business in the region. Lets examine this one piece at a time.

The disruptions to life and property can be examined by the number of dwellings or business properties that are built specifically to resist the type of disaster event we are talking about. In the case of fires, it is the number of properties that are built with the right building codes that are built under the right safety codes. This type of information requires some gathering but is publicly available information from the property divisions of several counties. In the case of hurricanes, it can be the number of houses that are constructed after a certain year when stricter building codes started to be enforced. This type of data gathering is extremely effort intensive but is often the difference between a good approximate model versus a really accurate model that can be used for insurance pricing decisions. With a competitive market like insurance where there are many companies operating essentially on price, the ability to build accurate models is a powerful edge.

The damage to life often has a very direct correlation to the amount of property damage. Also with the early warning systems in place ahead of disasters (except earthquakes, I suppose), it has become quite common to have really large disasters like hurricanes not resulting in any major loss of life. One significant example was Hurricane Katrina where more than a thousand people lost their lives in the Gulf Coast area and particularly in New Orleans.

In the next article in the series, I will provide an overview of the ReInsurance market. Which is where a lot of this probabilistic modeling ultimately gets applied.

Interesting links from Jul 17, 2010

1.The over-stated role of banking in the larger economy (Link here)

2. A very interesting article on the original monetary expansionist, John Law (Link here)

3. My latest area of passion, text mining and analytics. A blog entry from SAS. (Link here)

4.Commentary from Prof.Rajan on the inequality in US income and its inevitable lead to a crisis. His analysis on how income inequality forces asset-price inflation is fascinating (Link here)

Disaster estimations - Part 1b/3 Understanding the probability of disaster

Part 1 of my post on modeling catastrophic risk covered measuring the probability that a risk even can occur. This probability can derived based on empirical evidence as well as from other computer models that underlie destructive forces of nature. A good example of a paper that talks about how such a model is built and used is outlined in this paper by Karen Clark, a renowned catastrophic risk modeler and insurer. The paper was a seminal one when it came out as it outlined a scientific method by which such risks could be estimated. The paper is titled "A formal approach to catastrophe risk assessment and management" and the link is here.

The paper outlines an approach to estimate losses from hurricanes impacting the US Gulf coast and the East Coast. The model has a probability assessment for hurricanes making landfall, developed using historical information (going back to about 1910) from the US Weather Service. While this is a great starting point and helps us get to a good estimate of at least a range of losses one can expect and therefore the insurance premiums one should expect to sell, there are important places where the model can be improved. One example is the cyclical nature of hurricane intensity over the last 100 years. Between 1950 and 1994, the Atlantic hurricanes have run through a benign cycle. Hurricane activity and intensity (as measured by the number of named storms and the number of major hurricanes, respectively) have shown an increase since 1994, though. So a model relying on activity from the 1950-1994 period is likely to be off in its loss estimates by more than 20%. See the table for what I am talking about.

How can a modeler correct for such errors in estimates? One way to correct for these estimates is to use the latest in scientific technology and modeling in estimating the probabilities. Developments in scientific understanding of phenomena such as hurricanes means that it is now possible to build computer models that replicate the physics behind the hurricanes. The dynamic physical models incorporate some of the more recent understanding of world climatology, such as the link between Sea Surface Temperatures or SSTs and hurricane intensity. Using some of these models, researchers have been able to replicate the increase in hurricane intensity seen in the last fifteen years in a way that the empirical models built prior to this period have not been able to. The popular science book about global warming called Storm World by Chris Mooney spells out these two different approaches to hurricane intensity estimation, and the conflicts between the chief protagonists of each of these approaches. Based on the recent evidence at least, the more physics based approach certainly appears to be tracking closer to the rapid changes to hurricane intensity. William Gray of Colorado State University, whose annual hurricane forecast has been lucky for many years has been forced to re-fit his empirical model for the rapid increase in hurricane intensity post-1995.

Finally, I leave you with another note about how some of the dynamic physical models work. This is from one of my favourite blogs which is Jeff Masters' tropical weather blog. The latest entry talks precisely about such a dynamic physical model built by the UK Met Office. And I quote:

it is based on a promising new method--running a dynamical computer model of the global atmosphere-ocean system. The CSU forecast from Phil Klotzbach is based on statistical patterns of hurricane activity observed from past years. These statistical techniques do not work very well when the atmosphere behaves in ways it has not behaved in the past. The UK Met Office forecast avoids this problem by using a global computer forecast model--the GloSea model (short for GLObal SEAsonal model). GloSea is based on the HadGEM3 model--one of the leading climate models used to formulate the influential UN Intergovernmental Panel on Climate Change (IPCC) report. GloSea subdivides the atmosphere into a 3-dimensional grid 0.86° in longitude, 0.56° in latitude (about 62 km), and up to 85 levels in the vertical. This atmospheric model is coupled to an ocean model of even higher resolution. The initial state of the atmosphere and ocean as of June 1, 2010 were fed into the model, and the mathematical equations governing the motions of the atmosphere and ocean were solved at each grid point every few minutes, progressing out in time until the end of November (yes, this takes a colossal amount of computer power!) It's well-known that slight errors in specifying the initial state of the atmosphere can cause large errors in the forecast. This "sensitivity to initial conditions" is taken into account by making many model runs, each with a slight variation in the starting conditions which reflect the uncertainty in the initial state. This generates an "ensemble" of forecasts and the final forecast is created by analyzing all the member forecasts of this ensemble. Forty-two ensemble members were generated for this year's UK Met Office forecast. The researchers counted how many tropical storms formed during the six months the model ran to arrive at their forecast of twenty named storms for the remainder of this hurricane season. Of course, the exact timing and location of these twenty storms are bound to differ from what the model predicts, since one cannot make accurate forecasts of this nature so far in advance.

The grid used by GloSea is fine enough to see hurricanes form, but is too coarse to properly handle important features of these storms. This lack of resolution results in the model not generating the right number of storms. This discrepancy is corrected by looking back at time for the years 1989-2002, and coming up with correction factors (i.e., "fudge" factors) that give a reasonable forecast.

If you go to the web-page of the UK Met Office hurricane forecast, you can find a link of interest Reinsurance companies. This link is to buy the hurricane forecast which the UK Met Office has obviously gone to great pains to develop. Their brochure on how the insurance industry could benefit from this research makes for very interesting reading as well.

The BP oil spill and the disaster estimations - Part 1/3

The BP oil spill is already the biggest oil spill in the US and is on its way to becoming an unprecedented industrial disaster, given the environmental impact of millions of barrels of oil gushing into the Gulf of Mexico. Even the most hardened of carbon lovers cannot but be moved at the sight of the fragile wildlife in the Gulf literally soaking in the oil. The ecosystem of the Gulf states which were already ravaged by unrestrained development and the odd super-cyclone is now being struck a death blow by the spewing gusher.

Could the specific chain of events leading up to this spill have been predicted? The answer is no. But that doesn't mean that the outcome could not have been anticipated. Given the technological complexity that some of the deep-sea oil drilling operations typically involve, there was always a measurable probability that one of the intermeshing systems and processes would give way and result in an oil-well that was out of control. As Donald Rumsfeld, Secretary of Defense in the Bush II administration put it, stuff happens. But where there has been an abject failure of human science and industrial technology has been in underestimating the impact of this kind of an event on a habitat and overestimating the power of technology to fix these kinds of problems.

Fundamentally, the science of estimating the impact of disasters can be broken down into three estimations:
one, an estimation that failure occurs
second, the damage expected as a result of the failure
the third, (which is probably a function of the second) are our capabilities in fixing the failure or mitigating the impact of the failure.

In this post, I will discuss the first part of the problem - estimating the probability of failures occurring.

There is a thriving industry and a branch of mathematics that works on the estimation of these extremely low probability events known as Disaster Science. The techniques that the disaster scientists or statisticians use are based on the understanding of the specific industry (nuclear reactors, oil drilling, aerospace, rocket launches, etc.) and is constantly refreshed with the our increasing understanding of the physics or science in general underlying some of these endeavours. The nuclear-power industry's approach analyzes the engineering of the plant and tabulates every possible series of unfortunate events that could lead to the release of dangerous radioactive material, including equipment failure, operator error and extreme weather. Statisticians tabulate the probability of each disastrous scenario and add them together. Other industries, such as aviation, use more probability based models given the hundreds of thousands of data points available on a weekly basis. Then there are more probabilistic approaches such as tail probability estimation or extreme event estimation which uses math involving heavy-tailed distributions for the probability estimation of such events occurring. Michael Lewis in his inimitable style talked about this in an old New York Times article called In Nature's Casino.

One variable that is a factor and often the contributing factor in many such disasters is human error. Human error is extraordinarily difficult to model, just based on past behaviour because there are a number of factors that could just confound such a read. For instance, as humans encounter fewer failures, our nature is to become less vigilant and therefore at greater risk of failing. Both lack of experience and too much experience (especially without having encountered failures) are risky. The quality of the human agent is another variable that has wide variability. At one time, NASA had the brightest engineers and scientists from our best universities join. Now, the brightest and the best go to Wall Street or other private firms and it is often the rejects or the products of second-rung universities that make it to NASA. This variable of human quality is difficult to quantify or sometimes difficult to measure in a way that does not offend people on grounds like race, national origin, age and gender. Let us suppose that the brightest and the best joining NASA previously came from colleges or universities where admission standards required higher scores on standardized tests. Now we know that standardized test scores are correlated with the socio-economic levels of the test takers and hence to variables such as income, race, etc. So now if NASA goes to lower rung colleges, does it mean that it was being more exclusive and discriminatory before (by taking in people with average higher scores) and is now more inclusive now? And can we conclude that the drop in quality now is a direct function of becoming more inclusive on the admission criteria front? It is never easy to answer these questions or even tackle the question without feeling queasy about what one is likely to find while answering the question.

Another variable, again related to the human factor is the way we interact with technology. Is the human agent at ease with the technology confronting him or does he feel pressured and unsure from a decision making standpoint? I have driven stick-shift cars before and I have been more comfortable and at ease with the decision making around gear changes when the car-human interface was relatively simpler and spartan. In my most recent car, as I interact with multiple technology features such as the nav system, the bluetooth enabled radio, the steering wheel, the paddle shifter, the engine revs indicator, I find my attention diluted and I have seen that the decision making around gear changes is not as precise as it used to be.