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Class Connection: Optimization

Well, today is the last day for my application portfolio and last few hours before I hand over my exam. The phone rings and it’s my wife – Can you pick me up from the airport at 7:30pm? I say – why not? With a hidden hope that her flight gets delayed for an hour or two, I continued giving final touches to my application portfolio, cleaning up the grammar and other knick knacks. And then it happened!

How difficult would it be to schedule aircraft landing and takeoff at busy airports such as Atlanta? Captured by this thought, I quickly made my way to Wikipedia to gather some basic information. Atlanta’s Hartsfield International airport has 5 runways, 151 domestic and 28 international gates. It is the worlds busiest airport by passenger traffic as well as landings and take offs. Aha!! It had 994,346 flight movements in 2007 (a world record).

If I were to take up the assignment of scheduling the landings and take offs for my final project, how would it look like. First,I would have looked at what I want to achieve. Probably, minimum delays would be one objective. Then I would have thought of my decision variables – something like when will a particular flight take off, or when will it land, or when should it leave the departure gate (assuming the time it will take on the tarmac as constant). Then would come the constraints, many of them. Wow…even thinking of them makes me scared. Some would be, a flight should not be incoming, it has spent X amount of time at the gate, movement time after landing, and so on. There are so many uncertainties associated with this modeling. I could think of then doing a Monte Carlo simulation to simulate the incoming and departure of aircrafts. Something like a Poisson distribution may have helped.

A few minutes of thought and I feel glad that I did not broach this subject when discussing my final project. Something tells me that with the tools and resources we have, this simulation would probably have been very difficult and my poor team mates would have put me on the next flight to India. 

Class Connection: Value focused thinking, Optimization

Ok. See the chart in the figure alongside. At one glance can you tell what is it the chart of. If you have read the title of the blog, you know – its the stimulus package and how the money will be distributed among the several ‘reform’ projects.

Now, study the chart for a few moments and then start thinking about all the concepts taught in class recently. Want to learn how to make this chart?

Start with structuring your values as if you were the President of the United States. What matters to you? Is it functioning, well-oiled economy, employed and healthy citizens, etc. etc. Once you have defined those, lets move on to objectives – what do you want to achieve? Make sure you come up with a comprehensive list of alternatives on how things can be achieved. You will come up with your objectives related to the matters that you really value. Say, for citizens you may want to improve healthcare systems, create more jobs, and provide good education. Similarly, for the economy to come back on track you may want to stop banks from failing, prevent large companies to file for bankruptcy, improve trade relations, and others. Once you have gone so far try to separate the fundamental objectives from the mean objectives. Fundamental – Save banks, Means – Buy back toxic securities, reform accounting systems, pour money into the system. Fundamental – Healthy citizens. Means – Provide tax breaks, ease access to healthcare systems, save jobs. Remember, it is an iterative process. You may have to go back and forth a few times. But never mind, the exercise is worth it. Once you have the complete list down, you will have a clear objective hierarchy and it will start to look very close to the figure.

Next step – You need to think of few important concepts – resources/constraints, (un)certainties, possible outcomes, and consequences. You may want to brush up some skills in defining the metrics for the consequences so that you are comparing apples to apples and not not apples to cucumber where deciding what path to take. Some examples to lead the exercise – Resources/Constraints – $100 billion dollars, 2 years, and 5000 people. Outcomes – banks recover, companies don’t file for bankruptcies. Consequences – Stock market index grow by XX, GDP growth by Y%, Employment rate decrease by Z%. You will need to allocate the resources in proper amounts to each alternative and measure the consequences. The resources will need to be optimally assigned so to achieve the maximum result for each objective. You may want to try out some decision modeling techniques such as utilities, risk profiles, dominance, even monte carlo simulation to help bring metrics to a level field and also to make informed decisions. This may be time consuming so that best way to narrow the problem down as much as possible before starting the modeling process.

And then you should have all the information you need to draw the figure above.

Class Connection – Decisions and Uncertainties

I recently read the book – The Black Swan – by Nassim Nicholas Taleb and was highly impressed by his thought process and his staunch empiricism. While going through his website I chanced upon another article that he wrote, as a sequel to the original book, called ‘The Fourth Quadrant’.

In this article,he talks about the limitation of statistics in providing a fair assessment of risk involved. He argues that current sub-prime crisis is a manifestation of inaccurately interpreting the information that statistics bore upon us. His question is – Are we using models of uncertainty to produce certainties?

For those who are not aware of this theory, here is a small definition to guide you – The Black Swan theory refers to a large-impact, hard-to-predict, and rare event beyond the realm of normal expectations. Unlike the philosophical "black swan problem", the "Black Swan" theory (capitalized) refers only to events of large consequence and their dominant role in history. Black Swan events may also be called outliers.

If you carefully study the fourth quadrant diagram as shown above, he talks about the fallacy of statistics only when the payoffs are large and the probability distributions unique (sometimes derived from events of the past and not necessarily futuristic looking). The problems and assignments that we have done in our class of assessing risks with small projects and payoffs do not necessarily falls into the fourth quadrant, but in the 2nd or 3rd. In those problems we were trying to measure and predict outcomes. But when trying to gauge effects, problems arise. Because here things work differently and the conditional expectation of an increase in a random variable does not drop as the variable gets larger (for examples read the article and the book).

Rare events are by nature, rare. And hence they are difficult to predict. Taleb, at the end of the essay provides some guidelines to follow when faced with a fourth quadrant scenario. Read it and avoid it.

Class Connection: Risk Profiles

Goldman Sachs posted a decent earnings for this quarter yesterday. It reported a healthy profit of $1.66 billion and revenue of $6.56 billion (34% than its highest record) for the first quarter of this year. The reported earnings per share (EPS) of $3.23 beat analysts expectation of about $1.6 by a huge margin. The question everyone asked: Are banks back? Are they turning profitable as indicated earlier by Citibank, Wells Fargo earlier in the quarter? Some sure thought things are getting better and they helped Goldman Stocks (NYSE: GS) rise to $130 a share. But the events of the next day raised a few more questions: Are the earnings sustainable? Standard and Poor did not think so. Did Goldman report its earnings a day earlier with an hidden agenda? To help raise the stock price so that it can issue the necessary stock offering at a higher price in order to repay the TARP. One argues that the world may be going crazy. What was the nature of profits that Goldman reported? Economist and many other publications have explained this and this is exactly what we will discuss further.

Goldman Sachs profits have primarily come from their fixed income, commodity, and currency units and not from its core business, investment banking (or so it was before the bust). Goldman has made a killing during a time when companies are eager to issue bonds, with wider spreads and very low interest rates. Goldman is not only in a position of advantage, being a “big player” and with absence of competitors (remember Lehman and Bear Sterns). But what piqued my interest was this statement in the Economist’s article – “As measured by value-at-risk, a yardstick of how big your gambles are, Goldman took twice as much interest risk as a year ago. Bigger profits in part reflect bigger bets….”. This reminded me of the discussion we had had in class about risk profile and how much are you willing to gamble. If you are billionaire you may have a high risk tolerance as compared to not-so-rich. In this story we see that Goldman showed some traits of a billionaire by taking a huge risk.

This deduction begs to imply that such a risk taking attitude may not be acceptable to investors and thus might show up on the stock prices. That it did. It closed down by 11.56% today on beliefs that the earnings are not sustainable (a statement also conveyed by BlackRock’s managing director, Peter Fisher).

Class Connection: Monte Carlo Simulation

I have done a number of cases on pricing strategies. One approach to handle the question is to think about pricing from three different perspectives 1) Cost based pricing 2) Competitor based pricing and 3) Value based pricing (holy grail). So, when the Monte Carlo simulation was described to us in the class, the last thought that crossed (read: did not cross) my mind was that one of the ways price of a product can be estimated is by using Monte Carlo simulation. I came across an article accidently when searching for avenues where Monte Carlo simulation is employed in business world.

In regular modeling we take a finite set of inputs and the results based on those inputs are analyzed. This type of model is typically referred to as deterministic model (getting the same result every time you use the model). But what if the inputs have probabilities/uncertainties associated with them? What if they cannot be determined effectively? How many inputs can you possibly create than will create a representative se? Every time the model is used, a fresh set of output is generated. Now you may say that I can vary the inputs and record the results to analyze. But c’mon, how many times and when will you be satisfied/tired. This is where Monte Carlo Simulation comes in. This method is used for iteratively evaluating a deterministic model using sets of random numbers as inputs.

Can you now join the dots and use Monte Carlo method to price a product? If not, read on. In the simulation as shown by the author, he knows his company’s marginal costs and the target market share required. Similar information for the competitors are either known or have a probability distribution. For e.g. for player B, the marginal cost is $30 – $50, uniformly distributed, because of uncertainty of the exact figure. He then builds a model where the input parameters are the price and the elasticity of the product. Another important consideration to keep in mind is that a lot of theoretical economic assumptions (for a monopolistic market) are made in this model (for details, read the article). The author then proceeds to run the simulation and gets range of prices and elasticity’s out that can be further analyzed to price the product.

This model does have limitations – it does not adopt the holy grail method (value based pricing) approach, there are theoretical constraints, and finally the lack of exact figures pertaining to competitors. Nevertheless, this model can be a very good starting point, and may lead to a fairly accurate estimate of the price. I strongly believe that this approach should be taken (wherever possible) in conjunction with value based pricing in order to assess the validity of the final figure and build a comfort level around that figure.

P.S. – It would have been great if the author would have provided a spreadsheet version of the model in order for us/me to get a complete picture. But thanks to him anyways for the great article.

P.S. – For those who are wondering what that image is all about – It is the famous Monte Carlo Casino after which the Monte Carlo method gets its name. For details read this.

Class Connection – None

This is a great website to look for some cool charts and graphics. A bonus to all you readers for reading the blog.

Class Connection – None

Another interesting use of charts. This is more for fun but it sure makes things quite obvious. Click on the image to go the original post.

Class Connection: None

I like this chart as it represents a lot of data in a seamless and user friendly manner.

Source: Lawrence Livermore National Laboratory

Class Connection: Value focused thinking

I had read a book during my spring break called ‘Predictably Irrational’. psss….I think I am loving these kind of books now. This books talks about the various irrational decisions a rational person takes, such as, feeling better with a expensive drug as compared to a cheap over the counter medicine, stealing office supplies but not money, and etc. etc. But the interesting premise is that we tend to behave irrationally in a very predictable fashion. Unh!

For the purpose of this article, I want to discuss one such irrationality we display. In chapter 8, the author, Dan Ariely, discusses Why options distract us from our main objective? He cites several examples where given a choice between easy path and low rewards now vs. difficult path but high rewards later, people tend to choose the former. Another example talks about Xiang Yu who led an army against the Ch’in Dynasty.  While his troops slept, he burned his ships and smashed all the cooking pots.  He explained to his troops that they had to either fight their way to victory or die.  His troops won 9 consecutive battles.  Eliminating options improved the focus of his troops.

That brings me to the topic of value focused thinking. If we start with our objectives, then also we will have options/alternatives. But will they tend to distract us? I don’t think so because the options will directly stem out of the main objective. Again, yes we are irrational, and this step of listing the options will have to be made rationally. I am of an opinion that to remove this fallacy from our thinking, it is important that we try to build this habit of value focused thinking and sooner or later, our decision making process will be more streamlined and rewarding.

P.S. The author also has a website. Check it out for a good read.

Class Connection: Value Focused Thinking

After receiving my admission offer from Goizueta, I soon received my first assignment. Go to CareerBuilder (http://www.careerbuilder.com) website and take the tests. In a matter of few hours, the online application spitted out a few career alternatives for me.

The survey was very unique in the sense that it did not start with the alternatives but by asking what I was looking for in my career and what skill sets I believed I had. This is a classic example of value based thinking over alternative based thinking. When in India, our parents (I should say that a large proportion of current/past generation) had only three alternatives for their children’s future. If male, 1) get into family business (if any) 2) become a doctor (anything) 3) become an engineer (mechanical, electrical and more recently computer science). If female, replace the first and third option with ‘get married’. Once the alternatives were decided, logical justification of the choices were made that included everything from social status to affluence to foreign trips.

Coming to US (more recently Goizueta) and going through the CareerBuilder exercise, opened before me a new way of assessing and deciding careers. I had to take three tests that would answer one fundamental question – “What will my ideal career choice be?” The career builder site had the following tests:

1. BCII (Business Career Interest Inventory): To discover what my interests in business work are. In other words, help me define my fundamental objectives.
2. MPRP (Management and Professional Rewards Profile): To help me prioritize what really is motivating to me in work. Alternatively said, what makes my fundamental objectives fundamental?
3. MPAP (Management and Professional Abilities): To help me identify my top abilities in business work.

After finishing my tests, the application provided me a detailed report at the beginning of which were listed my top career choices (what should be and not what I intended it to be). Following that were detailed explanations and reasoning behind the choices (alternatives) provided. Thus, we moved not from “alternatives” to “goals” but from “what are the goals” to “what alternatives I have” or “what alternatives I should explore”.

What did Career Leader tell me to be? Well, that discussion is for another day.