Category: Blogs and Web

Operations Research and Financial Meltdowns

Aurelie Thiele has a thoughtful post on On Quantitative Finance, inspired by a number of articles on the August melt-down of quant funds. Computational finance has become big business, both in education and on Wall Street. Carnegie Mellon and the Tepper School has been a leader in this, in keeping with our history as a quantitative oriented business school.

Aurelie highlights an article on the role OR people play in this (most won’t call themselves OR people, but I won’t get into that discussion yet again! They use models and mathematics to make better decisions: call it what you want)

But I enjoyed most of all “On Quants,” a short rebuttal to “The Blow-Up” written by a MIT professor in mathematics, Dr. Daniel Stroock, who argues that “[quants’] mission is to blindly keep those stocks moving, not to pass judgment on their value, either to the buyer or to society. Thus, [the author] finds it completely appropriate that quants now prefer the euphemism ‘financial engineer.’ They are certainly not ‘financial architects.’ Nor are they responsible for the mess in which the financial world finds itself. Quants may have greased the rails, but others were supposed to man the brakes.” While the dilution of responsibility in the financial world is becoming a tad worrisome, I liked Stroock’s advocacy for the term “financial engineer.”

I too like the word engineer, but mainly because in fact it would bring into the system some true checks and balances due to an engineer’s professional, legal, and ethical obligations. I think you would be hard pressed to find a professional engineer who would blithely say “I knew it didn’t have any brakes but my job was to put in the biggest engine I could! Shame about those kids, though.” Most engineers would take it as a solemn responsibility to ensure that the nonsense generated by a damn-fool architect was actually safe and effective when put into practice.

Take the following from the National Society of Professional Engineers Code of Ethics:

Fundamental Canons

Engineers, in the fulfillment of their professional duties, shall:

  1. Hold paramount the safety, health, and welfare of the public.
  2. Perform services only in areas of their competence.

    3. …

II. Rules of Practice

  1. Engineers shall hold paramount the safety, health, and welfare of the public.
    1. If engineers’ judgment is overruled under circumstances that endanger life or property, they shall notify their employer or client and such other authority as may be appropriate.
    2. Engineers shall approve only those engineering documents that are in conformity with applicable standards….

I wonder if the “financial engineers” (very few of whom are professional engineers) really understood the assumptions in the models they used, to the level necessary to communicate that to employers.

Having “financial engineers” sounds like a great thing for the market, but only if they truly are engineers with all that entails.

Not at INFORMS

I’m not at this year’s INFORMS meeting.  No, it is not because I am in a snit because it looks like Seattle is going to beat the attendance record Pittsburgh set last year (I predicted it, and predict that Washington will set a new record next year that will take a few years to break).  I’m not going because it is 18 hours of flights away from New Zealand, and I needed to cut back on my travel a bit.

So I won’t be live-blogging the conference.  Laura McLay of “Punk Rock Operations Research” will be there so look for her.  If there are any other OR bloggers at the conference, let me know so I can follow the conference vicariously.

Aurelie Thiele on Operations Research

Aurelie Thiele of Lehigh has a wide-ranging blog on “Thoughts on business, engineering and higher education”. Many of her posts are on operations research. I particularly liked her thoughtful piece on the role operations research plays in the Grand Challenges in Engineering. The leadership of INFORMS put together a white paper on the subject, which I wrote about a while ago. Aurelie takes issue with the fragmented aspect of the proposed role:

The applications-driven paper lacks the unifying theme that a focus on information management would have provided, and instead OR comes across as an add-on to other people’s expertise – certainly valuable, but not critical. I’m not sure why anyone would want to be portrayed as jack of all trades but master of none… The authors also miss the opportunity to portray operations researchers as the center of inter-disciplinary teams bringing scientists from various disciplines together, drawing from their experience in one area to help researchers in another. When I finished reading the paper I wasn’t particularly excited to be working in the field, but I give the authors credit for trying – marketing OR is an uphill battle, given the aversion to math of most regular folks, and every little thing helps.

I have been struggling with many of this very issue during my year in New Zealand (since I have had the opportunity to give a number of “big picture” talks). Is OR just a collection of tools that we jealously guard or is there more commonality amongst us? And are we critical, or just a bit of sprinkle on top of the ice cream sundae? Of course, I remain excited by the field, and reading the rest of the blog, I think Aurelie does also.

Aurelie has been blogging since March: I can’t believe it took me so long to stumble across this excellent blog. Check it out!

Student finds small Turing machine

I wrote previously about a competition Stephen Wolfram (of Mathematica fame) had to show that a particularly small Turing Machine (the “2,3 Turing Machine”) is universal. Sure enough it is, as shown by a University of Birmingham (UK) student, Alex Smith, as reported in Nature. This machine, as shown in a diagram from Wolfram’s blog on the prize has just 2 states and 3 colors. It is certainly surprising that such a simple structure can compute anything any computer can!

I had hoped that operations research might be of help in this, but that does not seem to be the case.

Smith learned about Wolfram’s challenge in an Internet chat room and almost immediately went to work fiddling with the machine. After learning its behaviour, he set about proving that it was computationally equivalent to another type of simple, conceptual computer known as a tag system.

Mathematicians have already shown that tag systems can compute any problem, so proving the two were equivalent effectively proved the power of Wolfram’s machine. Smith’s proof is 44 pages long.

My (what is the word for “person who makes me jealous”? Hmmm… I’ll make up a word: “Jealous Idol”) jidol, Scott Aaronson managed to tear himself away from supermodels to provide a quote (albeit of a “raining on a parade” type):

The solution isn’t hugely relevant to modern computer science, says Scott Aaronson, a computer scientist at the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts. “Most theoretical computer scientists don’t particularly care about finding the smallest universal Turing machines,” he wrote in an e-mail. “They see it as a recreational pursuit that interested people in the 60s and 70s but is now sort of ‘retro’.”

Thanks to Kathryn Cramer for pointing out the result of this competition. She also has a very nice posting in her blog on visualizing the effect of the Federal Reserve rate cut, relevant to my previous posting on visualization.

Visual Display of Operations Research Data/Algorithms/Solutions

When I was a doctoral student (20 years ago!), my adviser John Bartholdi introduced me to Edward Tufte’s Visual Display of Quantitative Information, perhaps hoping that my dissertation would be a model of appealing, provocative display. I am afraid I disappointed him: just like every other doctoral student, my dissertation contained great vomits of tables containing every piece of data I had generated over the previous years. I still find the topic fascinating, and I still find Minard’s graph of the fortunes of Napoleon’s army in the Russian campaign of 1812 an inspiring sight.

Most operations research has pretty terrible presentation. Partially this is because of the high-dimensions in which we work. Only rarely can we work in 2 or 3 dimensions and present comprehensible results. Once in a whle I see something inspiring. One recent work I saw was the EURO Gold Medal presentation of Aharon Ben-Tal. Part of the presentation involved showing how nonlinear optimization algorithms affected the design of a bridge span. The display of the span made things perfectly clear. And the visual display of moats for the traveling salesman problem is a very effective way of getting across the definition and validity of the lower bounds.

Taking some sites from outside of our field for inspiration, let me offer JunkCharts (thanks to Kaiser for providing a comment which led me to the site) and Strange Maps. JunkCharts looks at a variety of displays of numbers, offering critiques about misrepresentations and other biases. Strange Maps matches with my own interest in antique maps, but concentrates on unusual maps, often representing some other data in its geographic layout.

We could use more such creativity in operations research: have you seen something particularly effective?

Better Models or Better Data

In Greg Mankiw’s economics blog, he excerpts from a review of Alan Greenspan’s new book:

Michael Kinsley reviews Alan Greenspan’s book (which I have not yet read). This passage from the review caught my eye:

You gotta love a guy whose idea of an important life lesson is: “I have always argued that an up-to-date set of the most detailed estimates for the latest available quarter is far more useful for forecasting accuracy than a more sophisticated model structure.” Words to live by.

Mike thinks Alan is being hopelessly geeky here. But I think Alan is talking to geeks like me, and also those who used to work for him on the staff of the Federal Reserve.

…

Better monetary policy, he suggests, is more likely to follow from better data than from better models. Relatively little modern macro has been directed at improving data sources. Perhaps that is a mistake.

This issue of data versus model is important in operations research. I must say that in my own consulting, I am rather cavalier about data quality. I was on a project with the United States Postal Service where it was surprising how iffy much of the data was (for instance, we had to estimate (not measure) the amount of mail that was sent between LA and NY each year). My view was that the qualitative conclusions were robust to reasonable variations in the data. So if the model suggested that the number of mail processing facilities could be cut by 25%, this was true no matter what the exact data was. Further, the solutions we generated were going to be pretty good, no matter what the true data was: the day-to-day operation could handle any mis-estimates that were in our approach.

I was not persuasive in this: a tremendous amount of time and effort was spent in getting better data. At the end, this was wise, since the results stand up much better to detailed scrutiny. No group faced with the closure of a facility wants to hear: it really doesn’t matter what the data is, since Trick says so.

I do wonder how many OR projects actually end up with poor or wrong answers due to not getting data of high enough quality. But it is a heck of a lot more fun to develop models rather than hunt down missing data.

My Jealousy Knows No Bounds (Quantum or Otherwise)

I’ll say it straight out:  I am really jealous of Scott Aaronson, author of the Shtetl-Optimized blog. First, he has an audience that provides hundreds of comments to his blog entries and visibility sufficient to be picked up by MIT Technology Review,  albeit with mixed results.   Second, he writes at length in an interesting and provocative way.  For me, it took four times before I could even spell provocative.  Third, he has broad interests and insights that belie his age (he received his doctorate in 2004 and I suspect did so before the normal time).  Fourth, … well, I’ll stop there, since it is only depressing me further.

Except now he has gone and topped everything!  He is being plagiarized by Australian fashion models!  I don’t think I have every written anything that would sound anything but goofy coming from the lips of a fashion model, but there he is providing dialog that actually sounds kinda … smart.   With his luck, he’ll sue for millions, win and get the chance to date the models.

Scheduling people in the Netherlands

John Poppelaars has started a blog entitled “OR at Work” about OR applications in practice. One of his posts is about employee scheduling, where a Dutch directive includes the following requirements:

In normal English the rule states that when an employee performs one or more resident on call duties, each period of 7 times 24 hours must contain at least 90 hours of compensatory rest. So far this is simple, we can easily add up all the rest periods an see if it matches the requested 90 hours. There are additional restrictions however on how the compensatory rest period is divided up over the 168 hours period. These restrictions state that there should be at least one period of 24 hours compensatory rest, four of at least 11 hours, one of at least 10 hours and finally one of at least 8 hours. 7 rest periods in total.

As John points out, such rules are really good for OR people: it makes things so complicated that only OR models have a chance of doing well. Of course, whether our own approaches work (let’s see, let x[10] be one if it is a ten hour rest, but not eleven, then x[10]= … hmmm… maybe a branch-and-price approach instead…) is a little unclear. But bring on complexity: the more people get frustrated, the more they need us!

Operations Research and Tattoos

Being in New Zealand, I am looking for the one big memento of the year. And naturally tattoos come to mind. Here, tattoos are not (necessarily) a sign of a an evening with too much drink, but are a an integral part of the Maori culture, and have been adopted by pakeha (those of European descent: New Zealand almost uniquely takes an indigenous term as a non-pejorative term for the later arriving group) as a sign of national pride. Of course, not just any tattoo would work. It would have to reflect my core values, my aspirations, the real me. So it would have to be operations research oriented. Now if I were a queueing person, I could just get Little’s Law (L= l w) (the little l being lambda), but I am not. I suppose I could get a two dimensional polytope with some objective lines put in. If I was more active in COIN-OR, their logo would look pretty nice, and I think my friend Robin would probably approve (she probably has one already!). Or with a skilled enough artist, perhaps one of Bob Bosch’s wonderful TSP arts: perhaps George Dantzig as TSP covering my back. But none of this feels right.

There is inspiration (some of it quite scary) from the rest of science in this wonderful post from the Loom. But there is also a horror story or two about.