The Power of Teaching

Serendipity works, by definition, in amazing ways. Because I went to New Zealand, I rented a house on Waiheke Island, which is now being considered by another professor for his sabbatical next year, who emailed me. And his email led to my visiting his home page, and clicking on a pointer or two, and then my reading an amazing story about teaching in a presentation by Daniel Fallon of the Carnegie Corporation. I will not ruin the story by excerpting from it, except to say it is about a first grade teacher (that is, a teacher of six-year-olds, though the other definition of “first grade” also holds) named Miss A, and the effect she had on her students. It reminds me, as I face another group of 80 MBA students, that education is important: I am not there primarily to evaluate them, but to teach them, so that as many as possible of them come out of the class with an understanding of, and perhaps a love of, operations research. And while operations research may not be as important as the information a first grade teacher gets across (having a four-year-old son provides a bit of perspective on this front), it is important to remember the effect a teacher can have on the life of his or her students. I won’t be as influential as Miss A, but I certainly can work harder on having an effect on the lives of those in my class.

Dice, Games, and ORStat

Last year, I received a paper from Prof. Henk Tijms of the Vrije University Amsterdam on using stochastic dynamic programming to analyze some simple dice games (pdf version available). A few years ago, I tried to do something similar with an analysis of a game I called Flip, but which is more commonly known as “Close the Box” (the paper appeared in INFORMS Transactions on Education). Both Tijms’ work and mine spend a fair amount of time discussing how well certain easy heuristics do relative to optimal decision making in simple games. Ideally, heuristics would get good, but not optimal solutions: that would make the game challenging as players tried to come up with better and better heuristics. For “Close the Box”, while the optimal decision was quite subtle, some simple heuristics got pretty close (perhaps too close to discern the difference). These games make good classroom demonstrations and even better mini-projects for summer schools and the like. Tijms’ paper was written for a journal aimed at students.

Tijms has also done the field a great service by making his software for applied probability available, which are good tools for education. Check it out at his web page.

Tom Cooley and the role of business schools

Business school deans, as an occupational norm, tend to write in terms acceptable to a very wide audience. After spending all day listening to students, faculty, and administrators, there is no sense adding wood to the fire by writing with undue specificity. This gets even worse when deans write for their alumni magazine. Forests of trees have been sacrificed so deans can say, essentially, “We’re doing great but we’d do even better if you gave us some money”. Part of the job.

So it comes as a surprise when Tom Cooley, Dean of NYU’s Stern School, pens an article on the importance of a research outlook for business schools, an important, but controversial topic. And he pens it in the Fall/Winter issue of the Stern alumni magazine! In the article, he takes on former deans and even the AACSB (the association that certifies business schools):

Jeffrey Garten, former dean of the Yale School of Management, told The New York Times that business school education should be more “clinical” and that business school faculty should be required to have more practical experience in business. And this spring, an Association to Advance Collegiate Schools of Business (AACSB) task force issued a draft report on research. Among its conclusions: that business schools should “demonstrate the impact of faculty intellectual contribution to targeted audiences,” and that AACSB should “develop mechanisms to strengthen interaction between academics and practicing managers in the production of knowledge in areas of greatest interest.”

I’m convinced that the critics of business school education have it exactly backwards: the pressure business schools face to give in to a vocational focus means that students do not acquire the analytical and intellectual training needed to inform a leadership career, and that enables businesspeople to deal with a range of variables far greater than a purely vocational “how to” approach can address.

Cooley celebrates many of the past successes of a research approach to business education:

Between 1954 and 1966 the Ford Foundation spent $35 million to foster business education and research at five schools: Carnegie-Mellon University, the University of Chicago, and Columbia, Harvard, and Stanford Universities. Using the transformation of medicine and engineering as a model, these schools invested heavily in research and in doctoral programs. The investments have clearly produced returns. In 1955-1956, graduate business education was virtually non-existent. Now, well over 100,000 graduate business degrees are awarded annually by 650 AACSB programs. Business schools now enjoy greatly improved status as professional schools, in large measure because the intellectual value of the undertaking is recognized. The widespread adoption of the MBA degree as a qualification for future business leaders has legitimized the position the Ford Foundation and others took 50 or more years ago.

Perhaps more important, business schools have generated ideas of depth and daring that have changed business and financial markets in important ways. Professors Finn Kydland and Edward Prescott were awarded the 2004 Nobel Prize in Economics for work they did in the 1970s and 1980s at Carnegie-Mellon’s Graduate School of Industrial Administration, now called the Tepper School of Management. Their work on what is called “the inconsistency of optimal plans” established the foundation for an extensive research program on the credibility and political feasibility of economic policy, shifting the practical discussion of economic policy away from isolated policy measures toward the institutions of policy-making. Kydland and Prescott were also cited for having transformed our understanding of business cycles by integrating it with the theory of economic growth.

Finn is a friend of mine, and is truly brilliant. He is, not coincidently, trained as an OR person. When he teaches, he is about as far from “vocational instruction” as you can possibly get, to the chagrin of some of our less open-minded MBA students (CMU MBAs are generally very open to research-oriented topics in the classroom: it is one of the hallmarks of our program).

Tom obviously likes Carnegie Mellon, even if he gives us a hyphen that we jettisoned years ago, and calls us a School of Management instead of School of Business.

Tom is an economist, and he gives a great example of how insights from economics provide the basis for serious training in management, and the harm a vocational, current-best-practices approach causes:

Would you rather have business school graduates who know what kinds of contracting structures businesses now use, or students who understand that contracts exist to solve moral hazard, asymmetric information, commitment, and agency problems? It is precisely because we don’t yet know the problems that we will be facing that practice-driven education, focused on current solutions to current problems, will always fall short.

He closes with some comments inspired by the book Moneyball:

One of my favorite business books of the past few years is Moneyball by Michael Lewis, the story of the Oakland A’s and their extremely successful general manager, Billy Beane. More important, it is the story of how baseball has been transformed by a generation of researchers (aka baseball nerds) whose major contact with the game is through data analyzed by increasingly complex computer programming. Beane understood that this apparently arcane research had the potential to create extraordinary value for his team. Indeed, under Beane’s leadership, the perennially undercapitalized A’s managed to reach the playoffs for four consecutive years. Over that period, their salary cost per victory was less than half of the next highest spending team and less than a quarter of teams like the New York Yankees.

Beane overturned the most basic principles of one of the most tradition-bound businesses in America – professional baseball – by using sophisticated statistical research in place of traditional “gut instinct.” Several major league general managers who have never played the game are now similarly schooled in the research tradition of “moneyball,” and executives in sports like basketball and football are catching on.

Moneyball is a good metaphor for what happens in academic research. You hire a bunch of bright, well-trained people with strong technical skills and a passion about what they study and turn them loose. With the right personnel, the right conditions, the right insights, and with a forward-looking rather than a backward-looking focus, exciting things can happen. And that research, applied in the right circumstances, has truly enormous potential for change.

To make the obvious OR connection: the sort of analytic skills you gain and the insights you learn from an analytic approach to business problems is what OR is all about. It is not about linear programming, or dual variables: it is about thinking clearly about objectives, decisions, and constraints.

Sorry for the long post, but Tom’s words speak for themselves. For those of us who are research-oriented, this is really inspiring stuff. Thanks to my friend and colleague Chris Telmer for the pointer.

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.

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.

Six Key Ideas of Operations Research?

For most of us, teaching a first course in operations research involves trotting out our old friends linear programming, sensitivity analysis, integer programming, and so on. Twenty years ago, we used linear algebra; today, we concentrate more on modeling. But it is not clear that students get the Big Picture. Like “Modeling is useful” or “Decisions can (sometimes) be made on marginal information” or even “If you add constraints, the best solution gets worse”.

Steven Baker, whose Blogspotting I regularly visit and which often provides material here, has a posting entitled Economics and Braille Keyboards pointing to an interview with the economist Robert Frank. Frank has a new book out based on the idea that there are really only about six key ideas in economics, and if you can get students to think about and apply those six ideas, then you have really had a successful introductory course. The title comes from a student essay on why drive-through banks have braille on the keyboard. If blind people can’t drive, why provide braille? The answer is in cost-benefit analysis: it would cost more to have two types of keyboards, so standardization is better (assuming putting on those dots is not costly in itself). As for benefits, this holds even if the benefit is zero. If the benefit is positive (blind people in cabs, say), then it holds even stronger.

What would be the six key ideas/insights about operations research? I listed a few above. Some others might include “If there is randomness, systems without slack run out of control” and “Nonbinding constraints don’t affect the optimal solution” (these are more insights than key ideas). As a prescriptive science (rather than the descriptive science of most economists), it is not clear that this method of teaching works as well, but it is useful for us to think about what we really want to get across in our classes.

OR, Poker, and Teaching

It is a lovely morning here in New Zealand, and the sun is rising over the bay that my house overlooks. So naturally I am wandering around the web.

Gary Carson’s Math and Poker blog is one that I regularly follow (not the least because he points to this blog). He writes about the role OR plays in understanding poker and about how OR is taught:

Part of the problem that operations research has in getting recognition is the way we teach it — it’s taught as a bunch of algorithms for the most part. Even when it’s taught as a bunch of models to be applied to real problems, the models are taught as members of a tool kit. Seldom do we teach OR as a process of using models to abstract or isolate the elements of a problem that are critical to decision making related to that problem.

I think OR education needs to put more of a focus on using the model and less focus on solving the model. I think students would form a deeper grasp of what OR is all about if that was done. Stuff like analysis of residuals and sensitivitey of LP solutions are just too important to be glossed over.

I teach at a business school and we have moved much more to teaching about using models for real-world business decisions. Things like sensitivity do end up taking a backseat, however, since without understanding the underlying algorithm/mathematics things like dual values are just mysterious black boxes. The challenge is, I think, how to get enough of the fundamentals across so people can confidently and correctly use the resulting models. And I think we are all over the map on this at the moment, to the detriment of the field.

The Blue Ball Production Problem

It’s course preparation time again. For those of you teaching production or scheduling, if you are looking for a graphic to show the need for split-second planning in certain production processes, I highly recommend the Blue Ball Machine. Hypnotic!

From Wired Magazine:

A Rube Goldberg machine made of animated tiles, with hundreds of blue balls moving in time to music from Pee-wee’s Big Adventure.
Max [of]says: This is our most viewed title ever. It was created by the Web site Something Awful – they had 100 people make 1-inch tiles, and the only rule was that a ball had to enter at a certain place and exit at another. It came out awesome.

Teaching Operations Research and Management Science

One of the ironies of academic life is that a large portion of it is taken over by an aspect that few of us have formal training in: teaching! The average high school teacher has a firmer foundation on pedagogic theory than almost any university professor. This is particularly problematic in operations research since the education most of us received (research oriented knowledge/teaching methods) is far removed from what we are expected to teach (undergraduate/MBA “practical” instruction). So many of us spend a year or two mimicking our previous education (“Here’s a great proof for you!”) rather than teaching students what they really should know. And given my clearly enthusiastic belief in the usefulness and relevance of operations research, that is really a shame: instead of inspiring students, we turn them against our field.

INFORMS has had its Teaching Management Science Conference over the past few years. This year’s version will be in San Francisco in July. The program looks to have a good mix of theory (how do students learn) and practice (successful examples from top faculty). You can even stay in dorms to get the full “Live life as a student” aspect.