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OR/MS Today - August 2003 Innovative Education Weapons of Mass Instruction Connecting the seat of the intellect to the seat of the pants By Sam Savage Robert Selman, a psychologist at Harvard University, has coined the term "Weapon of Mass Instruction" (or WMI) for a pedagogy that yields widespread educational impact over a short period of time. In this article I will present some thoughts for developing WMI in the area of operations research and management science. A Brief History of Civilization In a 1997 article in OR/MS Today on "Weighing the Pros and Cons of Decision Technology in Spreadsheets" [1], I presented a brief history of civilization, which I will elaborate on here.
I don't know much about how we learned to read and write, but the industrial age was characterized by harnessing the power of physics. This required chains of technologies, starting at the top with either mathematics or physics (depending on whether you ask a mathematician or physicist) and then moving on through engineering to the often ignored field of industrial design. To allow us to grasp the power of physics with our own hands, an industrial designer first had to develop an appropriate handle. Some famous examples include the steering wheel, the electric light switch and the typewriter keyboard. Now at the dawn of the information age, the power we are harnessing is not physical and you can't grasp it with your hand. You grasp it with your mind. Thus the role of the informational designer is not to develop a handle but a mindle. Operations research and management science are the quintessential "information age" fields, and are in dire need of better mindles as a necessary step toward WMI.
These are the equations of motion of a bicycle, and you solve them every time you ride; not, however, through the seat of your intellect, but through the seat of your pants. Experiential learning in an interactive environment is a powerful form of WMI. Can this be applied to OR/MS? Absolutely. At the time I described our work in terms of an automotive analogy. If linear programming was an internal combustion engine and the electronic spreadsheet was a wagon, then we were developing an automobile. The user interface, shown in Figure 1, was the dashboard, and contained the minimum mindles required to grasp optimization. Note that the Extension command was used to create vector products in the days before the SUMPRODUCT formula.
What'sBest! began to bring down the algebraic curtain, and I was reborn as a management scientist. Instead of teaching algebra, I now presented students with small interactive spreadsheet models containing what I called the developmental necessities of the application or DNA for short [1]. The DNA, usually no more than a handful of cells in a spreadsheet, provides a mindle for an entire class of optimization application. It is a living, breathing solution to a problem that can be replicated, scaled up and recombined with other DNA to create integrated models. This at last was a weapon of mass instruction. Students and practitioners loved it. But on several occasions I found professors of management science painstakingly building tableaus in the spreadsheet to use with What'sBest! This bypassed the powerful modeling capability of the spreadsheet, and was analogous to loading the engine onto the back of the wagon and then pulling the whole thing along with a horse. In the words of George Dantzig, the father of linear programming, "The final test for a theory is its capacity to solve the problems which originated it." [2] Internal combustion engines did not become truly successful until drivers didn't need to know they were using one. As a powerful testament to Dantzig's own theory, thousands of people are now performing optimization with no knowledge of the simplex algorithm. In fact, today, between What'sBest! and the Excel Solver, one can solve linear, non-linear and combinatorial problems, and build logical statements into optimization models. I began to use simulation to teach stochastic modeling to students who had already taken introductory statistics. I tested their intuition on the outcomes of the single spin of a game board spinner, and on the averages of two spins. At first I thought that much of the class didn't understand the central limit theorem. Then I made the shocking discovery that many students didn't even have a mindle on the concept of probability distributions.
In a given class, between a third to half of my students drew a bell shaped distribution for a single spin. If probability were a bicycle, these students wouldn't know the front from the back, yet they had all passed the equivalent of bicycles 101. In the words of Mark Twain, their schooling had interfered with their education. Presumably scores would be higher among Eskimos on an ice floe, whose only contact with probability theory came from playing Spin the Walrus Tusk. This work was described in my 1994 article in OR/MS Today [3] and also in "Statistical Analysis for the Masses" [4]. More formal testing in this area has been performed by Armann Ingolfson and David Zalkind, as described in 1999 articles in Interfaces [5, 6]. Using spinners to connect the seat of the intellect to the seat of the pants was a central theme in the seminal 1959 book on portfolio theory by Harry Markowitz [7]. When I first saw this classic a few years ago, I marveled that much of it could be understood by a bright high school student. Merton Miller, who shared the Nobel Prize in Economics with Markowitz and Sharpe in 1990, believed that Harry had never received the respect he deserved in the academic community because he had written a book that could be understood by the masses. As I will discuss further on, academia is probably not the place to look for WMI. A particularly gratifying case involved an investment banker client of mine in the entertainment industry. Rick Medress is president of Cineval LLC, a media-consulting firm in Los Angeles that provides valuations of film investments. When he asked my opinion of simulating the outcomes of portfolios of movies, I responded that it would be dereliction of duty if he didn't. So, based on the actual box office results of 28 "live action" films, I helped him create an interactive histogram [8], allowing him to scroll through the distributions of outcomes from a single film, a portfolio of two films, three films and so on. This is depicted in Figure 2 (click on the image to view a larger version in a seperate browser window) and is viewable as an animation online [9]. Selected frames are shown in Figure 2, with the one in the upper left displaying the historical distribution of revenues of the 28 live action films. Assuming that future movies of this genre are drawn independently from the same distribution, this would represent the distribution of outcomes of investing in a single film. Thanks to the central limit theorem, as one diversifies across additional films, the distribution approaches the normal. As a result, the chance of losing your shirt drops from above 20 percent with a single film to about 1 percent with four films. When I pointed this out to Rick, he said: "It takes some people decades to learn that." A few weeks after we had developed this simulation, Rick phoned me up. "It works with cartoons!" he reported excitedly. "What works?" I inquired. "When you simulate portfolios based on historical cartoon revenues instead of live action films, the outcomes become bell shaped again when you diversify," he explained. I grabbed my probability textbook off the shelf and looked it up. Sure enough, Rick was right; the central limit theorem does apply to cartoon revenues as well as live action films! Unlike most of my graduate students, Rick Medress now has an almost carnal knowledge of one the most profound results in mathematics. Briefly, it states that a non-linear function of a random variable, evaluated at the average of the random variable, is not the average of the function, or
"Functions of random variables," you say. "That's an exotic topic in advanced stochastic modeling." No. A spreadsheet model is just a function. And if you are uncertain of the inputs then it is function of random variables. Do the math. There are 40 million spreadsheet users of whom roughly 100 percent are uncertain about their inputs. In this light, the Flaw of Averages may be stated as follows: When you plug average values into a spreadsheet, you don't get average outputs unless the model is linear (and most people don't know if their models are linear or not). Less than 5 percent of my students seem to be aware of this crucial fact. Some examples,
Decision-Making Where it Counts What if we could help ordinary people make better decisions under the uncertainty of daily life? The Decision Education Foundation is a non-profit organization founded by Ron Howard of Stanford University, and other like-minded decision analysts, to do just that. Their goal is an ambitious program of WMI focused on teaching basic decision-making skills to high school students at a point in their lives when their decisions have the greatest impact on their future. This requires not only developing pedagogy for the students, but for their teachers as well. See www.decisioneducation.org for more details. The entire area of developing intuition into probabilistic models has just received a shot in the arm with Daniel Kahneman's Nobel Prize for showing how people (quoting from the academy) "take shortcuts that systematically depart from basic principles of probability." Could this be due to the way we teach the subject? Don't get me wrong, the classical theory of probability and statistics is powerful and elegant. But so is the steam locomotive, and they were developed around the same time. The difference is that although we no longer teach steam locomotion, we continue to teach steam-era statistics. Stanford's Bradley Efron, a pioneer in computational statistics and inventor of the statistical bootstrap, puts it this way: "As far as what we are teaching new students, statistics stopped dead in 1950." [11]. No wonder people don't know what a distribution is. This is an extreme example of a conflict of interest inherent in the teaching profession: by illuminating your subject, you increase the stature of your students; by obfuscating it, you increase your own apparent stature. If educators are worried that WMI could undermine their job security, can you blame them? Yes. They are holding our field back.
Sam Savage is consulting professor in management science and engineering at Stanford University, and president of AnalyCorp Inc., a firm that develops executive education programs and software for business analysis. Savage holds a Ph.D. in computer science from Yale, and has published a wide array of technical and non-technical works. His recent book, "Decision Making with Insight" from Duxbury Press has been called "a must read" by Harry Markowitz, Nobel Laureate in Economics. The book, with spreadsheet add-ins, attempts to explain OR/MS concepts by connecting the seat of the intellect to the seat of the pants. For more information see http://www.stanford.edu/dept/MSandE/faculty/savage/ or www.AnalyCorp.com. (c) Copyright Sam Savage 2003. OR/MS Today copyright © 2003 by the Institute for Operations Research and the Management Sciences. All rights reserved. Lionheart Publishing, Inc. 506 Roswell Rd., Suite 220, Marietta, GA 30060 USA Phone: 770-431-0867 | Fax: 770-432-6969 E-mail: lpi@lionhrtpub.com URL: http://www.lionhrtpub.com Web Site © Copyright 2003 by Lionheart Publishing, Inc. All rights reserved. | |||||||||||||||||||||||||||||||||