While a student at the University of California at Berkeley, George Dantzig once arrived very late for his statistics class. The professor was gone, and so were the other students. Dantzig mistakenly assumed that the problems written on the board were the class homework assignment — so he copied them down, worked on them at home and solved them.
His “mistake” became the stuff of legend when he turned in the correct solutions to his professor, who, as it turned out, had shared them with the class because they had been famous unsolved problems in statistics.
Legends have a way of making their way to Hollywood, of course, and Dantzig’s story provided the inspiration, although not the plot, for the movie, “Good Will Hunting.”
Dantzig developed the Simplex Method in 1947 as a way to determine the best solution to problems where there were multiple constraints. To figure out the best cattle feed mix, for example, there would be a cost constraint, some other constraints involving nutritional elements, and yet another constraint involving the overall taste of the ingredients. (It doesn’t much matter how mathematically elegant the solution is if the cows won’t eat it.)
Not all real world problems fit into the linear programming layout that Dantzig’s method was designed to solve. But there are two characteristics of the Simplex Method that apply to a lot of economic problems, up to and including health care.
The first characteristic comes from its being what mathematicians call an “iterative process,” that is, you have to repeat it several times to get to the right answer. There is, in fact, no direct solution, just a series of increasingly less imperfect, better solutions. The second characteristic is built into the math of the Simplex Method: Each repeating process gets you closer to the best solution.
We don’t have to use big league mathematics of any sort to take advantage of these characteristics when we are trying to solve economics or business problems. The important stuff is embedded in the logic of the iterative process: We know that we aren’t going to get it perfectly right the first time but we set it up so that things get better each time we do it.
In economics and in business, we are often faced with problems so complex that we can’t see our way to a total solution. And more than a few companies, small as well as large, have crashed because they insisted on a total solution that turned out either to be totally wrong or destructively inadequate in some other way.
The way to use the Simplex Method approach to our advantage is to break up the huge problem into its key components. We might not be able to solve the huge problem in its totality, but we can probably solve some of its component problems by launching repeated solutions, each one improving on the one before.
The short, high-profile history of our effort at health care reform suggests that it is a prime candidate for this approach. It is a big, complex problem and it may be helpful for us to realize that we may not be able to produce a comprehensive solution to the thing, at least in its current form.
There are several major components of the health care problem that are very closely related but do not necessarily lend themselves to the same solution. It seems very possible, though, that each might have a solution and we would make more progress by addressing them one at a time rather than confronting the whole.
One component problem, for example, is health care for those who do not have medical insurance. This is a complicated problem in its own right, and does not become less so by throwing it in with all the other issues. It isn’t easy to solve, but considered in isolation it is likely that the Congress, with the advice and consent of its constituents, could come up with a workable solution.
Another component problem stems from the significant variations in cost from region to region, state to state, and community to community across the United States. The role of fifty separate, regulated insurance fiefdoms in this cannot be ignored, but there are undoubtedly other factors which would emerge if we gave this issue our full attention.
Other problems, even the controversial ones such as end-of-life care issue and tort reform, also lend themselves to component-based problem solving. The alternative, all-or-nothing approach lends itself to the bulk packaging of legislation which few have read and even fewer understand, a process that looks a lot more like problem creation than problem solving. It also violates a fundamental rule of organizational effectiveness: If it doesn’t work, stop doing it.
James McCusker is a Bothell economist, educator and consultant. He also writes a monthly column for the Snohomish County Business Journal.
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