I've read a couple of books and watched several online lectures by the University of Michigan Ross School of Business professor, Scott Page. He uses a particularly useful metaphor (in my opinion) to describe the different kinds of problems that leaders may face. The first kind of problem, and perhaps the most easiest to solve, is what he calls a Mount Fuji problem. Take a look at a picture of Mount Fuji on the Internet (see here). Mount Fuji is kind of like J.R.R. Tolkien's fictional Lonely Mountain (from his book, The Hobbit) - sorry, I couldn't resist the reference - in that it seemingly appears out of nowhere. It is the single highest peak in all of Japan with an altitude of just over 3,776 meters, and it is just over 100 km outside of Tokyo and easily visible from there on a clear day. Consider a problem as a landscape, with the highest peak representing the best, or optimal solution, to that problem. A Mount Fuji problem, with a single, solitary peak, is the most straightforward problem to solve.
Let's use an example to further explain. Page refers to a theory known as scientific management (first described by Frederick Taylor in the late 1800's and early 1900's (scientific management is also known as Taylorism - for a more in-depth explanation, please see my previous post A response to "Medical Taylorism"). In one of Taylor's classic problems, he was asked to find the optimal weight of a shovel. At one extreme, you can use a stick, which is not very useful. The amount of material you can pick up and move with a stick is very small (almost zero). As you increase the size of the shovel, you can increase the amount of material that can be picked up and moved. However, at some point, the shovel becomes heavier to lift, and the amount of material that can be picked up and moved actually decreases. Graph this out on a chart (the x-axis is the size of the shovel and the y-axis is the amount of material that can be lifted and moved) and whola - you will see an almost perfect rendition of Mount Fuji. To use Page's metaphor, Mount Fuji problems can be solved with the principles of scientific management. Observe. Measure. Improve. Repeat.
Unfortunately, not all problems are Mount Fuji problems - in fact, very few problems these days are Mount Fuji problems! And most mountains aren't like Tolkien's Lonely Mountain, they are part of a mountain range. So now we move to Page's Rugged Landscape problem. Think of the Rocky Mountains or Appalachian Mountains. Here you have several peaks and valleys, so finding the highest peak (which is analagous to the most optimal solution to the problem) is much more difficult. Finding the solution to these kinds of problems often require a higher level of mathematical analysis (using my own analogy, if Mount Fuji problems can be described using a simple linear equation or binomial equation, Rugged Landscape problems often require polynomial equations with a much larger number of variables).
What makes a landscape "rugged"? Usually these kinds of problems involve multiple variables. I'm thinking right now of one of my business school classes on operations management (operations engineering) - think of your classic optimization problem (see this YouTube video for a really good explanation of this kind of problem). Many of these problems involve literally hundreds of variables and complex mathematical formulas (think multiple regression on steroids), simulations (e.g. Monte Carlo simulations), and /or methods such as linear programming. You don't have to know anything about these techniques and tools, but just realize that these kinds of problems can be solved for the best possible solution.
Unfortunately, even with all of these techniques to solve Rugged Landscape problems, we are left with the simple fact that most problems are what Page refers to as Dancing Landscape problems. Here we have a rugged landscape (again, think of the Rocky Mountains with multiple peaks and valleys) that changes from moment to moment - it literally dances! We can use the tools and techniques of operations engineering to find the optimal solution to a problem, even one as complex as finding the right flight schedule that maximizes profits for an airline company (there are numerous examples and published articles on this topic available online, but I particularly like this 1990 article from the Chicago Tribune). The important point that these optimization problems tend to neglect, however, is that there are other airlines that are trying to maximize their profits at the same time! In other words, if Delta starts a new flight route from Chicago to San Francisco, United may do the same thing to compete with Delta! The choices one company makes to maximize profits may cause (and frequently does) its competitors to act to maximize their own profits. Dancing Landscapes add a whole new dimension to the Rugged Landscape problems, and as a result, they are almost impossible to find the best optimal solution.
There are other analogies (which I will discuss in a future post) on the kinds of problems that leaders face, but I particularly like this one by Scott Page. His area of expertise is in something known as complex adaptive systems, which is a fascinating area that encompasses Chaos theory, Systems theory, Complexity theory, and Network science, among many others. If you are interested in just an introduction to this fascinating area of science, particularly as it relates to leadership and management, there are a number of free online courses provided by the Sante Fe Institute. What is clear after this discussion (at least I hope it's clear), is that we live in a world today where simple decisions for leaders are no longer the norm. It would seem appropriate for leaders to learn more about how we can make better decisions in a world full of complex adaptive systems. And if you don't like solving these kinds of problems, it's probably best to get out of leadership!
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