Based on Research By Arnaud Chevallier

Solving Problems By Exploring The Far End Of Possibility

  • When a company’s problem is complex or potentially costly, mapping it out is critical.
  • “Idea mapping” is a method of organizing the answers to a key question in a mutually exclusive, collectively exhaustive and insightful way.
  • Mapping makes space for innovative analysis.

Let’s say you’re in Houston, but you have a meeting in Paris. You could go by air–maybe first class, maybe business. Perhaps it’s smarter to take an ocean liner and then a little boat up the Seine. Should you build a rocket or walk? Maybe Paris should simply remove itself from France and appear on your doorstep.

This exercise is plainly absurd. Yet there’s good reason to dig for all possibilities when solving a problem, argues Arnaud Chevallier, a Rice University engineering professor, in his book Strategic Thinking in Complex Problem Solving. Analyzing extensive research on idea creation and decision trees, Chevallier argues that a technique called “mapping” forces decision makers to think differently and push the horizon of all that could be possible.

Mapping is worthwhile, Chevallier says, because the obvious solution isn’t always the best one. For a hotel manager fielding constant complaints about a slow elevator, replacing it with a faster one would certainly fix things. But as a solution, it’s expensive. The second most obvious plan, redoing the electronic dispatch algorithm, may be too costly as well. Casting the net wider for ideas can yield something altogether different: in this case, giving hotel guests something to do while they wait by installing TVs and mirrors inside the elevator and outside its doors.

For complex problems, or problems where making the wrong choice can be costly, mapping helps free decision makers from the obvious and allows them to find solutions with added value.

The first rule of mapping, therefore, is to include the obvious. Next, go beyond it. This creates space for innovation, forcing the thinker first to identify divergent explanations, and only then evaluate whether they are worthy alternatives. Cultivating this analytical style, Chevallier writes, can be a powerful tool for finding innovative solutions to key performance issues.

This is because problems have a structure. And while there are any number of mechanisms one can use to map an issue and represent it visually, what’s important is that the representation exposes the problem’s underlying structure. In other words, an image makes clear a problem’s various dimensions, which allows decision makers to better understand it.

To start an issue-mapping journey, decision makers need to consistently ask and answer one of two types of questions: diagnostic (“why?”) or solution-based (“how?”). If the question is diagnostic, the map will only show possible causes. If the question is solution-based, the map’s branches and byways will depict options for “how.”

A map should place these answers in mutually exclusive branches. Two events are mutually exclusive when the occurrence of one precludes the occurrence of another. Using mutually exclusive branches ensures that each solution gets considered just once—there are no overlaps. At the same time, maps should be collectively exhaustive. That is, to the extent possible they should include all outcomes. This forces decision makers to ask, “What other possible reasons could there be for the problem at hand?” That way, each map considers all possible answers exactly once.

Using these two methods forces decision makers to travel beyond cliché and conventional wisdom. But the only way to reach this point, Chevallier says, is to acknowledge each idea in its entirety–yes, even those that prompt laughter. Uncomfortable as it may be to draw, a complete map includes apparently dumb ideas.

By focusing on both mutual exclusivity and collective exhaustion, decision makers can avoid a few notorious pitfalls. First, the process discourages fixating on one particular explanation. And second, it reduces confirmation bias, the pernicious human habit of gathering just the evidence that favors our pre-existing beliefs.

Issue-mapping also helps decision makers examine a problem in detail. Take, for example, an information technology company trying to learn why it can’t turn a profit. While it’s easy to explain the problem with a standard breakdown between costs and revenues, drilling deeper can reveal the real issues.

“Why are the volume of sales too low?” Maybe clients are switching to competitors. But why? Some may do so because what the company offers is inferior. Alternatively, the offering may not be inferior, but clients nonetheless believe that it is. But why is it inferior, or perceived to be so? Is it because of one of the classic four Ps of marketing–price, promotion, place, product itself? If so, each of these issues could be broken down further. Studying problems with this level of relentlessness can unveil critical information that otherwise may be overlooked.

When a problem is complicated, of course, the danger is that mapping could go on and on. How to know when to stop? Surprisingly, Chevallier warns against immediately defaulting to a tactics such as time limits. When it comes to problem solving, he argues, artificial barriers can build artificial dead ends.

Ideally, Chevallier says, issue-mapping should continue as long as the new details discovered are valuable. The trick, of course, is to reach the far shores of possibility without falling into the claws of paralysis–the dragon that lurks where the map finally gives out.

Arnaud Chevallier is an associate vice provost and an instructor of strategic thinking in the George R. Brown School of Engineering at Rice University

To learn more, please see: Chevallier, A. (2016). Strategic Thinking In Complex Problem Solving, Oxford University Press.