Point Lookout: a free weekly publication of Chaco Canyon Consulting
Volume 21, Issue 23;   June 9, 2021: Self-Imposed Constraints

Self-Imposed Constraints

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When we solve problems, the problem definition and associated constraints determine the possible solutions. Sometimes, though, solving the problem is unnecessarily difficult because we accepted self-imposed constraints as real. How can we avoid that?
A meeting at a whiteboard

A meeting at a whiteboard. Exchanging views about a problem sometimes requires nonverbal methods.

When we're solving problems, we sometimes unintentionally impose constraints that make solving easy problems difficult and solving difficult problems impossible. Relaxing these constraints is rare because we so rarely recognize that the constraints are self-imposed. Sometimes we incorrectly regard these constraints as part of the original problem, and therefore not subject to alteration.

After changing these self-imposed constraints, problem solutions often become easier to find. Because the challenge is to recognize self-imposed constraints, it's helpful to have a handy checklist of the common forms they take. With checklist in hand we can inspect the problem definition and the associated solution constraints. When we find a constraint that's on this list, we can recognize it as a candidate for alteration or removal.

Here's a list of these patterns of self-imposed constraints. Maybe you can add more.

We can't revise the problem definition because it's complete and correct
In many cases the person or persons (the "problem-posers") who constructed the problem definition don't belong to the group charged with solving the problem (the "problem-solvers"). In these situations there's a tendency for the problem-solvers to accept the problem definition without questioning it. Sometimes this happens, in part, because the problem-posers outrank the problem-solvers politically, and the problem-solvers don't feel authorized to question the problem definition.
But until careful study begins, we can't be certain that the problem as posed actually has a solution. Or a solution might exist, but it isn't consistent with pertinent laws or regulations. Some problems have no solutions. Verify that what the problem-posers want is actually legal, that it conforms to the laws of Physics and Economics, and that it makes logical sense. You don't want to be engaged in a search for the cheapest way to turn marshmallows into gold.
That way isn't the best way
Suppose the problem-solvers have found an approach that seems to be workable. The approach has provided a path around all known obstacles. But someone has expressed a general feeling that there's a better way. This pattern can appear when solving problems of any scale, from World Hunger to the order of the items in a meeting agenda.
The odd We sometimes unintentionally impose
constraints that make easy problems
difficult and difficult problems impossible
thing is that for most problems, the group's charter has no language that commands, "Find the best solution." But we search for the best solution anyway. That can be wasteful, because good enough often is good enough. Complex problems rarely have a "best" solution. See "Problem Defining and Problem Solving," Point Lookout for August 3, 2005, for more.
We can't afford (or have no time for) experimentation
Some problems have solutions that become evident only after we try a series of candidate solutions, either in real life or in simulation. But trials — even simulated trials — require resources. When those who control those resources refuse to provide them, finding acceptable solutions can be more difficult. That's life. But when we decide not to request the resources necessary for trial solutions, or when we don't even think of experimenting with trial solutions, or when we fail to make clear the full costs of parsimony, we're imposing an unnecessary constraint on our own problem-solving process.
Try some experiments as a way of exploring possibilities. Start by making simplifying assumptions that might not be justifiable in the real problem. Be careful. Experiments have a way of morphing into prototypes that then become the solutions. This happens, in part, because people want to recover the costs of the experiments. Before constructing the real solution, terminate all experiments. Start the real solution from a clean sheet.
The solution to this problem will be much (cleaner, cheaper, …) if we do X first
When the problem at hand (I'll call it P) is to enhance or build upon existing assets, a common pattern involves attending to those assets (the X referred to above) before solving P. While doing so might be wise if the problem solution is already in hand, it's a dubious strategy if the solution to P is understood only in broad outlines. Actually solving P frequently produces new information that can alter views about exactly what X entails.
Undertaking X first is risky, because it defers P. There's a high bar to meet whenever we defer one problem to address another. That pattern can repeat recursively: as we defer P to handle X, we then find a third problem Y that causes us to propose deferring X until we deal with Y, and so on. If tackling X before solving P really is a good idea, then it's worth doing on its own merits. Consider putting X in the queue as backlog to be cleared after the work on P is completed. In other words, take on some technical debt (X) until the more immediate issue (P) is cleared. P might need some rework later, and that is a consideration, but do consider it.
Failed attempts are failed attempts; move on
When we try a candidate solution, and discover an insurmountable obstacle that renders it a failure, we can rule out that candidate. But there is usually value in understanding the details of the failure. Moving on without capturing that knowledge can be an expensive mistake. For example, a clear understanding of one failed candidate solution can be useful for adjusting other candidate solutions before studying them.
Design your solution attempts so that you can harvest value even from failure. If they succeed, fine. But if they fail, they should fail in ways that expand your knowledge and understanding of the problem. This means that you need to gather enough data along the way so that even if an approach fails, it helps you formulate new approaches, or suggests ways to modify other failed approaches.
Pictures don't lie: diagrams are complete and unbiased
Solving some problems entails constructing and interpreting graphic representations of concepts and relationships. When these representations are displayed on screens, paper, or whiteboards, they are inherently two-dimensional. That's fine for many situations, but if higher dimensionality is needed, these graphical representations can only be approximations, and approximations can be misleading. Sizes and placement of representations of elements of the problem can convey biases that affect how we understand the problem and how we formulate solutions.
The biases that arise from the inherent limitations of two-dimensional diagrams are outside our awareness. That's one reason why intentionally removing the biases can be so difficult. One approach that helps in some cases is redrawing the graphical representations in different ways, perhaps by different people. Scramble the diagrams. New insights might result.
Wait! There's more!
The problem-solvers found a solution to what the problem-posers asked for. But as they were implementing it, they realized something they could add (call it X) with what seemed to be just a bit more work. And that additional capability would surprise and delight everyone. The problem-posers weren't actually asking for X, but the problem-solvers were certain that they would be pleased.
If the additional work really is just a "bit," then of course, just do it. But that case is rare indeed. Almost always, the additional work is a budget buster.
Solving the more general case
The problem-solvers found a solution to what the problem-posers asked for. But as they were implementing it, they realized that their solution (call it X) was just a special case of a large class of problems for which they believe they have the general solution (call it X'). They believed that X' wasn't much more work than X, and that having X' in hand would surprise and delight everyone. The problem-posers weren't actually asking for X', but the problem-solvers were certain that they would be pleased.
This is another example of the actual implementation turning out to be far more work than it first seemed. Almost always, this is another budget buster.

Over time, solving many problems, you'll occasionally have "a-hah" moments when you suddenly realize how to solve the problem at hand. When that happens, ask yourself, "What was I assuming that prevented me from seeing this until now?" If you find an answer to that question, and it turns out to be a self-imposed constraint of a kind not included in this list, add it to your own list. And send me a note. I'd like to expand my collection. Go to top Top  Next issue: Organizational Roots of Toxic Conflict  Next Issue

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