Complex Problem Solving in 3D

Strategic Frameworks

Complex Problem Solving in 3D

Whether we like it or not, complex problem solving is here to stay; and the ones who understand the difference between the simple and the complex…will lead.
By Jeff Dickson

Simple vs. the Complex

What are the simple, routine problems you solve everyday? If you are like most, you might be hard pressed to answer that question. If you are in leadership, you might secretly wish you had more of those “easy days”. Unfortunately, it is the non-routines and the complex problem solving that have become the rule, rather than the exception. The reason? There has been a rapid rise in non-routine work this past decade caused by globalization, fast moving technologies like AI, and low barriers to entry. In fact, Gartner Research predicts that one in five workers will be engaged in mostly non-routine tasks by 2022. Whether we like it or not, this means that complex problem solving is here to stay; and the ones who understand the difference between the simple and the complex…will lead.

The Problem with Problem Solving.

Yet, despite the rising demand for complex problem solving, we tend to use the same mindset for the complex as we do the more simple problems. Consider some of your most recent problem solving activities. Which part of the activity did you receive the most praise? The solution of course! Naturally, because the reward is in finding the solution, we adopt a one dimensional mindset of problem solving. However, to adopt the same mindset for complex problem solving would be a mistake. Unlike simple problems, complex problems are dynamic, offering both challenges and rewards in multiple areas. Said another way, complex problem solving is made up of several problems; and they rarely occur in any kind of orderly fashion. Certainly, a three-dimensional perspective is far more beneficial.

Gartner Research 2015-2022

Gartner Research 2015-2022

Problem Solving in 3D.

Although the literature is replete with exhaustive methods such as the Simplexity model by Min Basudur, we find this to be unnecessary. Methods like these may make for fun workshops, but they assume a simple problem solving context where there is a defined problem set, shared typicality among the solvers, and a linear approach that is not indicative of complex problems. Additionally, the complexity of these frameworks can diminish our creativity or ability to work within them efficiently. 

Instead, we suggest a sensemaking framework that helps us understand the three most valuable dimensions of problem solving and how they relate to one another. We call this framework “3D problem solving”—Detect the challenge, Diagnose the Problem, and Develop options. Notice something missing? What about the solution? Or, for fun, why isn’t there a fourth “D” called decision? Unlike simple problems, this particular framework does not have a single output stage, because each of these components can equally lead to different types of valuable outputs. For instance, the value of an early hurricane Detection from a weather agency is of extreme importance, yet the weather man may not solve the problem. Similarly, an accurate Diagnosis of a logistics problem by itself can eliminate unneeded trial and error, and subsequent higher costs, in other areas of the business. Finally, the Development of rapid options for an advertising campaign, allows for more evaluation, confident decisions, and better outcomes.

Gary Klein , Research Psychologist, Author

Gary Klein, Research Psychologist, Author

Dynamic Benefits.

This particular framework is based on the work of Gary Klein who explained that complex problem solving is rarely linear and involves simultaneous evaluations of the goal during the problem solving process. The same is true of this framework. Not only does each component offer its own inherent value, the goal itself can evolve as each dimension is evaluated. Additionally, this sensemaking framework promotes a constructivist approach, where the interrelationships between all three components work together to build a better solution. For instance, a diagnosis of your brand communications may detect a new challenge in your business model. The development of new options for solving this business model challenge may lead to a deeper diagnosis of the brand, and so forth. Ultimately, true to Gary Klein’s assertion, we may decide to focus on a new goal altogether! Surely, had our brand team attempted to ignore these insights in pursuit of delivering “better” communications, we would have overlooked an incredible amount of value! Yet, all too often we do. Instead, this framework promotes both the value of each component as well as the dynamic relationships of all three dimensions. Any point of the problem solving process can spur a dialogue with the other two. 

Complex Problem Solving,  ALLOY 2019

Complex Problem Solving, ALLOY 2019

This three dimensional understanding not only allows us to refine the goal throughout the problem solving process, but simultaneously strengthens multiple areas of value in judgement, decision making, collaboration, communication and, ultimately, learning. By abstracting complex problem solving into three dimensions of detectdiagnose, and develop, we can now capitalize on all of this value rather than the final result alone. 

How would you capture this value? What kind of strategies would you implement to improve the strategic thinking of your teams? For ourselves this ability to understand complex problems led to our most competitive advantage to date: An adaptive problem solving mindset we call Framework Thinking. Not ready for a new mindset just yet? Try some of the tactics listed below to improve each dimension of complex problem solving.

Tactics -

Detect the problem

Research: employing existing ideas or adapting existing solutions to similar problems

Morphological analysis: assessing the output and interactions of an entire system

Divide and conquer: breaking down a large, complex problem into smaller, solvable problems

Substitution: transforming the problem into another problem for which solutions exist

Diagnose the problem

Root cause analysis: identifying the cause of a problem

Means-ends analysis: choosing an action at each step to move closer to the goal

Proof: try to prove that the problem cannot be solved. The point where the proof fails will be the starting point for solving it


Develop options

Abstraction: solving the problem in a model of the system before applying it to the real system

Brainstorming: (especially among groups of people) suggesting a large number of solutions or ideas and combining and developing them until an optimum solution is found

Lateral thinking: approaching solutions indirectly and creatively

Analogy: using a solution that solves an analogous problem

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