How do continuous learning cultures facilitate complex problem-solving?
“Every complex problem has a simple solution that doesn’t work.”
– H.L. Mencken
Some time ago, I was facilitating a group session using an approach to team coaching called Action Learning, which is uniquely designed to help develop leadership skills through the process of collectively solving complex problems. In this case, the “problem presenter” shared a challenge with the group that went something like, “our sales for Product Y took a sudden dip last quarter after growing for two consecutive years.”
The first rule in Action Learning is no statements except in response to a question. The initial task is to ask questions from a diversity of perspectives about the problem until there is consensus about what the real problem actually is. In this case, for example, the dip in sales was not the real problem, but rather a symptom of a “deeper” issue which often involves multiple factors. This is the nature of complexity: the real problem is hidden from sight as we perceive only it’s symptoms.
It is through asking the right questions that we are able to organize our attention and energy properly to work toward a solution that will actually solve the real problem. When we fail to do so, we waste this energy working on the wrong problem and in many cases, only make the problem worse or create negative unintended consequences.
Action Learning has proven to be an effective tool because it recognizes an important principle of complex problem-solving: we rarely, if ever, come to an understanding of the “real” problem alone as individuals. It takes the collective intelligence of multiple perspectives to achieve this clarity, which enables us to effectively orient ourselves and organize action. At the heart of the process is the way in which we ask each other questions.
There is, of course, a good reason why this is more important now than ever before. We are facing more complex challenges on every level—a result of living in a world that is changing at an increasingly rapid rate. It represents an emerging set of skills and practices that must be top priorities for leaders and organizations who plan to survive in a transforming environment.
This cues up our 7th principle of Leading Continuous Learning:
Principle #7
Solving complex problems is a process of continuous learning, driven by the dynamic balance of action and reflection.
In order to better understand the nature of complex problems and how they differ from other types of problems, we’ll turn to Dave Snowden, complexity scientist and creator of the Cynefin (koo-neh-vin) Framework. Snowden outlines 4 categories of problems: clear, complicated, complex, and chaotic.
Clear problems are those where the root cause of the problem is obvious and which can be solved with a best practice. The process involves recalling (from memory or elsewhere) the best practice and implementing it. Best practices for recurring Clear problems can be collected and organized for easy access at point-of-need, and/or transferred into long term memory through formal training.
Complicated problems are those where the root cause of the problem is discovered through expert analysis, resulting in identifying a good practice. A “good” practice differs from a “best” practice in that it is more context-specific—there are multiple existing solutions and the task is to discover the right fit given the current situation. The process involves analyzing the problem, then identifying and assessing potential solutions before making a decision about an appropriate course of action.
The Clear and Complicated domains are what Snowden refers to as “ordered” systems. He writes, “order is constrained and future outcomes are predictable as long as the constraints can be sustained.” In other words, decision-making processes around such problems can be effectively governed by documented rules and standards on procedures. The causes and solutions are either known (Clear) or knowable (Complicated).
Complex problems, on the other hand, are categorically different than those of “ordered” systems. They cannot be solved with the same rigid governing or fixed constraints that effectively solve Complicated and Clear problems.
With Complex problems, the root causes are neither known nor knowable (through the analysis of existing data). Rather, they are unknown. They are discovered through a process that begins with what Snowden calls “probing.” As with the Action Learning approach, probing centers around asking the right questions and collecting multiple perspectives to get to the “real” problem, or the root cause of the perceived problem (the symptom).
In contrast to the governing/fixed constraints that guide decision-making in “ordered” systems, Complex challenges call for enabling constraints. Whereas governing constraints consist of SOPs, rules, laws, and set processes, enabling constraints consist of principles and heuristics designed to empower individuals to make context-specific decisions that are not effectively directed from the top down. Solutions in the Complex domain emerge through this process.
Finally, problems in the Chaotic domain require immediate action despite their root causes being unknowable. These problems require novel solutions and the task is to stabilize the system and move it towards Complex where it can be effectively managed.
An important insight is that we often end up in the Chaotic domain by way of approaching Complicated and/or Complex problems with “best practices” suited for Clear problems. As Snowden writes in Cynefin: Weaving Sense-Making into the Fabric of Our World:
“The boundary between Clear and Chaotic is a cliff or catastrophic fold. It is easy to walk blindly off this cliff through excessive confidence in the context-free applicability of rigid constraints.”
As we face more and more complexity in our work together, we will need to learn how to effectively discern between these domains and develop the ability to manage our teams with the appropriate types of constraints, depending on the nature of the challenge at hand. In doing so, we can avoid falling off the “cliff” into Chaos.
Enabling effective problem-solving
A team properly equipped to thrive in a fast-paced and rapidly changing environment must possess the capacity to quickly and effectively sense problems as they emerge and employ appropriate strategies for solving them. Bottlenecks created by a lack of this capacity threaten an enterprise’s ability to survive in such circumstances.
We can contextualize the problem-solving process through the lens of the Continuous Learning Spiral. Continuous learning itself can be framed as a process of continuous problem-solving, though we might emphasize that this doesn’t mean it is inherently reactive—“problems” can also be reframed as opportunities informed by sensed creative tensions that inspire us to imagine possibilities and potential.
A useful way to think about this cycle is as the dynamic interplay between action and reflection. In this case, the Core Competencies of Designing Experiments and Testing Ideas are “active,” while Exploring Possibilities and Analyzing Opportunities are “reflective.”
As we explored in more depth here, these four Core Competencies that make up the Continuous Learning Spiral consist of specific skills that we should be focused on developing within both individuals and teams. We’ll explore some of these as they relate specifically to solving Clear, Complicated, Complex, and Chaotic problems.
Sensing tensions and opportunities
First we’ll look at the capacity to sense creative tension in everyday experience. This happens naturally, of course, but often haphazardly and opportunities are missed. When individuals and teams are supported to develop this capacity, tensions can be more systematically surfaced and processed.
This can be done by supporting both individuals and teams to establish a Reflective Practice. In his book The Reflective Practitioner, Donald Schon distinguishes two types of this practice: reflection-in-action and reflection-on-action. He writes:
“Many practitioners, locked into a view of themselves as technical experts, find nothing in the world of practice to occasion reflection. They have become too skillful at techniques of selective inattention, junk categories, and situational control, techniques which they use to preserve the constancy of their knowledge-in-practice. For them, uncertainty is a threat; its admission is a sign of weakness. Others, more inclined toward and adept at reflection-in-action, nevertheless feel profoundly uneasy because they cannot say what they know how to do, cannot justify its quality or rigor.”
A high-leverage strategy for breaking this threatening pattern is facilitating reflection on the pattern itself: how are we relating to what we know (or think we know) as we go about our work? How are we relating to the uncertainty we are facing as things change?
As leaders, we must also address the cultural values or practices that may reinforce such patterns, which can be explored together. In which ways are we allowing uncertainty or an admission of uncertainty to be perceived by others as a “sign of weakness?” How can we change this perception so that together we recognize it for what it is: a sign of strength, courage and humility? How can we incentivize honesty and transparency as we come up against the limits of our technical knowledge in an environment that calls for new ways of thinking and acting?
Facilitating conversations around such questions begins to transform culture and collective mindset. An awareness around the nature of the challenge of collaboratively embracing a new world emerges. Individuals can be supported to understand that success in this new world is less about being an all-knowing expert and more about being a Continuous Learner.
Establishing individual- and team-level reflective practices
Regular reflective practices can be established on multiple levels to facilitate collective sense-making and problem solving. Here we’ll explore the individual and team levels through the lens of reflection-in-action and reflection-on-action.
Individual reflection-in-action: staying aware of and identify appropriate application of technical knowledge and established technique in the flow of work, including their limits as one comes up against uncertainty. This may include capturing emergent questions or insights for future processing. It may also include quick cycles around the Continuous Learning Spiral where opportunities are analyzed and rapid experiments are run in real time. A good line of questioning might be: what is the skillful practice here? How do I know, or how sure am I? If I’m not sure, how do I best find out?
Individual reflection-on-action: creating dedicated time and space for reviewing experience and surfacing creative tensions as opportunities. Ideally, this becomes a formal daily habit of documenting observations and processing tensions to facilitate better decision-making and action-taking.
A critical factor to sustaining individual practices is to support them with a team-level practice:
Team reflection-in-action: asking questions in the flow of work that trigger reflection on the limits of current knowledge and “best practice.” Such cues may be designed into the environment or into work processes. For example, during a meeting where an important decision is to be made, there is a formal set of questions baked into the process, such as: how certain are we that this is the real problem and not the symptom of a problem we don’t fully understand? How is this situation unique from past situations? How certain are we this is an appropriate solution? What are the potential unintended consequences? How can we mitigate risk by running a rapid experiment to validate or invalidate our hypothesis?
Team reflection-on-action: creating dedicated time and space for collectively reviewing experience and surfacing creative tensions as opportunities. For example, this may take the form of something like an After-Action-Report (AAR) or “Retrospective” meetings where the work experience is formally reflected upon to generate insight and clarity on next steps. This may also show up as a short daily briefing or check-in where ideas and observations are captured and processed.
From exploration to analysis: identifying the right course of action
The reflective process begins with sensing tensions and opportunities and then transitions into processing those tensions through analysis. Here we’ll circle back to Snowden’s Cynefin framework to provide some insight into this process.
There is a line of questioning that will orient an individual and/or a team to select the appropriate strategy for any given problem. The first and most important question is, what type of problem is this?
Is it clear what the cause of the problem is? Is this problem identical to what others have faced in the past? If so, it’s a Clear problem: there is a best practice to be identified or recalled and followed step-by-step.
Is the cause of the problem unclear but knowable through analysis of existing data? Is the problem a technical “puzzle,” similar to that which others have faced in the past, where there is very likely an existing solution out there somewhere that will work to solve it? If so, it’s a Complicated problem: there is an existing good practice to be identified through a process of deeper analysis and expert guidance, then implemented.
Is the problem a symptom of a deeper issue that we don’t fully understand? Is it influenced by a number of factors? Is it unique to our particular situation? If so, it’s likely a Complex problem: solving it begins with working to understand the “real” problem by probing multiple perspectives and uncovering the root causes, to the point that a hypothesis can be formed and a “good enough for now, safe enough to try” experiment can be designed and run to test it.
Is the nature of the problem a mystery and it represents an urgent existential threat? If so, it’s a Chaotic problem: the task is to act quickly and in the best way you know how, with the aim of stabilizing the situation into the Complex domain where there is space to process and understand it.
Developing systems to support the decision-making process
As learning leaders, we can look to create systems that both support individuals and individuals to properly identify the type of problem they are facing, as well as implement the appropriate strategy to solve it.
First, how can we support individuals and teams to identify which “domain” the problem they have sensed belongs to? Asking questions similar to those provided above is a good start. Where and how to do so will vary in different contexts, but we’ll share some thoughts here for consideration.
For example, such questions can be embedded into reflective practices on both the individual and team levels. Individuals might be formally supported to establish their practices with an initial workshop or blended training program. This could include providing templates for regular reflection that include specific lines of questioning to help them sense tensions and opportunities, as well as identify appropriate strategies based on the nature of the issue.
Individual reflective practices can feed team practices, where sensed tensions and opportunities are communicated and processed collaboratively. The more complex the issue, the more fundamental is collaboration to solving it. Here space can be created during regular meetings, and/or dedicated digital channels, where individuals can share and initiate dialogue.
A good strategy is to have systems in place so that Clear problems can be solved by the individual, without eating up bandwidth in meetings or group spaces better dedicated to Complicated and Complex issues. For example, one strategy related to Knowledge Management (KM), which we explored in more depth here, involves creating Communities of Practice (CoPs) which curate resources such as current best practices around a specific domain. When an individual encounters a relatively common challenge, they have easy access to a library of best practices they can leverage to get the job done.
For Complicated problems, organizations can create systems to provide the “governing constraints” that effectively guide the process of analyzing the problem and identifying appropriate solutions. Such systems should focus on connecting individuals within knowledge networks where personalized support can be facilitated between experts or mentors and solution-seekers. Some may be internal to the organization, whereas others may transcend organizational boundaries into broader networks where expertise on specific technologies (physical or social) can be accessed to help identify the existing “good practice” that is a best-fit for the puzzle-like challenge at hand.
As discussed, systems for solving Complex problems are built around heuristics or principles rather than fixed or governing constrains. As in the Action Learning example we began with, the goal of such systems is to facilitate dialogue focused on exploring multiple perspectives to achieve an understanding of the “real” problem. Even so, such understanding and the actions they inform are best framed as hypotheses in the Complex domain. The goal is to design and run an experiment to test the hypothesis and often, it is only through running and reflecting on multiple experiments does clarity around the real problem emerge.
As leaders we can look to facilitate the process of exploring perspectives, creating hypotheses, and systematically testing our assumptions as we learn into the emergent solutions that Complex problems call for. Examples of such systems are Agile, SCRUM, and Lean, which can be adopted or adapted for our unique cultures and situations. All represent a shift toward ways of working better suited for complexity and uncertainty, becoming increasingly popular as the old ways, which were designed for a world of primarily Clear and Complicated problems, reach their functional limits.
On a high level, all such systems for working in the Complex domain follow a similar pattern, which maps to the Continuous Learning Spiral. We could say that the more complex a problem, the quicker and more frequently we need to iterate around the cycle to gain clarity and insight into the real problem and develop an appropriate solution.
Arguably, Chaotic problems are avoided with the capacity to identify and process issues appropriately to the domain they belong. Recall Snowden’s insight that a common cause of Chaos is falling off the “cliff” of the Clear domain—in other words, oversimplifying Complicated or Complex problems by attempting to apply one-size-fits-all solutions or best practices.
Of course, Chaos may also be imposed from the environment in which case we are challenged to act to the best of our current abilities and instincts. The goal is to stabilize the situation into the Complex domain where perspectives can be explored and experiments can be designed and run to facilitate sense-making. When we have systems for complexity in place, we can respond more skillfully in chaotic situations. When we don’t, we may in our haste implement oversimplified solutions to the wrong problem and only exacerbate the Chaos.
Not all problems are Complex, but all are embedded in complexity
A best practice well-suited for a task in the Clear domain may no longer be valid a month from now. A new technology we implemented last year, which effectively solved a Complicated challenge then, may no longer be the right fit. Such is the nature of operating in the modern world.
All organizations are complex adaptive systems, which means they have emergent properties that cannot be fully predicted and therefore cannot be fully controlled. In other words, as an organization we are always facing uncertainty. On a high level, an organization itself is a Complex challenge requiring ongoing experimentation, testing, and reflection to survive. This has always been true, but the faster things change in the environment, the faster and more continuous the cycles around the learning spiral must be.
For this reason, the modern organization must be designed to continuously reflect on all current practices and ask: is this still the right solution for this problem? Has the problem changed? Are there newer and better solutions?
A balance must be considered, of course, as there are costs to switching out solutions. Yet failing to systematically reflect in this way can lead to implementing outdated solutions—or oversimplified solutions to problems that have become more Complicated/Complex—which can lead us toward the cliff over Chaos.
Summary
Not all problems are created equal—different types problems call for different approaches. The right approach has much to do with the level of complexity we are facing. Dave Snowden’s Cynefin (koo-neh-vin) framework is a useful tool for guiding our thinking and action in solving different type of problems. Snowden presents four domains, each with a unique approach: Clear, Complicated, Complex, and Chaotic.
In today’s rapidly changing world, we are facing more Complex and Chaotic challenges than ever before. Chaos can be largely avoided by learning how to better manage complexity.
Complex challenges call for new ways of problem-solving, which are rooted in exploring multiple perspectives through open dialogue, as well as iteratively designing and running experiments to facilitate collective sense-making. This pattern aligns with the Continuous Learning Spiral.
We typically observe the symptoms of Complex problems first. The problem-solving process in this domain begins with “probing” to understand the “real” problem. Jumping to solutions that focus on the symptoms of the real problem often produce unintended consequences that may only make the situation worse.
Increasingly popular systems and ways of working such as Agile, SCRUM, and Lean are designed to facilitate this process and may serve to inform appropriate strategies for our organizations.
Complicated problems can be thought of as technical puzzles. The problem-solving process involves sensing the problem then analyzing it to identify the right existing solution, or “good practice.” This process often involves leveraging expert knowledge to choose the right-fit solution.
Many traditional ways of working, heavily centered around analysis, are well-suited for solving Complicated problems. Modern networking technology can also be leveraged to better connect solution-seekers to the expert knowledge needed.
Clear problems are those for which there is an obvious best practice. Best practices can be collected and accessed at the point of need. Formal training can also be effective, aimed at supporting individuals to skillfully access resources as well as transfer best practices into long-term memory. As things move faster and attention is increasingly limited, empowering individuals to proactively solve Clear problems on their own represents low-hanging fruit for many organizations.
As the context around Complicated and Clear problems is shifting faster than ever, their existing solutions become obsolete faster than ever. Organizations must effectively facilitate the process of periodically reflecting on these changes and new opportunities, or risk sliding toward collapse into Chaos—a result of applying oversimplified solutions to more complex problems that are not well understood.
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