“Two roads diverged in a wood, and I — I took the one less traveled by, And that has made all the difference.” — Robert Frost

We tend to focus on the present moment. Most PhDs are awarded for studying a single weed in a field and analyzing it in great detail.

Good systems models, on the other hand, take a broader perspective.

They look at the big picture, analyzing long-term patterns and trends to find ways to improve things over time.

Most mental models are confined to a narrow window of time and space. We usually focus on a few days or weeks, while the system operates over years. Similarly, we usually concentrate on our department or division, but to effectively manage the system, we need to consider multiple organizations, agencies, groups, and stakeholders.

HOW DEEP (DETAILED)?

You may have heard that adaptive leaders should step back from the action and take a look at the big picture. It can feel like we’re stuck in the weeds, and it’s hard to see the shared values and beliefs that guide our interactions with others. Instead of focusing on individual differences, it’s helpful to apply a broader context (FOREST Thinking) — see how forces and conditions in the ecosystem shape our actions. Public health professionals deal with large numbers of people, while marketing departments manage diverse customer groups.

HOW TO BUILD GOOD SYSTEMS MODELS

Good systems models are in the upper right. They see broadly to understand the forces across the full ecosystem responsible for the performance issues you want to improve. The goal is to generate good systems models. The challenge? How to get there!

There are two roads we could take to reach the destination of a useful systems model.

Road 1: Representing the System

Road 1 is the most popular path. It tries to make a complete map of everything from the start. Teams of experts with specific knowledge often try to put all their knowledge together into a detailed picture. But this can make the map too complicated — like the Afghanistan Counterinsurgency map — with too much detail. This pursuit of a complete system map puts us on the Low Road (Road 1).

The Low Road starts with myopic perspectives and ideas and tries to put them together into a coherent whole. Since we have so much detail and the task is so big, this process only makes things more complicated. If we’re lucky, the mapping project ends up with a map as complex as the counterinsurgency map — a tangled web of information. More likely, the mapping effort stops, leaving us lacking comprehension and insight.

The problem is trying to map the whole system instead of just understanding the cause of the behavior (performance measure) we want to improve. Remember the slinky. If our performance goal is to significantly reduce the Slinky’s oscillation, we only need to focus on the physics of the spring. We can leave out colors and shapes — in fact, we must exclude them to reduce complexity. For usability, we must eliminate parts of the full system that don’t contribute to the performance we want to improve.

Road 2: Get Up Out of the Weeds

Road 2 is the wisest path. It is the High Road path.1

The High Road starts by setting the big picture vision for system performance. Then, you can decide what you want to see improve. Next, you can build a simple causal map, pick and change a system archetype, or make a simulation model. These tools help you understand what causes that behavior.
The High Road process involves sharing the initial “starter map” or “starter model” with others. Does it provide a clear explanation of why the performance issue is occurring? What changes or additions are needed? Make small adjustments and share the updated model for testing.

Keep adding more details a little at a time. This is the gradual descent in the diagram labeled “and add breadth and a bit more detail — slowly”.

Apply OPERATIONAL Thinking when adding more elements. Answer the question: “Does this contribute to the physics of the challenge — does it explain how the performance is generated?”

When you can finally answer the question “Does this describe the essential drivers of the performance issue?” with “Yes!”, it’s time to stop mapping (or whatever SysQ analysis process you’re applying.

Then use the map or analysis to start exploring ways to improve performance. You can look for solutions to close the future performance gap — you can find interventions to achieve your vision.

The concept of two roads, and especially the High Road, was first developed by Barry Richmond while teaching system dynamics to his undergraduate class. The concept was so memorable that one of his students years later asked me if I had a Powerpoint slide with the roads on it.

“Nothing is as practical as a good theory” — Kurt Lewin

We’ve talked about how the SysQ Mindset (AIM) helps us deal with complexity instead of just making things simpler, and how the SysQ Thinking Skill Set (APTITUDE) helps us figure out how our challenges are connected. Now, we’ll show you how to use these skills to put them into action in a three-step process: Sensemaking, Solving, and Implementing & Learning. These are the things we need to do to move from a problem to a solution in a way that helps us learn the most.

This process isn't just another framework - when infused with the SysQ Mindset and Thinking Skills, it becomes a powerful, yet practical, approach helping us find genuine leverage points in complex systems while avoiding the common pitfalls of rushed solutions and unintended consequences.

This process isn’t just another way to do things — when we use the SysQ Mindset and Thinking Skills, it becomes a powerful and practical way to find real opportunities in complex systems. We can avoid the common mistakes of rushing to solutions and making things worse.

Think of this process as the guide that shows us how to use the SysQ AIM and APTITUDE. While the steps seem simple, they challenge our natural tendency to jump to solutions before we really understand the problem. By following this process, we can move beyond just fixing things to make real, lasting changes in our organizations and communities. This process helps us answer three important questions: Are we working on the right problems? Do we really understand what’s driving them? And are we creating solutions that work?hoosing solutions that will create sustainable improvement rather than temporary relief?

The SysQ Process has three steps that are like a loop. These steps are called phases in the “finding leverage” process:

1. Sensemaking

2. Solving

3. Implementing & Learning

A. SENSEMAKING

To truly improve a situation, we need to understand the underlying forces driving the problem. Often, we jump straight into solutions without fully grasping the root cause. A common mistake is assuming we know what’s causing a problem and moving on to the next step.

The real issue with ineffective strategies is often that we choose the wrong problems to work on. We’ve poorly framed the problem, and that’s why our efforts aren’t making a difference.

Let’s say I want to improve my child’s grades. I might think the problem is their focus and persistence in studying. So, I’ll give them money as a reward. This might work in the short term, but the real problem is likely their intrinsic motivation. And the cause of that might be low self-worth or self-efficacy. If that’s the case, then the reward solution was the wrong approach for the right problem.

This is like building a great ladder and then leaning it against the wrong wall. To make sense and build a successful strategy, we first need to choose the right performance issue to improve. We need to pick the right wall.

Even if we take the time to frame the right issues, we still often just jump to solution mode. We start building our favorite ladder without thinking about the bigger picture.

In the example of my child’s grades, we might react by giving them words of encouragement. You’re smart. You can do it. But then, our child might reject those words and even argue with us. We might get frustrated, and the wall might include a fixed or stuck mindset: I’m bad at math.

To climb the right wall, we need to identify the fixed mindset. Instead of labeling our child as “good at math,” let’s focus on learning. “Yes, you might not be as good at math as you’d like, but you can learn. Even though you still got a C on the latest test, it’s several points higher than the last.” By internalizing the message that we’re learning, we’ll create a lifelong learner who accepts and embraces challenges in productive ways.

Successful sensemaking starts by framing the right performance issues to improve and then building a systemic picture of the structural forces driving performance. We often avoid or don’t apply either step. We tend to say, “Don’t just sit there, do something.” But, the right frame of the issue and a rigorous operational picture of structure must come first. Next, you can generate higher leverage solutions…and then act.

So, we’ve picked the right performance issue to fix, we’ve figured out the root causes, and now we can find ways to make a real difference.

If we’re using high SysQ, we’ll look for places to make a big impact. We’ll try to make the most of our resources and avoid any surprises.

When startups enter the market, they often follow a simple strategy: they lower their prices to boost sales. But this can backfire. It can create demand that’s too high for our current capacity, leading to delays, defects, and unhappy customers. And that can hurt our sales in the long run.

Instead, we should try a different approach. We should keep our prices high enough to meet the demand of early adopters who want new features and services, but not so high that we lose customers who are looking for something easier to use. We can use our profits to improve our capacity and then slowly lower our prices so that demand and capacity stay balanced.

But here’s the thing: most of the time, we choose solutions that we’ve already tried or even thought of before. We’re not very creative, and we tend to get stuck in a rut. We end up with slightly better or worse customer satisfaction, a little less or more unemployment, or a small decrease or increase in carbon emissions. But we don’t get close to our goals.

SysQ can help us break free from this cycle. It can help us generate solutions that are truly innovative and effective. SysQ-generated solutions are often:

1. Innovative and Overlooked

2. Counterintuitive

3. High-leverage

Innovative and Overlooked

Focusing on interrupting the madrassas pipeline training new terrorists is an overlooked strategy — it’s far more innovative and outside the box than typical anti-terrorism strategies.

When a metro region decided to boost its arts and culture scene to address workforce shortages, that was a smart economic move.

Another innovative idea? Redefining internal customers as ‘On the Fence,’ ‘Advocates,’ and ‘Haters’ and implementing a strategy to reduce early ‘Haters’ instead of the usual focus on building early ‘Advocates.’ This approach saved the aerospace company millions.

We can come up with all these innovative, outside-the-box strategies because we’ve set up the right issues and developed the right mental model of structural drivers. We see things more broadly and understand them more operationally.

Counterintuitive

We often focus on the right levers to make positive changes, but sometimes, the results are unexpected. For example, lowering prices might seem like a good idea, but it can actually lead to lower sales in the long run. Similarly, building only low-income housing can create neighborhoods that become poverty pockets. It’s counterintuitive to remove low-income housing and replace it with mixed-use and mixed-income housing, which includes a range of low to high-income housing. The amount of solely low-income housing is a lever, and our emotional reactions to poverty often lead us to pull the right lever in the wrong direction.1

High-leverage

Have you noticed how often we have planning meetings where we adjust last year’s strategy or budget a bit? We might allocate slightly more resources here and less there. This tweaking approach could be called the Buckshot Strategy Planning Process. We believe that we need to use our scarce resources wisely, so we take a broad approach.

Suburbanites are often surprised when building new roads and adding more lanes to reduce traffic congestion only ends up causing more congestion a few years later. Reducing congestion encourages more development, which then increases congestion. Instead of trying to eliminate congestion, it’s better to accept road congestion and use that pressure to stimulate jobs closer to where people live or public transportation.

One insurance provider faced a challenge in maintaining enough claims processing staff. New hires quickly burned out dealing with stressed-out customers who wanted their claims processed quickly. The typical approach is to increase the number of people involved; we need more of them! So, the company hired more, only to find burnout and turnover continued to accelerate. They discovered a high-impact strategy. Develop the emotional intelligence (EQ) of the department, especially its senior leaders. This allowed the staff to interact with customers with greater empathy and reduced burnout. Additionally, senior leaders could support their staff by focusing on their well-being, which reduced turnover more.2

Our strategies often involve multiple physical aspects of the ecosystem. We increase inventory and buffers, build new facilities and infrastructure, and hire more staff. But it’s often more effective to focus on the less physical aspects: building skills, changing decision rules, reducing burnout, improving information flows, increasing incentives or penalties, and ultimately transforming the mindsets we use to make all decisions.

These high-leverage interventions, though rare in typical planning frameworks, become clear when we apply the skills of SysQ.

“The illiterate of the twenty-first century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” — Alvin Toffler The Problem with Our Mental Models

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Have you ever felt like your organization is running faster and faster just to stay in the same place? Like Alice in Wonderland, many leaders find themselves stuck in what I call the "organizational hamster wheel" - expending enormous energy without making real progress. The root cause often lies in our mental models - the stories and assumptions we use to make sense of the world.
The Limits of Single-Loop Learning

Most organizations default to what Chris Argyris calls single-loop learning - we observe an outcome we don't like, make an adjustment based on our existing mental models, and hope for better results. Consider these cautionary examples:

Wells Fargo tried to improve performance by incentivizing employees to open more accounts. This led to widespread fraud as employees opened accounts without customer knowledge.

Humanitarian organizations attempted to combat malnutrition in Peru by providing food directly to families. They didn't anticipate that families would give the food primarily to working males, leaving children still malnourished.

A claims processing department faced high workload and responded by aggressive hiring. This actually worsened their problems as training demands increased stress on experienced staff, leading to more turnover.

In each case, organizations applied solutions that made intuitive sense based on their existing mental models. But those models were fatally flawed.

The Anatomy of Problem-Solving

To understand why organizations fall into these traps, let's examine the three distinct phases of problem-solving.

Phase 1: Sense-making

We begin by identifying a gap between our current reality and our vision of an ideal future state. Think of it as the difference between "what is" and "what could be." This gap forms the frame of our problem. We then access our mental models - our theories about how the world works - to make sense of this gap and its causes.

Phase 2: Solving

Using these mental models as our guide, we develop solutions. Like a receipt printing from a checkout machine, our mental models process the problem and output what we believe to be the best plan or strategy. While this might seem oversimplified, it evocatively represents how we move from understanding to strategy and decisions.

Phase 3: Implementing and Learning

Finally, we put our solution into action and observe the results. Sometimes everything works as planned. But often, we face unexpected outcomes or find the gap persists. At this critical juncture, we face a choice: do we just adjust oursolution, or do we question our underlying assumptions?

This is where many organizations get stuck. When faced with disappointing results, the natural tendency is to loop back to Phase 2 - tweaking the solution while using the same mental model.

Remember our claims processing department? When their initial hiring push didn't solve the problem, they simply decided to hire even more people, faster. This is classic single-loop learning - adjusting the solution without questioning the underlying assumptions that created the problem in the first place.

Understanding Double-Loop Learning

This is where double-loop learning is required. Instead of just adjusting our actions (the single loop), we examine and revise our fundamental assumptions about the problem (the second loop). We surface, explore, test, and improve our mental models.

The process works like this:

We identify a performance gap between our aspirations and reality Instead of jumping to solutions, we pause to examine our mental models We use tools like systems mapping to make our assumptions explicit We test and revise those assumptions based on evidence We develop new mental models that better reflect reality Only then do we design solutions based on these improved models

Moneyball: The Power of Double-Loop Learning

One of the clearest examples comes from baseball. Billy Beane and the Oakland A's revolutionized how baseball teams evaluate talent by questioning fundamental assumptions about what makes a valuable player. The conventional mental model focused on batting average and RBIs. By examining and revising these mental models, they discovered that on-base percentage and slugging percentage were better predictors of offensive success.

This wasn't just about trying a different metric - it was about fundamentally reconceptualizing how baseball talent evaluation worked. The results transformed the sport.
Building Better Mental Models Through Systems Thinking

Effective double-loop learning requires what's called "systemic intelligence" or SysQ - the ability to see and understand the driving characteristics of complex systems. This involves:

Making mental models explicit through visualization tools Understanding how different parts of the system interact Identifying high-leverage intervention points Testing assumptions through careful observation Learning continuously during implementation

Billy Beane — and his management staff — double-looped their mental models of what makes great baseball players. The claims department mentioned earlier eventually double-looped and stopped hiring to address staff shortage. And organizations working to address malnutrition in Perú also created a double-looped, collective mental model of why malnutrition persisted…and how to reduce it.
Moving from Theory to Practice

To apply double-loop learning in your organization, team, or coalition:

When facing persistent problems, pause to examine your mental models — step off the hamster wheel!Where possible, use tools like system mapping to make assumptions explicit Apply Conversational Capacity to engage multiple perspectives — challenge your mental models Test revised models with small experiments — use maps or simulation models if possible Create learning loops to enable continuous learning — leading indicators are especially valuable

Remember: the goal isn't just to solve today's problem, but to transform our understanding in ways that help us handle future challenges better. That's how organizations break free from the hamster wheel and achieve sustainable success.
Double-Loop Learning in Action: Two Success Stories

Transforming Huntsville into a Tech Hub

Huntsville, Alabama faced a critical challenge: despite attracting young tech talent for internships and entry-level positions, they couldn't retain them. The city's initial mental model assumed this was primarily a compensation issue. However, through a rigorous ecosystem mapping process, they discovered something surprising: their strategy of importing seasoned STEM professionals was actually undermining their ability to retain young talent.

The mapping process revealed that young professionals weren't leaving primarily because of money - they were leaving because they were bored. This insight led to a fundamental shift in their mental model about what makes a city attractive to tech talent. Instead of just focusing on traditional economic development, they realized they needed to invest in what they called the "soft stuff":

Creating vibrant arts and culture scenes Developing outdoor shopping areas with restaurants Establishing entertainment venues like minor league baseball Building community gathering spaces

By questioning and revising their mental models about talent retention, Huntsville transformed itself into one of America's most attractive tech communities. This wasn't just a change in tactics - it represented a fundamental shift in how they thought about economic development.
Reimagining Claims Processing

A Fortune 100 financial services company provides another powerful example of double-loop learning. Their claims department faced a perfect storm: high turnover, increasing workload, stressed employees, and declining customer satisfaction. Their initial mental model suggested a straightforward solution: hire more people faster.

However, through a series of facilitated mapping sessions, they discovered that their assumptions about the problem were fundamentally flawed. The mapping process revealed:

Faster hiring actually increased stress on experienced staff Training burdens were overwhelming mentors Emotional intelligence was more critical than technical skills Staff well-being directly impacted customer satisfaction

This new understanding led them to completely reimagine their approach. Instead of just accelerating hiring, they:

Developed emotional intelligence training programs Created formal mentoring structures with manageable ratios Implemented stress management initiatives Built workload management systems that balanced efficiency with employee wellbeing

The results were transformative: higher employee satisfaction, lower turnover, and better customer outcomes. But perhaps most importantly, they developed a new mental model about the relationship between employee development, workplace stress, and organizational performance.
The Learning Organization: Moving Beyond the Hamster Wheel

These success stories illustrate a crucial point: double-loop learning isn't just about solving problems - it's about transforming how organizations think about their challenges. In both cases, success came not from trying harder within existing mental models, but from fundamentally reconceptualizing the nature of their challenges. By seeing deeper…with greater rigor and clarity.

The ultimate benefit extends beyond solving individual problems. It creates what Peter Senge calls a "learning organization" - one that's constantly examining and improving its mental models and growing its systemic intelligence (SysQ).

In today's complex world, this capacity for deep learning might be the most important competitive advantage an organization can develop. Are you ready to question your mental models and engage in double-loop learning?