“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

---

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?

“The important thing in science is not so much to obtain new facts as to discover new ways of thinking about them.” —Sir William Bragg

The slinky principle: Transforming how we see and solve problems.
In today's rapidly evolving world, we face increasingly complex challenges - from organizational transformation to climate change, from healthcare reform to technological disruption. Yet our approach to solving these challenges often resembles watching a magic show: we see the effects but struggle to understand the underlying causes. We react to symptoms, implement quick fixes, and wonder why our solutions don't stick. There's a better way.

Systemic Intelligence (SysQ) offers a powerful lens for seeing and solving complex problems. Like a skilled magician who understands the mechanics behind illusions, those with high SysQ can peer beneath surface-level events to grasp the deeper structures driving behavior. This capability isn't just academic - it's the key to finding genuine leverage points for lasting change. Whether you're a business leader navigating market disruption, a policymaker addressing societal challenges, or an individual trying to create sustainable change, understanding the "magic" behind system behavior is your pathway to more effective action.

SysQ’s magic helps you find leverage — the ability to fundamentally transform the system you wish to improve. This requires understanding the mindset — or as I often think of it, the purpose or aim of systemic intelligence. The aim of SysQ is summarized in a a core tenet that is the magic phrase you’ll need to find the leverage you want.

The AIM of SysQ is summarized by the core tenet…

To Find Leverage You Must First Understand the Structure Driving Behavior

Let’s clarify what that tenet means…

EXAMPLE — SYSTEM 1

I’m going to administer an input to this system.

Answer the following question with the first idea coming to mind.

**What’s the cause of this behavior?**

Before going to next video please answer: What’s the cause of this behavior?If you’re like most people, you answered: gravity. Some will answer removing the hand.

Here’s another system. I’ll administer the same input.

Huh? Different behavior. Thankfully, because my feet are still dry!

So gravity and removing the hand are necessary but insufficient to create the oscillating behavior.

What’s the cause of this behavior?

Most people will now say it’s the slinky…it’s the system. A slinky is an oscillation waiting to happen — which happens when I remove my hand.

One important attribute of leverage is the ability to fundamentally improve ecosystem performance. Imagine that you want to significantly improve the oscillations — to dramatically dampen them — perhaps they are creating chaos in your organization.

If your mental model is that gravity is causing the oscillation, how much leverage do you have? None. You can’t change gravity. I imagine you can identify times in your work and personal lives where it feels like fighting gravity — something refuses to improve no matter hard you try.

If your mental model is that removing the hand is causing the oscillation, how much leverage do you have? Before you say some, let’s also say you don’t own the hand. Again you have no leverage. You can either become an Eeyore and proclaim nothing will change. Or you’ll be a victim and blame others. Either way, you’re stuck with a persisting problem.

However, if your mental model is that the system generates the behavior, you can modify the system so that when the hand is removed by someone else — and even though gravity exists — the behavior is fundamentally improved. You’ve found leverage.

**EXAMPLE — SYSTEM 2**

Watch this cotton candy eating contest.1

---

Whose mental model is the high-leverage one?

---

Although I personally relate to how the guy on the left feels, let’s focus on the winner. He had a mental model that deeply understood the structure of cotton candy. He was able to modify that structure to significantly accelerate the time to eat the cotton candy.

FOCUS ONLY ON THE STRUCTURE GENERATING PERFORMANCE

We often respond to problems using the same old thinking — applying outdated and inaccurate mental models — without once questioning: Do I know the structure of this ecosystem that’s responsible for this problem?

If we don’t develop sufficient systemic insight into how the ecosystem works we will be unable to achieve the performance goals we set. We will continue to be buffeted by the “slings and arrows of outrageous fortune”. The Focus is Only on the Structure Generating Performance

Disciplined application of SysQ means we focus only on the structure generating performance. People with high SysQ first filter out the essential from the mess — from the noisy barrage of data — to cogently establish what performance they wish to transform…what they wish to see unfold in the future. Then they assemble a mental model of the structure that can generate that performance.

They use a range of tools to pool perspectives into a collective mental model that understand this structure. You may have heard of systems archetypes, causal loop diagrams, stock and flow maps, and simulation models. Each of these tools has its place in developing a more rigorous “picture” of the structure. Sometimes a simple archetype or causal loop diagrams is sufficient; sometimes teams need to spend the time and resources to develop a computer simulation model. You’ll learn more about each of these tools in this substack — and can dive into ones you find most relevant to your current needs. Back to the aim of SysQ.

Having high SysQ means avoiding wasted time in trying to understand EVERYTHING about the ecosystem. Remember the slinky: all I need to know to improve performance is to focus on the structure. If I tried to understand the whole system I’d needlessly spend time analyzing the colors or the shape. Similarly, with the cotton candy system, although perhaps of great importance to the eater, the flavors or colors don’t contribute one iota to how the structure behaves. Yet many of my clients want to spend time analyzing interesting aspects of their ecosystem — even though those aspects are irrelevant. Studying them consumes precious time and resources…often leading to the dreaded analysis paralysis.

WE OFTEN FOCUS ON THE LOW LEVERAGE

Organizations often focus on low leverage interventions, investments, and policies. They will adjust price up or down. They’ll tweak a complex budget a few percentage points one way or another. They’ll replace one person in a leadership position with a different one — without changing how the rest of the organization around the individual works with them.

These low leverage interventions cannot fundamentally change behavior — changing the colors on the slinky would change its appearance, but it will still oscillate.

Our low leverage strategies overlook more transformational interventions: radically modifying rewards and incentives; changing the type of data generated — including who does…or doesn’t…have that information; and shifting fundamental paradigms — like competition vs cooperation.

SysQ helps identify those higher leverage interventions often overlooked when developing strategy. Remember, the basic tenet of SysQ — the underlying mindset — is:

---

To Find Leverage You Must First Understand the Structure Driving Behavior

---

> “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.

---

“Details are confusing.

It is only by selection, by elimination, by emphasis, that we get at the real meaning of things.”

—Georgia O’Keefe

---

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.

---

“A model should be as simple as possible, but no simpler.”

—Albert Einstein

---

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.

---

“If you can define the problem differently than everybody else in the industry, you can generate alternatives that others aren’t thinking about.”

—Roger Martin

---

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.

---

“It’s not that I’m so smart, it’s just that I stay with problems longer.”

— Albert Einstein

---

C. IMPLEMENTING & LEARNING

We start by defining a performance issue and building a model to understand it. Then, we come up with a hypothesis for how to improve it, which we call decisions, plans, or strategies. But even though we’ve tested our hypothesis using SysQ skills and tools, we still need to build confidence in it. And that confidence is best built as we implement the strategy.

Kurt Lewin said that to understand something, we should try to change it. As we learn from implementation, we can see what works and what doesn’t. This helps us figure out if we have the wrong strategy, if it needs to be changed a little, or if it needs to be changed a lot. A higher-leverage learning happens when we realize we’ve made a mistake in our thinking. Maybe we don’t fully understand the causes of the problem or we’ve chosen the wrong performance issues. We’re building ladders for the wrong wall.

Sometimes, we review and revise our sensemaking, which is called double-loop learning. Single-loop learning happens when we think we’ve figured everything out and only change our tactics to solve problems. Double-loop learning happens when we hold our beliefs as hypotheses and are willing to question them. We’re willing to change our thinking before we change our actions.

In the Implementation and Learning phase of the iterative learning process, our goal is to gather as much useful information as possible to confirm or disprove our strategy. We’ll look for leading indicators, keep an eye on them, analyze the data, and decide if we have the wrong strategy. If we do, we can try adjusting it using the same thinking we used to develop it. Or, we might realize we have a wrong understanding of the ecosystem or the performance issues we want to improve. In that case, we’ll double-loop and make any necessary changes to our assumptions. Then, we’ll go back through the process again.

---

If you want to truly understand something, try to change it.

— Kurt Lewin

---

SUMMARY

The SysQ Process is a super-powerful tool to tackle tricky problems in our crazy, interconnected world. Instead of jumping to solutions, it guides us through three main steps: first, we figure out what’s really going on and why; second, we come up with creative, unexpected, and super-effective solutions; and finally, we put those solutions into action while staying open to learning and changing our approach as needed.

At its heart, this process helps us avoid common pitfalls like building solutions for the wrong problems or fixing things that just make things worse. Instead, it helps us understand the real issues, see how they’re connected, and come up with truly transformative solutions. By doing double-loop learning, we can keep improving not just our actions, but also our understanding of the challenges we face. This iterative approach means our solutions get better and better over time, leading to lasting positive change instead of just temporary fixes.

The SysQ Process is actually pretty simple, and we all know the steps. But we often don’t spend enough time figuring out what’s really going on or challenging our thinking in ways that support double-loop learning. That’s why this substack is here! We’ll share examples, practical tools, and tips to help us use the SysQ Process more effectively and learn more by doing.
1

Meadows, D. H. (2015). Thinking in Systems. Chelsea Green Publishing.
2

Clark, K. et al. (July 2015). Using Systems Thinking to Shift Mindsets. [white paper]. International System Dynamics Conference, Boston, MA, USA

"We count by years, but we live by days. Rightfully, we should do both by seasons." — Ivan Doig, Dancing at the Rascal Fair

YOU’RE ON A COMPANY’S EXECUTIVE TEAM

Imagine you’re on the senior executive team of a multimillion-dollar company. Last quarter’s financial statement just came out, and it’s a doozy. Your company lost several million dollars. How do you feel?Bad, right? Worried?

What ideas / actions do you think the executive team would discuss and probably implement? If like most companies, you’d consider:

• Lay offs?

• Travel restrictions?

• Training reductions?

• Several other cost cutting strategies?

---

Now suppose you were shown the graph drawn below. Profit has plummeted over the past six months.
How do you feel now?

Even worse!

Why? Because it looks like things are getting worse–and rapidly!

What ideas or actions do you think the executive team would discuss and likely implement? If you’re like most companies, you might consider:

• Hiring a consulting firm?

• Closing part of the business?

• Ending a product line?

And as an individual on the team you are concerned about a few things. There is probably going to be some personnel changes on the leadership team. The CEO might be let go. The board will certainly be looking for someone to blame.

You? You are most likely improving your resume, contacting friends and colleagues in your network–spending a lot of time on LinkedIn.

Now, suppose you’re shown this graph.Now how do you feel?

Relieved, right? You may notice even a slight relaxing in the body now (if you were reacting at all to the exercise).

Why relieved?

Because, when viewed over a longer time horizon, the plummeting profits are part of a larger, predictable pattern.

If I asked you what type of business you’re running, you might say it’s a commodity business or more likely a seasonal business (since it has the same fluctuations at the same time each year). Maybe you’re a winter sports chain in New England, selling mostly skis and snowboards.

If it’s a seasonal business (like the winter sports chain) what solutions might you suggest?

You might suggest the following:

Closing the business in the summer months

Diversifying (sell golf clubs!)

Investing during the summer months to boost sales in the winter

Compare the solutions you would propose when analyzing the broader pattern to those you would suggest based solely on quarterly results (an event) or just a half-year’s worth of data (a small window).

The newer solutions are proactive, giving you more control and often revealing opportunities to generate additional revenue. In contrast, the reactive solutions focus on cutting costs.

The key difference between reactive and proactive solutions lies in your understanding of the business’s nature. It’s like seeing the slinky. You grasp the business’s structure, which is seasonal. Once you understand this structure, you can develop a much more effective solution tailored to your business.
EVENT THINKING VS. DYNAMIC THINKING

From a young age, our minds naturally focus on events and behaviors that occur right in front of us, both in space and time. This is not surprising, as humans are hardwired to prioritize the immediate and the present. The ability to hyper-focus on a predator that suddenly leaps from the bushes and react swiftly has proven beneficial to our species. There’s no need to meticulously chart the long-term history of predators leaping from bushes and consuming humans; the immediate response is sufficient: run!

What sets humans apart from other animals is an additional skill: the ability to discern patterns and make predictions. This higher level of consciousness, our meta-level thinking, is essential for addressing adaptive challenges. Our mental models must also include the capacity to represent patterns and make predictions.

In the context of terrorism, it is common in the United States to focus on a single event like 9/11, while overlooking the long history of observable behaviors that could have predicted it. There had been escalating tensions, punctuated by events such as the bombing of the USS Cole and the subsequent US response, as well as actions by the US that supported those considered enemies of terrorist organizations. Adopting a broader, longer-term perspective enhances the quality of any mental model attempting to understand the expansion of terrorism—it helps us identify the causal structures driving it.

Organizations often focus on recent events, such as the last quarter’s profit margin, this week’s layoffs, or current scandals. For instance, the 2008 mortgage crisis in the US (and globally) is a notable example. Initially, in mid-2008, people believed it was a recent phenomenon, likely due to poor management decisions by a few. However, a longer-term perspective reveals that the US economy since the 2000 “sell-off” was stimulated by lower interest rates set by the Federal Reserve. Additionally, granting subprime loans increased at a much faster rate than disposable incomes could sustain. In response, the Federal Reserve resorted to lowering interest rates once again to address the crisis.

The power of an event is undeniable, but unfortunately, we often react impulsively to it. Layoffs, reassignments, extra bureaucratic oversight, and even military invasions are common knee-jerk responses. However, understanding the world and its causes requires the ability to identify patterns of behavior.
REDUCING YOUR STATE’S MEDICAL COSTS

You’re working on your state’s initiative to reverse the rising medical costs. You’ve selected two states to benchmark, and based on their performance, you’ll contact your counterpart in each state to learn from their strategies.

Here are the per capita medical costs for two states. Which one, based solely on this data, would you choose as your benchmark for “best practices”?

he obvious answer is State B.

However, before you draft an email to your counterpart in State B, your assistant bursts into the room with this chart. Now, which state should you contact to learn from their successful practices?
This time the answer is State A.

This illustrates why it’s often beneficial to analyze metrics through the DYNAMIC Thinking lens. First, relying solely on “point in time” data can lead to incorrect conclusions. We frequently make decisions based on narrow time slices of data, which could be detrimental.

Second, comparisons between similar entities, such as states vs. states, cities vs. cities, or countries vs. countries, that only use single numbers can be misleading. Even if two states have the same per capita medical costs, if their trends are similar to those observed here, one may be experiencing rapid deterioration. They are not comparable at all.

Trend lines, on the other hand, often trigger deeper, more rigorous mental models. They provide an operational narrative that enhances our thinking, decisions, and strategies. In this case, observing the trends revealed a story about the direction of performance and clarified the clear winner.

So a good SysQ tenet is:

When something’s important, look at it as a trend over time.
TIME DELAYS: ANOTHER REASON WE NEED DYNAMIC THINKING

In many cases, decisions made today may not have a desired impact on the organization for years. For instance, the Beer Game, a popular learning tool, illustrates our inability to comprehend time delays. Thousands of executives, MBA students, engineers, and others who have played this game have struggled to manage their inventories, even when the retail demand is linear and predictable. Anyone who has played this game understands how inherent time delays in a system’s structure contribute to its performance.

This common limitation also affects our understanding of climate change. John Sterman and Linda Booth-Sweeney have shown that a majority of their subjects (MIT students) cannot predict how long it will take a decrease in carbon emissions rates to impact global climate temperatures, even when the assumptions are clearly stated.1

Ignoring time delays is a major contributor to the planning fallacy. This fallacy occurs when our predictions about the time required to complete a future task are overly optimistic, leading us to underestimate the actual time needed.

Overlooking time delays often results in the common “worse before better” dynamic when implementing public policies or organizational improvements. Public sector managers, rightly so given the public’s understanding of dynamics, avoid policies that may eventually improve situations in the long run but may worsen conditions in the short term.2
THE SHAPE OF A MOVIE

In 2017, we witnessed a plethora of extraordinary yet peculiar movies. Among them, The Shape of Water emerged as the most peculiar of all, potentially claiming the coveted Academy Award for Best Picture.

Its premise, centered around a mute woman named Elisa’s profound love for a creature that resembles a fish more than a human, may initially appear absurd or even laughable. However, this film achieved remarkable success, garnering numerous accolades and captivating audiences.

The trend line for her love of the water man is shown here.

The Shape of Water’s success lies in its ability to gradually reveal credible reasons for Elisa’s growing affection for the water-dwelling entity. This gradual progression allows viewers to comprehend and even predict her actions throughout the movie. If you were to watch only the last third of the film, you might find the scenes so absurd that they would be difficult to take seriously, potentially leading you to leave the theater.

However, by watching the entire movie from the beginning, you are presented with a rational, rigorous, and dynamic mental model that enables you to grasp and even anticipate the protagonist’s actions.
STARTING A SYSQ ANALYSIS? FIND THE PLOT FIRST!

At the outset of every SysQ initiative, establish a clear purpose. Collaborate with your team members and stakeholders who need to be involved. Ensure that everyone is aligned in understanding the purpose of the mapping, modeling, or SysQ analysis.

The purpose is to comprehend the underlying causal factors that contribute to ____________. This knowledge will enable us to develop more effective strategies, policies, or levers to influence ____________ in the future.

While verbal descriptions can suffice, visualizing trends over time through a trend graph can significantly enhance the impact and utility of the analysis, particularly in collaborative settings.
OPERATIONAL THINKING — FOCUSING ON THE PHYSICS — REQUIRES CLARITY ON THE TRENDS

OPERATIONAL Thinking — focusing on the physics — is fundamental to high SysQ and applying it involves gaining a comprehensive understanding of the systemic structure that generates the desired behavior. Clarity on the behavior is not merely a desirable outcome but a prerequisite for effective SysQ application.

The process of clarifying the trends, drawing and agreeing on these trends, aligns everyone on the same page regarding their underlying reasons for working together. It provides a clear vision, fostering strong motivation for collaboration. Therefore, I allocate significant time early in any consulting engagement to assist my clients in achieving vivid and rigorous clarity on the purpose: the trends they intend to influence.
ADDITIONAL BENEFITS OF DRAWING TRENDS OF INTEREST

Gaining clarity on trends helps focus effort and develop a vision of success. But it offers many other benefits as well.
Benefit 1

It automatically shifts collaborators into a generative process. Seeing a trend line prompts the mind to seek understanding. Why does this happen? This prompts us to delve into our mental model database, searching for past experiences that might explain it. We transition from reactive thinking—“How do I react to this?”—to becoming more inquisitive and reflective. We seek causality and explore what generates the behavior or enter a generative process.4
Benefit 2

It helps identify faulty thinking by comparing our perceived trends to what others believe or know. Sometimes, our assumptions are incorrect.

Let’s say I believe that our projects are taking increasingly longer to complete, perhaps because my last two actually did. I might suggest to colleagues we need to invest in some improvement, perhaps upgrade the IT system. Instead of promoting a solution to a nonexistent problem, if I drew a trend line similar to the one shown below, I could share with others, including those who have the data.

I’d then have the opportunity to learn why my assessment of reality was so far off. Yes, I’d have a learning moment. In this case I’d learn that I was still holding onto the increase in project time that happened late 2016 when several employees left. And even though project times had steadily improved since then, my confirmation bias kept me looking for the trend to go the other way. In this case, I learned something about myself.

I’ve seen the process of drawing trends help legislators who are normally disagreeable with one another become collaborative because they had some inaccurate understanding corrected. If seen engineers show trend lines to management to help them become more concerned about where a staffing issue was likely trending in the not too far future.
Benefit 3

Asking others you wish to be part of the effort to draw trends they are most concerned with–trends that may not be ones you initially used in trying to motivate their involvement–may help you find common ground. It may also help you expand your field of vision.
TO LEARN MORE ABOUT USING TREND GRAPHS

Check out this substack’s resources section to learn about several trend graph types you’ll find useful across a diverse range of purposes and activities.
1

J. Sterman and L. Booth Sweeney, Understanding public complacency about climate change: adults’ mental models of climate change violate conservation of matter, Springer Netherlands, Vol. 80, Numbers 3-4
2

Soderquist, C. Facilitative Modeling: Using Small Models to Generate Big Insights, The Systems Thinker, Pegasus Communications, December, 2003
3

Forbes, 'The Shape Of Water' 4K Blu-ray Review: The Ultimate Wet Dream, Mar 19, 2018, https://www.forbes.com/sites/johnarcher/2018/03/19/the-shape-of-water-4k-blu-ray-review-the-ultimate-wet-dream/
4

Calancie L, Anderson S, Branscomb J, Apostolico AA, Lich KH. Using Behavior Over Time Graphs to Spur Systems Thinking Among Public Health Practitioners. Prev Chronic Dis 2018;15:170254. DOI: http://dx.doi.org/10.5888/pcd15.170254.

The authors used trend graphs with teams in maternal and child health. 86% of participants found the technique improved their thinking and engagement enough to indicate they would use these tools after the workshop.

"The framework, tools, and language of system dynamics should be accessible to all. Anyone can do this at some level, and everyone should try!" — Barry Richmond

The SysQ Mindset (or Aim) is important, but it’s not enough on its own to create effective and powerful strategies to tackle tough, complex, and chaotic issues. Just saying, “Let’s try to understand the structure causing the problem we want to solve” without coming up with new ways to make sense of the world is like knowing you’re drowning but not knowing how to swim. As Einstein said, “We can’t solve our problems with the same thinking we used when we created them.” Others might say, doing the same things over and over again (with the same thinking) is the definition of insanity.

THE THINKING APPLIED TO THE US COUNTERINSURGENCY STRATEGY

Excerpt From a Famous — Yet Poorly Titled — New York Times Headline

We Have Met the Enemy and He Is Powerpoint

Gen. Stanley A. McChrystal, the leader of American and NATO forces in Afghanistan, was shown a PowerPoint slide in Kabul last summer that was meant to portray the complexity of American military strategy, but looked more like a bowl of spaghetti.

“When we understand that slide, we’ll have won the war,” General McChrystal dryly remarked, one of his advisers recalled, as the room erupted in laughter.

The slide has since bounced around the Internet as an example of a military tool that has spun out of control. Like an insurgency, PowerPoint has crept into the daily lives of military commanders and reached the level of near obsession. The amount of time expended on PowerPoint, the Microsoft presentation program of computer-generated charts, graphs and bullet points, has made it a running joke in the Pentagon and in Iraq and Afghanistan.

“PowerPoint makes us stupid,” Gen. James N. Mattis of the Marine Corps, the Joint Forces commander, said this month at a military conference in North Carolina. (He spoke without PowerPoint.) Brig. Gen. H. R. McMaster, who banned PowerPoint presentations when he led the successful effort to secure the northern Iraqi city of Tal Afar in 2005, followed up at the same conference by likening PowerPoint to an internal threat.

“It’s dangerous because it can create the illusion of understanding and the illusion of control,” General McMaster said in a telephone interview afterward. “Some problems in the world are not bullet-izable.”

In General McMaster’s view, PowerPoint’s worst offense is not a chart like the spaghetti graphic, which was first uncovered by NBC’s Richard Engel, but rigid lists of bullet points (in, say, a presentation on a conflict’s causes) that take no account of interconnected political, economic and ethnic forces. “If you divorce war from all of that, it becomes a targeting exercise,” General McMaster said.

Commanders say that behind all the PowerPoint jokes are serious concerns that the program stifles discussion, critical thinking and thoughtful decision-making. Not least, it ties up junior officers — referred to as PowerPoint Rangers — in the daily preparation of slides, be it for a Joint Staff meeting in Washington or for a platoon leader’s pre-mission combat briefing in a remote pocket of Afghanistan.1

As I mentioned, I think the title of this article is a bit misleading. It’s not really a problem with PowerPoint, although I’ll admit that it can make meetings a bit dull. The issue is with the type of “systems” analysis that’s been done.

While it’s a good attempt to map out the system, it doesn’t follow the basic principles of focusing only on the parts that are responsible for the behavior. It’s like mapping every color in the slinky shown earlier and including all the other details that don’t really matter. When we’re trying to solve complex problems, we need to simplify the process of mapping the structure and use some additional SysQ Thinking Skills.

---

---

THINKING SKILLS

To truly apply the mindset of SysQ, we need to think beyond the surface-level behaviors and delve into the underlying structures that drive them. This requires developing some rarely used systemic thinking skills.2 We must shift up from the traditional thinking we apply to problem solving into this higher order thinking. Some types of shifts in thinking include shifting from…

EVENT Thinking — We focus on the latest or most newsworthy event.

➡︎ DYNAMIC Thinking — We shift to seeing how that event is part of a longer term trend. This helps us more clearly see what structural forces might be driving that pattern.

---

TREE Thinking — We focus individually on each silo in the organization, each department in the division, each sector in society, each country around the world — the equivalent of tree-by-tree thinking.

➡︎ FOREST Thinking — We try to understand how each silo interacts within the organization and even outside it. This interconnected web of departments, divisions, offices, silos, and sectors — the forest — is responsible for larger system behavior.

---

CORRELATIONAL Thinking — We use regression and correlational analysis to propose causality. Or use anecdotal evidence of when I see this happen something else happens — without a clear and rigorous causal theory.

➡︎ OPERATIONAL Thinking — A former colleague read an esteemed academic paper on US dairy policy that was prescribing potential strategies to increase production. The analysis included economic indicators like interest rates, feed prices, farming expenses — but despite having high statistical confidence, the analysis left out the one system component essential for production. Cows. Only a model that includes cows can be operational. Only operational models create clarity on levers. Yet we often create strategies based on non-operational thinking as useless as a dairy model without cows.

---

LINEAR Thinking — We assume working more hours will complete more work, hiring more people will impact client satisfaction, hiring a consultant will improve our capacity, and an endless stream if IF THIS THEN THAT.

➡︎ FEEDBACK LOOP Thinking — The world works with feedback loops. For example, there’s a feedback loop where more hours worked builds burnout, less completion, more hours, and more burnout. A nasty feedback loop begins to dominate as a vicious cycle. Feedback loops drive the complex dynamic behaviors experienced in the real world and must be included in our strategies and decisions.

---

MEASUREMENT Thinking — A commonly held belief is to make decisions only using data that we can measure precisely and represent numerically in our models. Production can be included. Burnout can’t.

➡︎ QUANTITATIVE Thinking — Anyone think Burnout doesn’t determine production? Unless you do believe it has no impact, then you know any model excluding it to be wrong. Even if unmeasured, it can still be quantified — even if anecdotally — ranging from 0-100. 0 means everyone is super relaxed; 100 means they are immobilized from burnout.

---

---

APPLYING SOME OF THE SYSQ THINKING SKILLS TO THE COUNTERINSURGENCY STRATEGY

Let’s explore how the thinking skills mentioned here can be applied to the US Afghanistan counterinsurgency strategy. We’ll use an analysis by Barry Richmond, the mastermind behind many of the SysQ thinking skills, and further explained by my colleague, Steve Peterson, in The Systems Thinker.3

Instead of looking at the latest terrorist event as something to respond to in isolation — where a strategy would be to hunt down those responsible and bring to justice — DYNAMIC Thinking would suggest it’s important to look at terrorist acts as a trend over time
Further, FOREST Thinking would ensure that the activity of terrorism wasn’t just localized to a region within Afghanistan. Instead it would look at such activity over the breadth of the country…and perhaps the network extends even outside the borders.

Having used SysQ to frame up the issues to be able to find leverage, we can apply OPERATIONAL Thinking. The number of terrorist acts occurring over a year is generated by two variables: the number of terrorists and their productivity (terrorist acts / terrorist / year). By making it operational, it becomes clear there are only two levers (places) to apply an intervention to reduce terrorist activity. Reduce the number of terrorists and or reduce their productivity — their terrorist acts/terrorist/year. That’s it. All solutions will be designed to work on one or both of these levers.
LINEAR Thinking would suggest that an intervention that kills terrorists would work to reduce terrorist activity. However, by applying FEEDBACK LOOP Thinking we can see it will likely kill people who are not terrorists; this will motivate more potential terrorists to enroll in terrorist organizations and eventually be trained to commit acts. This would create a nasty reinforcing feedback loop (R1) known as a vicious cycle.
Finally, although in our simplistic view of the world, it would be awesome for our actions to only impact the intended target, we just learned with FEEDBACK LOOP Thinking that’s often not the case. In fact, by applying RIPPLE EFFECT Thinking we can see that not only would killing terrorists likely lead to more terrorists signing up. Killing innocent civilians can also lead to more sympathizers, who can increase terrorist productivity. They might provide financial resources, hide or house terrorists, or share intelligence that helps them.

In our organizations, we often think that lowering prices will boost sales. But here’s the thing: sometimes, when sales go up too high, customers aren’t happy, and sales start to drop. It’s like a balancing act.

In our communities, we usually think that the only way to stop crime is to have more police. But there are other things that can help too, like housing, unemployment, access to support for substance abuse, and even a sense of community and belonging. We need a different way of thinking, like the FOREST approach, to find effective solutions.

There are many thinking skills that are part of what I call Systemic Intelligence, which is like a superpower for thinking. These skills can help us in all sorts of areas of life. But to make better strategies and find ways to make things work, we need to use them in a process and with some tools.
SYSQ THINKING SKILLS

If you’re looking to learn practical ways to build any or all of these thinking skills, you’ve come to the right place. This substack will dive deep into each skill. There’s even a section dedicated to just the skills. And guess what? The best way to build these skills is by using some of the SysQ Tools as you learn (using the SysQ Process). Keep an eye out for articles tagged with Thinking Skills to see how they’re applied in real-world examples.

Everyone has some level of these skills, but the goal of this substack is to help us all improve and strengthen them so we’re ready to tackle the toughest problems life throws our way.
1

Bumiller, E. We Have Met the Enemy and He Is PowerPoint, New York Times (April 26, 2010), https://www.nytimes.com/2010/04/27/world/27powerpoint.html
2

Barry Richmond first proposed 7 Systems Thinking Skills of a Systems Thinker in The Thinking in Systems Thinking: How Can We Make It Easier to Master? The Waters Center for Systems Thinking has since added to those skill to create the 14 Habits of a Systems Thinker.
3

Peterson, S. Applying System Dynamics To Public Policy: the Legacy of Barry Richmond. The Systems Thinker. https://thesystemsthinker.com/applying-system-dynamics-to-public-policy-the-legacy-of-barry-richmond/

The SysQ AIM (Mindset) and SysQ APTITUDE (Thinking Skills) guide us in seeing the world and analyzing complex challenges, but it’s the Tools that make our thinking visible, shareable, and actionable. These tools aren’t just ideas; they’re practical instruments that help us map, understand, and improve the systems that shape our lives. From simple SysQ Questions that can be used in any meeting to sophisticated simulation models that predict system behavior, each tool has a specific purpose in turning complex challenges into manageable opportunities.

Think of these tools as different lenses through which we can examine reality — each one revealing different aspects of the same system. Just as a carpenter wouldn’t use only a hammer for every job, systemic intelligence practitioners need multiple tools to effectively understand and influence complex systems. The key is knowing which tool to use when and how to combine them for maximum impact. In the following sections, we’ll explore each tool in detail, showing you not just how to use them, but when and why they’re most effective.
IT’S EASY TO FOCUS ON THE TOOLS FIRST

During the 1990s, organizational learning became the leadership fad du jour, with “systems thinking” becoming one of the biggest buzzwords of the decade. Everyone wanted to be systems thinkers. The Fifth Discipline, The Fifth Discipline Fieldbook, and The Systems Thinker1 accelerated a broad diffusion of systems thinking tools and techniques that were adopted by private sector organizations, NGOs, schools, and communities.

accelerated a broad diffusion of systems thinking tools and techniques that were adopted by private sector organizations, NGOs, schools, and communities.

Systems archetypes were the most popular tools. These are recurring behavior patterns found in various systems. For instance, one archetype is called Shifting the Burden to the Intervenor.

This is when a company relies too heavily on external consultants to solve problems they could solve themselves. It’s also when someone becomes overly dependent on coffee to boost energy instead of getting enough sleep, eating well, and exercising; with changes to a few words it also applies to using alcohol to address anxiety.Archetype here from The Waters Center for Systems Thinking

After studying many different systems, from businesses to ecosystems (even families), experts in the field identified a few common behaviors. Eventually, the systems thinking field settled on nine archetypes (like Fixes that Backfire,Shifting the Burden to the Intervenor, and Success to the Successful).

Fixes That Backfire, sometimes referred to as Fixes That Fail, is shown above. Besides using this substack, you can learn more about Fixes That Fail and the other archetypes from the Waters Center for Systems Thinking.2

These archetypes were helpful and easy to understand, so many people who wanted to use systems thinking got excited about them and only used these tools.

But there are many other tools and techniques that should be part of the SysQ toolbox. Some are easier to learn and apply than the archetypes. Others take years to master, but the insights and learning they offer are worth it.

ARTIFACTS (APPLICATIONS and TOOLS)

There are myriad tools — of varying complexity — you should put in your SysQ Toolkit. They include:

SysQ Questions

a set of carefully crafted inquiries that can be used in any conversation to quickly surface systemic patterns and relationships, making them perfect for meetings and initial problem exploration

Behavior Over Time Graphs

help visualize how key variables change through time, revealing patterns and trends that might otherwise remain hidden in the complexity of data and stories

Multi-solving Tools

tools like FLOWER,3 guide us in finding solutions that address multiple challenges simultaneously, maximizing the impact of our interventions while minimizing unintended consequences

Systems Archetypes

recurring patterns of behavior found across different contexts and industries, serving as diagnostic tools to identify common systemic structures that often lead to problematic outcomes

Causal Loop Diagrams (CLDs)

map the circular nature of cause and effect in systems, helping us visualize how different variables influence each other and create feedback loops that drive system behavior

Stock and Flow Main Chains

identify the core structure of systems by tracking the accumulation (stocks) and movement (flows) of resources, information, or other variables that are central to the system's behavior

Stock, Flow and Feedback Maps

combine the power of stocks, flows, and feedback loops to create comprehensive visual representations of complex systems, revealing both structure and behavior

System Dynamics Models

computer simulations that bring these maps to life, allowing us to test different scenarios and policies before implementing them in the real world, providing the highest level of confidence in our understanding of system behavior
OPTIMAL TOOL? USES MINIMUM EFFORT / RESOURCES TO ACHIEVE REQUIRED CONFIDENCE

Each of these tools is like a special tool in our toolbox for understanding and changing complex systems. Some are easy to learn and use right away, while others take a bit more time and effort, but they give us deeper insights. The key is to know which tool to use when and how to mix them together to solve the problems we’re facing.

Many of these SysQ tools have been around for a while, and my colleagues and I have come up with some new ones to make it easier to use SysQ. For example, the SysQ Questions were created when I was teaching public health practitioners how to quickly improve the level of systemic rigor in conversations without having to use some of the more time-consuming visual tools.

There are at least two ways to figure out if these tools are worth using. One way is to think about what you want to achieve with them. The main goal of all these tools and techniques is to make your mental model (the way you understand the world) better, more accurate, and more reliable. In other words, the tool should make you feel more confident that you understand the underlying forces (the basic things that make things happen) that are causing the performance you want to change or improve.

Building simulation models, if done well, creates the most confidence — because they must be internally consistent and use quantifiable data. Creating stock and flow maps can also give you a lot of confidence because they’re the foundation of simulation models and could be simulated if you added data to them. But since we can’t always predict how things will change without a computer, we don’t have as much confidence in our ideas as if we had a simulation model. The chart below isn’t based on any research or has any exact numbers.
Another thing to consider is how much time it takes to learn and use each tool. SysQ says that tools that are easy to learn usually take less time to apply and use. For example, people can learn the SysQ Questions in a few hours and immediately apply them in meetings. But the skills of a good simulation modeler take months or even years to learn, and it can take several months working with an organization to develop a simulation model that gives you confidence in your insights.

Skilled SysQ practitioners are like tool experts who introduce and teach specific tools at the perfect moment. They help clients apply the tools in the right order based on their learning goals and what they want to achieve.
A VISION FOR CAPACITY BUILDING

Barry Richmond vision was everyone could learn systems thinking — at some level. It’s an innate intelligence that can be increased.4 Barry, when teaching system dynamics modeling to various multinational organizations, would show a Venn diagram (below) that suggests everyone should be able to read system maps, many should be able to draw simple maps, and a few should be able to build good simulation models. I’ve updated his original diagram to include additional tools used by other practitioners.Based on my experience teaching many of the SysQ tools above, I believe an ideal distribution of competency would see everyone using the mindset and SysQ Questions. Most would be able to read and apply archetypes, a few would be able to draw useful system maps, and a small percent would be able to develop simulation models. Such a distribution would create an ecosystem of creators, curators, and consumers of SysQ artifacts and insights.

The time it takes to learn and apply the tools and techniques increases exponentially the further right in the diagram. It takes a lot more time and skill to develop a map than to draw a behavior over time graph. And it takes exponentially more to develop a simulation model with dynamic questions and data. However, the amount of confidence in the insight generated from using tools on the right goes up. The type of insight and amount of time available are major factors in deciding which tool(s) would be most appropriate for your work.

“The framework, tools, and language of system dynamics should be accessible to all. Anyone can do this at some level, and everyone should try!”

–Barry Richmond
A WORD OF CAUTION ABOUT TOOLS: SOMETIMES WE CLING TO THEM AT OUR PERIL

We tend to cling to our tools — even when they’re not the best fit for the situation. Remember the saying, ‘a hammer looks like a nail’? Firefighters risk their lives by not putting down their tools when they need to run away from a wildfire.

“In Montana’s 1949 Mann Gulch fire, made famous in Norman Maclean’s Young Men and Fire, smokejumpers parachuted in expecting to face a “ten o’clock fire,” meaning they would have it contained by 10 a.m. the next morning. Until the fire jumped across the gulch from one forested hill slope to the steep slope where the firefighters were, and chased them uphill through dry grass at eleven feet per second. Crew foreman Wagner Dodge yelled at the men to drop their tools. Two did so immediately and sprinted over the ridge to safety. Others ran with their tools and were caught by the flames. One firefighter stopped fleeing and sat down, exhausted, never having removed his heavy pack. Thirteen firefighters died. The Mann Gulch tragedy led to reforms in safety training, but wildland firefighters continued to lose races with fires when they did not drop their tools.”5

Although the failure to set aside our favorite tools may not often have as deadly a consequence as firefighters who fail to do so, we can still be led astray. One common practice is to only apply systems archetypes to an organization’s problems. Newly minted “systems thinkers” may acquire a laminated chart of all the archetypes and scan it to understand all problems. Often an archetype doesn’t apply, so any insight from the chart is most likely unfounded — sometimes it could lead to a bad strategy.

In the case of the Challenger disaster, the standard tool was a having a “solid quantitative case” that the o-rings would be unable to withstand the unusually cold Florida temperatures. The Thiokol engineers who cautioned against giving the mission the green light couldn’t provide the rigorous analysis the managers at NASA required — their standard for the go/no go decision.

“NASA’s Mulloy [one of their senior managers] later argued that he “would’ve felt naked” taking Thiokol’s argument up the chain of command. Without a solid quantitative case, “I couldn’t have defended it.”

The very tool that had helped make NASA so consistently successful, what Diane Vaughan called “the original technical culture” in the agency’s DNA, suddenly worked perversely in a situation where the familiar brand of data did not exist. Reason without numbers was not accepted. In the face of an unfamiliar challenge, NASA managers failed to drop their familiar tools.”6
SUBSTACK MISSION: TEACH WHEN AND HOW TO USE EACH TOOL

Systemic intelligence is all about having the right tools at the right time to understand and influence our complex world. Imagine a master craftsperson’s toolkit, each SysQ tool has a specific purpose. From the quick-to-learn SysQ Questions that can transform any conversation, to the more sophisticated simulation models that can predict system behavior with amazing accuracy. The cool part is that this toolkit is scalable, so everyone can learn to use basic tools to improve their understanding, while those who invest more time can develop deeper capabilities to tackle more complex challenges.

What makes these tools truly powerful is how they work together to build our systemic intelligence. Whether you’re trying to improve organizational performance, address environmental challenges, or solve community issues, these tools provide a structured way to see beyond surface-level symptoms to understand and influence the underlying patterns and relationships that drive system behavior. By mastering these tools, you’ll develop the essential capacity needed to navigate and succeed in today’s interconnected world.

By exploring this substack, you’ll be able to answer:

Why should you use each tool and under what circumstances?

How does that tool work? When applied by an individual? When applied by a team or an organization?

How do you build and deepen proficiency to use each tool?

How do you teach the tools others?

How do you use to facilitate team learning?

“Man must shape his tools lest they shape him.”

—Arthur Miller

Purpose

The Performance Framing Graph is like a visual map that helps us understand the systemic nature of the challenge. It shows us the desired future (vision), what we have now (current reality), and the gap between the two.

This graph is highly useful when we’re starting to solve problems or come up with strategies for adaptive challenges. It can also be helpful for regular problems.

Drawing performance framing graphs helps us think more dynamically — about any issue, simple or complex. They boost our DYNAMIC Thinking capacity.

VALUE & OUTCOME

If done well, it provides you and your team or coalition with the following information:

What’s the current reality of the issue we want to improve?

How did we get here…and what’s the historical trend?

What’s the price paid if the issue remains unsolved?

What’s our ideal future if we could wave a magic wand?

The performance framing graph becomes the starting point for modeling and mapping. As we develop the graph, it often illuminates the structure responsible for performance —it stimulates OPERATIONAL Thinking. We begin to better understand the ecosystem.

HOW Individually Draw two axes. The horizontal will be time. Think about the issue and decide on the most useful time horizon to analyze. How far back does the issue go? How far into the future should we care about it? Put the starting time at the intersection of the axes and the end time at the right end of the horizontal axis.

The vertical axis is the performance measure—your metric or variable of interest. It’s a good idea to normalize this variable (if possible): convert to percentages, averages, or dollars per person, or some other comparable unit.

Draw a dot at the current time. Then, draw a line from the left vertical axis to the dot that represents the current reality. This line can be going up really fast, staying the same, going down super fast, or it could be going up and down in a pattern.

Keep drawing to the right until the final time point. Imagine what the future might look like if we don’t take action. This is the cost of inaction or not solving.

Now, let’s imagine the future. Where would you like this measure to be by the end of the time horizon? What’s the best possible path to get there?

Small Groups

Form small groups (3-5 people) and have each person present their performance framing graph. No discussion or talking of any kind. After everyone shares their graphs, the group should discuss and try to create one graph that they think best explains the issue.
Large Groups
Each group shares their graphs in a large group using the same no talking rule. After everyone’s presented, see if the large group can create 1-3 graphs that they think really capture the essence of the issue. It’s best if there are a few graphs that cover very different aspects of the issue (e.g operations, finances, human resources.)

“Where the world is dynamic, evolving, and interconnected, we tend to make decisions using mental models that are static, narrow, and reductionist." — John Sterman

The claims department of a Fortune 100 financial services company faced several critical challenges. Staff turnover had reached unprecedented levels, with remaining employees exhausted and emotionally drained by an escalating volume of customer claims. The increasing complexity of these claims necessitated more time, particularly with emotionally charged customers, yet hiring efforts proved ineffective in addressing the persistent understaffing problem. The impending retirement of key senior leaders further exacerbated concerns.

A team of executives, managers, and subject matter experts strengthened and applied systemic intelligence (SysQ) to address this complex, dynamic challenge. The team embarked on a SysQ mapping and modeling process. The learning journey’s objective: identify systemic causes of chronic turnover and claims errors and develop effective strategies to improve these workforce dynamics. Through the SysQ Process, they explored the structural reasons for their difficulties in attracting, developing, and retaining a capable workforce.

The cross-functional team developed a comprehensive theory of the claims process — encompassing workflow, staffing, skills, and stress. The resulting ecosystem strategy map improved and integrated existing mental models across the organization; it provided a comprehensive and balcony-level view of the claims process. By applying SysQ Thinking Skills, they significantly refined this working theory of how the claims ecosystem generated processing errors and staff turnover.

A key discovery was the pivotal role of emotional intelligence (EQ) — or lack of it — played in the workforce dynamics. Building EQ was particularly essential for senior staff — it improved the mentoring of more junior staff. High EQ proved essential for both skill development and stress management. Mentoring was needed to facilitate staff learning and increase productivity.

However, the knee-jerk response to turnover — increasing the hiring rate— inadvertently exacerbated the situation. Too many new staff overwhelmed senior leaders’ ability to keep up with mentoring; this slowed the rate of learning by junior staff, thus lowering overall productivity and driving up the workload per staff member. This led to additional stress. And stress further compounded the issue, leading to decreased productivity, increased attrition, and costly errors. This created a nasty reinforcing feedback loop — a vicious cycle.
The comprehensive mapping process underscored the importance of balancing workloads and mentoring in fostering a supportive work environment and enhancing staff effectiveness. Notably, it revealed that the prevalent practice ofrapidly hiring when attrition rates increase actually exacerbates burnout and attrition.

The collaborative mapping process emphasized the significance of proactive measures to address workforce challenges. By providing emotional intelligence training to senior staff to enhance mentoring, implementing stress management training for all employees, and optimizing processes, the company successfully managed stress, reduced decision times, and minimized error rates in claims processing. These interventions led to a more resilient and nimble team, capable of meeting the evolving business demands in a complex environment.
TRADITIONAL PROBLEM-SOLVING APPROACHES ARE INADEQUATE, INEFFECTIVE, AND OFTEN MAKE THINGS WORSE

In today’s interconnected world, organizations encounter increasingly intricate challenges that defy simplistic solutions. For instance, corporations striving to maintain market dominance, government agencies grappling with public health and well-being concerns, and non-profits working to combat poverty all face a common reality: traditional problem-solving approaches are inadequate.

Many organizations adopt a “mechanical mindset,” viewing issues as isolated events to be resolved with straightforward solutions. This approach is effective for routine problems with clear cause-and-effect relationships and established solutions. However, today’s most pressing organizational challenges are adaptive and complex, resisting simple solutions.

As John Sterman (Jay W. Forrester Professor of Management at the MIT Sloan School) states in Public Health Matters, “Where the world is dynamic, evolving, and interconnected, we tend to make decisions using mental models that are static, narrow, and reductionist. Among the elements of dynamic complexity people find most problematic are feedback, time delays, and stocks and flows.”1

Systemic Intelligence (SysQ) provides a powerful framework for comprehending and addressing the most challenging, perplexing, and intractable organizational issues.

The SysQ Process, when rigorously applied with the SysQ mindset, skills, and tools, enables the identification of deeper patterns and relationships that drive organizational behavior. Mental models developed with SysQ include Sterman’s feedback, time delays, and stocks and flows. Understanding the drivers of ecosystem dynamics is crucial for developing actionable and transformative strategies. One of the most effective ways to grasp these drivers is by creating an ecosystem strategy map, just like the claims processing department developed.
HUNTSVILLE’S ECOSYSTEM STRATEGY MAP POWERED ECONOMIC GROWTH

A recent headline by WBHM — NPR News for the Heart of Alabama — highlights the impact of a community-wide ecosystem strategy mapping effort.

Mississippi wants more tech jobs. Here’s what it can learn from Alabama’s tech success
“The Huntsville/Madison County Chamber of Commerce said about 15 years ago it had that same problem with retention. Companies were drawing in young talent from across the country for internships and entry levels programs, but they would leave for bigger cities.

The reason? They were bored.

So the city and chamber worked to find out what would entertain and stimulate young tech talent and bring in those features. Huntsville now has an outdoor shopping mall with restaurants, a minor league baseball team, a food truck festival and an ice-skating rink in the winter. Lucia Cape, the head of economic development at the Huntsville/Madison County Chamber of Commerce, said the efforts to improve the city’s quality of life worked and led to more talent sticking around.

‘The soft stuff is almost harder because you can’t just put money on it and contract it out,’ Cape said. ‘You have to be responsive and it has to be, you know, genuine.’

O’Mara agrees that quality of life is essential for keeping and attracting the talent needed to spur innovation.

‘Part of what made Silicon Valley and other places thrive is not just investment in tech or engineering,’ O’Mara said. But investment in social infrastructure, having strong public schools, having thriving neighborhoods.’”

The Chamber of Commerce identified the “soft stuff” by first developing an ecosystem strategy map. During the mapping process, the cross-sectoral team came to an uncomfortable realization: their current tech strategy of importing more senior-level STEM professionals was actually discouraging recent college graduates from staying in Huntsville.
Instead, the map revealed that by building a community with a strong arts and culture scene — attributes that coastal tech centers possessed more readily than Huntsville — they could attract, retain, and nurture young STEM professionals. This approach created a more sustainable tech hub. Consequently, Huntsville became one of the most attractive communities in the US, as recognized by US News & World Report.

A more complete map is below.
MOVING TO A HIGHER PERFORMANCE ORBITAL

Perhaps the most adaptive challenge an organization can attempt is the desire to transition from less than optimal performance to a level of high performance, akin to reaching a high-performance orbital. Organizations, communities, and nations perform at varying levels in terms of their collective health and well-being. Some organizations consistently innovate and sustain growth; others stagnate or even face setbacks, leading to their demise. Although this analysis applies equally well to communities and countries, let’s now focus on organizations.

Every organization has a current level of performance aligned with its mission. This encompasses various aspects, such as:

Aligning with the organization’s purpose — How effectively is the organization achieving its intended goals?

Customer service — Are customers experiencing exceptional service or subpar experiences?

Growth and expansion — Is the organization growing and increasing its reach and influence, or is it stagnant?

Employee well-being — Are employees fulfilled and nurtured, or are they miserable?

Overall impact — Is the organization nurturing people, creating public value, and generating a profit?

Consider an organization that is currently functioning at a suboptimal level. While it may not be performing abysmally, everyone agrees that there is substantial room for improvement. Customers and employees could experience greater satisfaction, and the organization could have a more significant global impact.Such an organization is stuck in a lower performance orbital. To achieve greater success, leadership aims to lift the organization’s performance to a higher level. They might establish a desired performance standard, which serves as a vision of an ideal future, by setting a BHAG. A BHAG stands for a Big Hairy Audacious Goal, and it was first introduced in the book Built to Last: Successful Habits of Visionary Companies by James Collins and Jerry Porras.2 Collins and Porras urged organizations to set their BHAG as a long-term objective to fundamentally alter the very essence of a business’s existence. Huntsville’s BHAG was to become the most thriving tech hub in the United States.

An organization or community becomes trapped in a lower performance trajectory due to the way it organizes and deploys its resources — where and how it allocates attention and assets. The organization’s organizational structure, resource allocation practices, rules, policies, strategies, and norms and culture all stem from leadership’s and employees’ mental models about the organization and the environment (ecosystem) it operates in.
Leadership and staff develop a strong attachment to “the way it is,” which solidifies the strategy and culture. Individuals become ensnared by their mental models. The only path to elevating the organization to a higher performance trajectory is to challenge and transform those mental models. SysQ is the capacity required to facilitate this transformation — it promotes what is known as Double-loop Learning. Both the financial services company and the Huntsville metropolitan region underwent a remarkable transformation precisely by using ecosystem strategy maps to Double-loop the problem.
A FAILURE TO QUESTION MENTAL MODELS

Early in the 1990s, I worked as an OD consultant for the Bureau of Engraving and Printing (BEP), a government agency responsible for currency production. At that time, the BEP also handled the printing of postage stamps. BEP stamps were renowned for their artistic quality and had garnered immense admiration from the numismatic community for over a century. This artistic approach and meticulous attention to detail were a source of pride for the engravers and printers at the BEP.

However, during my tenure, some for-profit printing companies began producing lower-grade (less appealing to collectors) stamps with a unique feature: self-adhesion. The prevailing leadership mental model — and those responsible for engraving and printing — was self-adhesive stamps would have limited demand due to their inferior visual quality.

On June 10, 2005, less than a decade after leaving to work for a system dynamics consultancy, I discovered that the BEP had printed its last stamp. Self-adhesive stamps had become the industry standard, and the preference for the aesthetically pleasing but inconvenient work-of-art stamps diminished in favor of the practical self-adhesive option. The public not only hated the inconvenience of licking stamps, they hated the taste!

Ironically, the BEP had been the pioneering organization to test self-adhesive stamp printing in the 1970s. However, leadership had made the decision not to market them due to the prevailing mental model that undervalued the potential of self-adhesive stamps.

This phenomenon is not unique to the BEP. Numerous other organizations have had great ideas but failed to pursue them because of their collective mental models that undervalued the potential of those ideas. For instance, Apple products are filled with innovative ideas that originated from Xerox, but these ideas were never fully implemented because Xerox’s mental model positioned it as a printing company rather than a computer company. Another ironic twist is that Xerox itself emerged from IBM innovations that were never marketed because IBM’s identity was as a computer company rather than a printing company.
MONEYBALL — CHALLENGING AND CHANGING ASSUMPTIONS

In the 1970s, the Oakland A’s found themselves in a dire situation, struggling to climb out of the basement of the American League West division. Their financial constraints made it impossible for them to compete with teams like the New York Yankees, who could afford to acquire the best players in the major leagues, including all-stars. Despite their best efforts, the A’s were unable to rise near the top of their division, let alone reach the league championship series.

The story of Billy Beane, the A’s general manager, and his team was brilliantly portrayed in Michael Lewis’s book, Moneyball: The Art of Winning an Unfair Game. Wikipedia provides a comprehensive account of Billy and his team’s journey.

“The central premise of Moneyball is that the collective wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over the past century is outdated, subjective, and often flawed, and that the statistics traditionally used to gauge players, such as stolen bases, runs batted in, and batting average, are relics of a 19th-century view of the game. Sabermetrics and statistical analysis had demonstrated, for example, that on-base percentage and slugging percentage are better measures of batting. The Oakland A's began seeking players who were "undervalued in the market” — that is, who were receiving lower salaries relative to their ability to contribute to winning, as measured by these advanced statistics.

By re-evaluating their strategy in this way, the 2002 Athletics, with a budget of $44 million for player salaries, were competitive with larger-market teams such as the New York Yankees, whose payroll exceeded $125 million that season. The approach brought the A's to the playoffs in 2002 and 2003.”3
Billy Beane’s success in making the A’s competitive is an example of Double-loop Learning, a concept developed by Chris Argyris. By challenging and altering conventional assumptions about what constitutes high-valued baseball players, Billy Double-looped the problem of poor performance.
DOUBLE-LOOP LEARNING — WHY IT’S CRUCIAL TO ACHIEVING HIGHER PERFORMANCEWhen confronted with problems, issues, and challenges, we become motivated to address them. We recognize the gap between our aspirations and our current reality; the gap motivates us to analysis and action. In the face of such a gap, we must devise a plan, make a decision, or develop a strategic approach.

The strategy we construct doesn’t come from the ether; it’s a product of our mental models of reality. M. Scott Peck aptly describes these models as our mental maps of the world. These mental models represent the causal assumptionswe employ to make sense of — to understand — how the universe works. They must include our assumptions about the underlying causes of the problem: without a clear understanding of the causes, we are unable to develop an effective strategy.
Therefore, we run problems through our existing mental models of those challenges (second column from the left in the diagram). By drawing upon our assumptions about the causes of the issue, we determine the most suitable plan or strategy. Subsequently, we execute this strategy, taking concrete actions (the second column from the right in the diagram).

Our actions and behaviors lead to tangible results, outcomes, and consequences (the far right column). If we have the right strategy and execute it effectively, the outcome will be positive: we successfully resolve the issue and narrow the gap between our aspirations and reality.

The diagram and accompanying text provided in Conversational Capacity by Craig Weber offers further insights into this process.

“Obviously, proactive double-loop learning is the preferable option, and getting people with differing and conflicting views to lean into their differences is essential. ‘If people don’t engage across the divide of their differences, there is no learning,’ says Ron Heifetz.’ People don’t learn by looking in the mirror. They learn by talking with people who have different points of view.’”4

They learn by collaborating with others to identify, challenge, and modify their mental models. However, one challenge is that it can be difficult to recognize our own mental models. They become like the water we swim in; when asked about our mental maps, we might respond, “What water?” This is why ecosystem strategy mapping — surfacing and challenging our mental models — is a valuable tool.
WE MUST RETHINK…NOT REACT

As the previous diagram illustrates, our typical response to a challenge is to react. We don’t question our understanding of the world; we simply continue as if we know the exact cause of the issue. We modify our strategy only slightly. For instance, if sales still decline despite reducing prices, we might lower the price further. In many cases, lowering prices can boost sales in the short term; however, this strategy can also lead to long-term sales declines if our capacity is insufficient to meet the increased demand, resulting in lower customer satisfaction.

This type of strategy adjustment — tweaking around the margins without questioning our mental models — is what Chris Argyris labeled Single-loop Learning. Single-loop Learning is our default mode of operation. It serves us well in many situations — for example, when we feel our hand burning, we know it’s from the stove we’ve placed it on, so we simply remove the hand.

However, Single-loop Learning — although efficient because it requires minimal inquiry and reflection — is often insufficient to solve problems or develop strategies in the face of adaptive challenges. Adaptive challenges, by definition, defy routine problem-solving. We don’t truly understand what creates those challenges, so how can we effectively solve for them?
THE FINANCIAL SERVICES COMPANY DOUBLE-LOOPED THE PROBLEM

This is where it’s crucial to reevaluate our mental models: we need to adopt a Double-loop approach to problem-solving.

Double-loop Learning involves first ceasing our reactive behavior and then initiating reflective thinking. We must examine, challenge, and change our assumptions about the issues and the cherished beliefs we hold dear that hinder their resolution. We identify the gaps between our mental models and the real world; if those gaps are incorporated, our mental models would become more congruent with reality.

A more pertinent question arises: what are our misconceptions about the causes of the challenge that are simply incorrect? These misguided beliefs might inadvertently exacerbate the problem.

For instance, the financial service company’s impulsive decision to hire more employees actually worsened the situation — contributing to a more rapid turnover rate and heightened workplace stress.
ECOSYSTEM MAPS — BUILT WITH SYSQ — ILLUMINATE AND TRANSFORM OUR MENTAL MODELS

The financial services team and their extended organizational partners developed actionable insights by creating the claims ecosystem map. A less-than-obvious truth of the SysQ Process is that actionable insights aren’t generated in a single, two-hour meeting filled with PowerPoint slides. Instead, insight — Double-loop Learning — requires an extended process involving multiple meetings where a team or coalition collaboratively builds a map (and often utilizes other SysQ Tools). Such meetings require effort, can be emotionally charged as the team challenges their understanding of reality, and work best if spaced out enough to provide ample individual reflection.
In the claims process, it was during the third of five half-day sessions that the mapping team had an “Aha moment” they realized their initial hiring strategy was exacerbating the problem. It took another session before they also identified the need to incorporate emotional intelligence as a crucial aspect of the workforce strategy.

Similarly, it took several mapping sessions for the Huntsville coalition to recognize that their strategy of importing seasoned STEM professionals was hindering their ability to attract and retain the workforce of the future. They also identified the “soft stuff” they needed to implement, such as creating a vibrant arts and culture scene.

Neither the financial services company nor the community coalition would have achieved Double-loop insights in a single hour PowerPoint presentation from external experts. The team would likely have dismissed external experts by claiming they lacked understanding of the organization’s or community’s dynamics.

Double-loop Learning occurs during collaboration. It requires surfacing, exchanging, questioning, transforming, and integrating mental models. It’s impossible to achieve Double-loop learning simply by wishing for it within the confines of a single session. There’s no “watch me wave my wand and poof…the magic happens” that will occur. Attempting to generate insight in a single session leads to getting stuck on a Single-loop hamster wheel.

‘A slow sort of country!’ said the Queen. ‘Now, here, you see, it takes all the running you can do, to keep in the same place.

If you want to get somewhere else, you must run at least twice as fast as that!’

— Lewis Carroll, Alice in Wonderland

I’m sure you can relate to feeling like your organization, coalition, or clients are stuck on a hamster wheel, as if you’re Alice in Wonderland running faster and faster to stay in the same place.

Double-loop learning is essential to stepping off the hamster wheel. SysQ and an Ecosystem Strategy Mapping Process can facilitate Double-loop learning — lifting an organization or community to a higher performance orbital.
BUILDING ECOSYSTEM STRATEGY MAPS FACILITATE DOUBLE-LOOP LEARNING

Elevating to a higher performance orbital system requires collaboration and dialogue among and across organizational leadership and contributing stakeholders. To guide a successful transformation, it’s crucial for groups across the ecosystem — departments within the organization and partners outside — to comprehend and visualize how the system functions. However, our current understanding often falls short, leading to flawed assumptions that exacerbate our challenges.

Ecosystem mapping is a valuable tool to gain a comprehensive operational view of the system, enabling us to make informed changes and enhance its performance. By applying the SysQ Process and Skills in constructing ecosystem strategy maps, we significantly increase the likelihood of achieving Double-loop Learning.

1

Sterman, John. Learning from evidence in a complex world. Am J Public Health. 2006 Mar;96(3):505-14. doi: 10.2105/AJPH.2005.066043. Epub 2006 Jan 31
2

Collins, J., & Porras, J. I. (1994). Built to last: Successful habits of visionary companies. HarperBusiness.
3

https://en.wikipedia.org/wiki/Moneyball:_The_Art_of_Winning_an_Unfair_Game
4

Weber, Craig, Conversational Capacity: The Secret to Building Successful Teams That Perform When the Pressure Is On