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

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

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

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

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

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

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

The Anatomy of Problem-Solving

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

Phase 1: Sense-making

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

Phase 2: Solving

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

Phase 3: Implementing and Learning

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

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

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

Understanding Double-Loop Learning

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

The process works like this:

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

Moneyball: The Power of Double-Loop Learning

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

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

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

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

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

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

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

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

Transforming Huntsville into a Tech Hub

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1. Sensemaking

2. Solving

3. Implementing & Learning

A. SENSEMAKING

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1. Innovative and Overlooked

2. Counterintuitive

3. High-leverage

Innovative and Overlooked

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

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

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

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

Counterintuitive

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

High-leverage

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

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

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

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

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

The 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