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Resources
From decades of building systemic intelligence in organizations and communities, we’ve assembled resources to help you tackle complex problems more effectively. These videos, articles, and interactive tools are organized into four essential categories that together build your capacity to create lasting change. Each set develops a critical aspect of SysQ—from shifting your mindset to see systems differently, to following a proven learning process, to strengthening specific thinking skills, to mastering visualization tools that make the invisible visible. Whether you’re addressing organizational challenges, community issues, or global concerns, mastering these complementary resources will transform how you and your team approach complex challenges and implement high-leverage solutions.
mindset
System Transformation
MINDSET
Why lasting change begins with improving mental models, and how leaders facilitate the learning to improve them.
WHITE PAPER
In the spring of 2002, the Oakland Athletics walked into a season they had no business winning. The previous October, after winning 102 games and pushing the New York Yankees to a fifth and decisive playoff game, Oakland watched three of its best players walk out the door. Jason Giambi, the reigning American League MVP, signed with the Yankees. Johnny Damon, the leadoff hitter, signed with the Red Sox. Jason Isringhausen, the closer, signed with the Cardinals. The reason was money. Oakland's payroll for the coming year would be roughly $40 million. The team they were chasing would spend more than $125 million. By the standards of the sport, the math was finished. Owners counseled patience. Sportswriters told their readers Oakland was rebuilding.
They should resign themselves to a losing season. And then it didn't happen.
That summer the Athletics won 103 games. They won twenty in a row, the longest winning streak in the history of the American League. They went back to the playoffs. They did it spending less than a third of what their rival spent.
What Billy Beane and his assistant Paul DePodesta had done was abandon the mental model the sport had used to judge players for more than a century. For a hundred years a player's value had lived in the heads of professional scouts: the look of him in a uniform, the smoothness of his swing, the famous "good face." Bill James had spent twenty-five years showing, with patient arithmetic, that the most valuable thing a hitter can do is simply not make an out. Baseball ignored him. The men who ran the game already knew what a ballplayer looked like.
Within a few years Boston had hired Bill James and won its first World Series in eighty-six years. Within a decade every front office in the sport had been remade in Oakland's image. The system the A's had been competing inside of in 2001, the one in which a $40 million franchise cannot beat a $125 million franchise, was no longer the system anyone was playing in. A different game had begun.
Systems don't slide. They jump.
Barry Richmond, who taught at Dartmouth and later founded isee systems, had a particular way of describing system performance, and he took it from physics rather than from management. Systems do not perform on a smooth gradient. They perform in orbitals: stable bands of behavior held in place by the forces that shape them. A system fluctuates inside its orbital, and it does not drift out of one without something significant changing.
Organizations behave this way. So do agencies, coalitions, school districts, and entire fields of practice. Whatever else these systems differ on, they share the physics.
A lower orbital is not a sign that nobody is trying. The 2001 Athletics were trying extremely hard. Portugal, facing a heroin crisis that had reached nearly one percent of its population, was trying hard too, applying enforcement and prosecution and imprisonment year after year while the numbers got worse. So is every agency, district, and coalition currently stuck in the orbital it wants to leave. A lower orbital means the structure is producing the result it was built to produce. Another quarter of incremental effort, another round of best practices, another well-intentioned initiative: these add energy, but rarely the right kind, and rarely enough. The system absorbs the bump and settles back where it was.
What actually holds an orbital in place
The temptation is to answer that question with a list of factors. Politics. Budgets. Culture. Inertia. Resist the list, and ask instead how the thing actually works.
The visible answer is structure: rules, policies, budgets, org charts, procurement procedures, performance metrics, funding cycles. That machinery is real, and it is not the deepest cause. Beneath it sits the shared mental model the structure expresses. It is the collective answer to "what work are we actually here to do?", "what do the people we serve actually need?", "what counts as quality?", "who gets to decide?" Those answers are usually unspoken and almost never examined. They are also usually correct enough to have produced the success the system once enjoyed. That is what makes them so hard to see, and so hard to change.
Chris Argyris gave the two kinds of learning their names. Single-loop learning corrects the action: something goes wrong, you adjust, you patch the procedure, you try harder. A thermostat learns this way, and never once asks whether 68 degrees is the right temperature. Double-loop learning reaches past the action to the model producing it.
That is what Beane did in Oakland. It is what Portugal's national commission did in Lisbon in 1998, when it concluded that the country's failure ran deeper than bad enforcement. The model underneath the laws was wrong: addiction as crime, users as criminals, punishment as cure. Portugal reclassified personal possession as a public health matter in 2001 and moved the money from prosecution into treatment. Over the following decade, HIV infections among drug users fell by more than 95 percent, and the share of the prison population serving time for drug offenses fell from 44 percent to 24 percent. No new medicine was invented. What changed was the set of assumptions governing how the problem was defined and who owned it.
Double-loop learning is uncomfortable work. It exposes the possibility that people who spent careers mastering the existing model have been mastering the wrong thing. It is also the only kind of learning that has ever moved a system from one orbital to another.
Why so many transformations don't take
Most of them were attempted as routine work. Ronald Heifetz's distinction is the one to keep. A routine problem is one where the know-how exists, the authority to act is clear, and the path from problem to solution can be drawn in advance. Routine problems can be enormously complex. Landing an aircraft. Performing surgery. Running a national election. Their complexity is the kind that yields to expertise.
An adaptive challenge is different in kind. Nobody yet knows what the path is, and solving it requires people to change their values, habits, loyalties, and ways of working. The expertise that produced past success may actively obstruct future success. Beane's challenge in 2002 was adaptive. The routine version of it ("assemble a roster on $40 million") had a known answer that everyone in the league understood, and that answer was: lose.
When an adaptive challenge gets handled as a routine one, the pattern is familiar. New software gets installed. New procedures get documented. A strategic plan circulates, committees get renamed, the org chart gets redrawn. The mental models don't shift, the loyalties don't move, and eighteen months later the system is producing what it produced before. The diagnosis afterward is almost always some version of "we tried, it didn't take." The work was misdiagnosed at the start.
What's in the paper
System Transformation: An Introduction is the opening chapter of the System Transformation Guide, and it makes this case at full length: Oakland and Portugal in detail, the Bureau of Engraving and Printing holding on to steel-plate engraving while the world switched to peel-and-stick, Xerox inventing the personal computer and failing to recognize it as something Xerox would ever sell, and Laurence Gonzales's mountain climbers, who die on familiar mountains because they expect conditions to match their last successful ascent. It takes up Jennifer Pahlka's Three Horizons distinction between reform that builds the next system and reform that quietly extends the life of the failing one. And it lays out the Five Phases the guide is organized around:
- Frame the Right Problem: separating adaptive challenges from routine ones, and drawing the trend you're trying to bend before anyone argues about strategy.
- Work Across Boundaries: who actually has to be in the room, and the conversational capacity to keep them there.
- Improve Mental Models: surfacing assumptions, recognizing the archetypes that make well-intentioned interventions backfire, and building a shared causal picture a group can argue with.
- Apply Action Learning: turning that picture into a testable hypothesis, so you find out early whether reality agrees.
- Build Adaptive Leadership Capacity: the phase most guides skip, and the one that decides whether any of this outlives you.
The paper is free, it runs about 30 pages, and you can read it in an evening.
Then come work it with other practitioners
Reading the argument is the easy part. The hard part arrives on a Tuesday morning, when you're standing in front of fourteen people who report to different bosses, hold different theories of the problem, and have already survived two failed transformations. Somebody has to ask the question that surfaces the assumption nobody has said out loud. Somebody has to keep the room in the inquiry at the exact moment it wants to bolt for a decision. That somebody is you, and no argument, however well made, tells you what to say at 9:15.
We acquire the skills to lead transformation by doing the work on problems we care about, often poorly at first, with somebody to compare notes with. Pilots don't learn to fly by studying aerodynamics. They log hours.
The Orbital Shift Lab is where those hours get logged. It's the practitioner community for consultants, facilitators, OD professionals, and internal change agents who run these sessions for a living or are moving toward it. Members bring a real challenge, get help framing it, come back with what happened, and find out how somebody in a county health department solved a problem that looks nothing like theirs and turns out to be the same problem underneath. Every member gets a starter set of facilitation exercises free, so you can run one with a real group before you pay for anything. Subscribing members get the complete guide, every exercise deck in both self-guided and facilitated modes, the full facilitation layer, live practice sessions, and direct access to me.
If you'd like to learn more about how to guide system transformation...
Join Us
Our mental models set our performance orbital. They do more than hold our systems in place. They hold us in place. So the work begins there, and it begins better in company than alone.
If your work sits in public health, climate, education, democracy, or humanitarian response rather than in consulting or OD, the sister community is The Flourishing Constellation at flourishingconstellation.com. Joining is free, and so is the starter set of exercises.
If you'd like to learn more about how to support system transformation across society as we navigate a range of interconnected, systemic issues...
Process
The Process Redux: The Learning Journey Takes the High Road
PROCESS
Attribute of Process
Start with the whole, then earn the details
About
Most analysts do it backwards. They dive straight into the data, pull apart the pieces, and try to build understanding from the bottom up — only to find themselves buried under complexity they can't escape. Barry Richmond called a different route: the high road. The systemic intelligence process begins at altitude, with boundaries drawn wide enough to hold the full performance story. Stocks, flows, a handful of loops — the simplest possible structure that could plausibly generate what you're seeing. Then, and only then, do you add detail — one layer at a time, just enough to close the gap between model behavior and reality. The discipline isn't knowing when to go deeper. It's knowing when to stop. The moment your map can reproduce the performance you're trying to understand, you have what you need. Not a perfect map. A useful one.
About
When I sit down with a team — a leadership group, a program staff, a community coalition — someone almost always says it within the first hour: "The devil's in the details." They mean it as a warning, or sometimes as a point of pride. Either way, it's usually where the conversation wants to go next.
I push back...gently.
Diving straight into the details tends to produce the opposite of what people are hoping for. More confusion, not less. More overwhelm, not more clarity. And almost never a real grasp of the structural forces actually shaping performance — whether that performance is a problem we're trying to solve or a success we're trying to understand and sustain.
What we need first is a useful mental model. By useful, I mean a model that explains why performance looks the way it looks. One that helps us visualize causes, not just feel the symptoms.
That's what the SysQ Process is built to do. We start at altitude — the high-level view of the system — and then add one layer of complexity at a time. A stock here. A feedback loop there. A delay we hadn't accounted for. Each addition earns its place by helping the team see a larger force at play that the previous picture couldn't explain. The details come back in eventually — but now they sit inside a structure that makes them make sense.
“Two roads diverged in a wood, and I — I took the one less traveled by, And that has made all the difference.” — Robert Frost
The Road Less Traveled
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 (Dartmouth College) while teaching system dynamics to his undergraduate students. 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.
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Skills
DYNAMIC Thinking
SKILLS
Attribute of Skills
Life is dynamic and ever-changing. Understanding how and why things change over time is dynamic thinking.
About
When I work with teams, the other common refrain is the number on the page. "Revenue's down 18% this quarter." "Project times are getting longer." "Our medical costs are higher than the benchmark state's." Someone points at the number, and the room starts reacting — layoffs, travel freezes, a new IT system, an email to the state that looks better.
Then I ask a different question: what does this look like over time?
Usually the picture changes. Revenue that looked like a collapse turns out to be the winter side of a seasonal swing. The project times that felt like they were getting longer have actually been improving for a year — I just remembered the two bad ones most recently. The benchmark state with the lower costs is on a steep upward trajectory; the one we were about to pass over is quietly bending its curve down. Same numbers. Different story. Completely different set of moves worth making.
That's DYNAMIC Thinking. Instead of reacting to an event — a quarterly result, a single data point, the thing that just happened — we widen the time horizon until the pattern shows up. A trend. A seasonal rhythm. A delay between cause and effect. An S-curve that's about to flatten. Once the shape is visible, the mental model shifts from react to explain — and explanations are what good strategies are built on.
The deeper payoff is what trends do to a group. Reactive thinking narrows people; generative thinking opens them. The moment a team draws the curve together — not the number, the curve — they stop arguing about what to do and start asking why the system is producing this shape in the first place. That's the question worth answering. It's also the only question whose answer tends to hold up six months later.
"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?
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Travel restrictions?
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Training reductions?
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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:
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Hiring a consulting firm?
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Closing part of the business?
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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.
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tools
Seven rules, two kinds of nurse, and almost no one gets it right
You are the HR Director at General Hospital, and the nursing workforce is yours to manage.
The system you are responsible for runs on exactly seven rules. No hidden assumptions, no trick wording.
- There are two kinds of nurses, experienced and inexperienced. Only two.
- For months the hospital has held the same number of experienced nurses.
- Over that same period, a steady number of experienced nurses have quit each month.
- Policy is to hire one inexperienced nurse immediately for every experienced nurse who quits.
- Every inexperienced nurse completes a six-month training program and then becomes experienced.
- The training is good enough that no inexperienced nurse ever quits before finishing it.
- Three months from now the quit rate rises once, permanently. From then on, experienced nurses quit at that higher rate indefinitely.
That is the whole system. Seven rules, two categories.
A real workforce has dozens of moving parts and layers of policy stacked on top of them. This one should be easy.
So: three months from now the quit rate jumps and stays there. What happens to the number of experienced nurses on staff?
Don't overthink it. Sketch the shape, trust your instincts, and write it down before you read any further. The rest of this only works if you have committed to an answer you can be wrong about.
Here is what comes back when I run this with a room of thirty people.
Eight patterns, and the disagreements are not about magnitude. Some people expect oscillation. Some expect a slide that levels off. Some expect a dip and a full recovery. Some expect a decline all the way to zero. Those are different theories about how the system works, and in a real hospital they would produce different staffing decisions, different budgets, different arguments with the CFO.
If thirty thoughtful people cannot agree about a system with seven rules and two variables, consider what happens in a room arguing about a system with two hundred.
Now go run it.
Stop here if you haven't run it yet.
At month three the quit rate rises. The experienced nurse stock declines steadily for exactly six months, and then levels off at a new, lower number.
Most people do not predict that on the first try. In sessions with senior engineers, executives and seasoned managers, almost no one does. Mental simulation runs out sooner than almost anyone expects, and no amount of domain expertise extends it.
The logic, once you can see it:
- When quitting jumps at month three, hiring jumps immediately to match it. That is the policy, working exactly as designed.
- Those new hires will not be experienced for six months. So for six months, more people are flowing out of the experienced stock than are flowing into it.
- Whenever outflow exceeds inflow, a stock falls. That holds for any accumulation, anywhere.
- At month nine the first wave of new hires finishes training and starts arriving. Inflow matches outflow again, and the level holds at its new lower value.
The system does not oscillate. It does not recover. It settles lower, and it takes exactly six months to get there.
The picture is what makes the argument. Two stocks, three flows, one policy. The inexperienced nurse stock is drawn as a conveyor, which is the modeler's way of saying that everyone who steps on takes exactly six months to come off the other end. That delay is doing most of the work here, and it is nearly invisible when the same seven rules are written out as sentences.
Words are where the ambiguity comes from. We describe one relationship at a time, and we cannot hold two accumulations and a six-month delay in mind at once. What we end up with is a partial map, accurate in pieces and never assembled into something that runs.
If you want experienced nurses to hold steady, the rate at which people become experienced has to match the rate at which they quit. If you want trust in a community to grow, trust has to be built faster than it is lost. We usually end up working on one side of a stock when the answer is sitting on the other.
Which side of the stock am I working on? That question is worth asking about anything you are trying to move: headcount, cash, morale, burnout.
If you want the longer version, I walk through this exercise end to end in about eleven minutes, including what happens to people's guesses once they can see the map.
The Orbital Shift Lab is where people practice this on their own work. Members bring a system they are responsible for, map it, and find out where it surprises them. Joining is free.
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