Daring Experiment

People Express Airlines was a daring experiment, going from startup to over $1 billion in revenues in less than four years — the fastest growing company in the history of America at that time. It offered deep discount fares, innovative human resource policies such as employee ownership, self-supervised teams and job rotation, and a charismatic founder, Don Burr. But the interaction of these strengths produced a disaster. In the first six months of 1986 the company lost $132 million, and in September of that year it was swallowed up by Texas Air.

Although People Express no longer exists, it is possible to reenact the scenario described above by climbing into the cockpit of a new tool called a management flight simulator. A management flight simulator, similar to the ones used to train pilots, allows managers to test the outcome of different policies and decisions without “crashing and burning” real companies. It is made up of a system dynamics model that has been changed into an interactive game by the use of an interface (for more information on software for creating interfaces, see this issue’s Viewpoint).

The People Express Management Flight Simulator was the first of its kind. The simulator was developed by Professor John Sterman at the MIT Sloan School of Management using STELLA. Ernst Diehl, president of MicroWorlds, Inc., developed the interface software. Introduced in 1988 as part of an orientation workshop for incoming Sloan School master’s students, the simulator has since been adopted by dozens of universities, including Harvard Business School, Stanford Law School, London Business School, and the University of Texas. It has also seen wide use in management training at all levels of management in dozens of major corporations.

‘Tangled Webs’

According to Sterman, the system dynamics model behind the People Express Simulator integrates the structure of People Express — its operations, human resources, organizational structure, and philosophy — with the structure of the US air travel market and competitive environment of the early 1980s. In addition to “hard” variables such as fleet size and financial data, the model includes such “soft” variables as the effects of overtime on morale, and the effects of service quality on the airline’s reputation.

The People Express Simulator workshop puts each participant in the role of the top management, where they must “wrestle with the tangled web of cause-and-effect relationships that create corporate dynamics,” as Sterman describes it. But in addition to real-time problem solving, the workshop offers participants the chance for reflection and inquiry, an opportunity that is seldom afforded to managers during their hectic daily schedules.

Although various instructors have developed different ways to use the simulation, Sterman says he begins his workshop with a short review of People Express’ history and a screening of a 1985 video clip in which Don Bun speaks about his philosophy and vision for the company. The video invariably stimulates vigorous discussion, says Sterman, in which participants offer their own theories of why the company failed and suggest alternative strategies.

In the Pilot’s Seat

Then comes the real test of those theories. Participants break off into groups of two to three players to become the management team of their own airline. After drawing up their strategy for the company, they make five decisions on a quarterly basis: how many planes to buy, how many customer service managers to hire, how much to spend on marketing, what fare to charge, and what scope of services to offer. After entering their decisions, participants receive reports which keep track of information such as cash flow, stock price, and employee morale. In the course of a few hours, teams are able to run the simulator several times, testing and debating various strategies. “Nearly all teams experience bankruptcy at least once,” Sterman estimates. “But by the end of the session nearly all of them find a successful strategy.”

The discussion that follows the game is “much deeper than the case discussion at the beginning of the workshop,” he notes. “Participants are more aware of the side-effects, counter-reactions, time delays, and trade-offs they face.” For example, participants often suggest during the initial discussion that People Express could have succeeded if it “maintained service quality.” But after playing the game, show a better understanding of the time lags and counter pressures that may frustrate quality improvement programs. They discover that even if service improves, People Express’ low fares attract still more would-be customers, causing overbooking, more busy signals on the reservation lines, and other problems which drag service quality down again.

The success of the People Express Simulator, Sterman believes, is in part due to its experiential format. “Participants are rarely able to identify and integrate all of the subtle dynamics of the company without experiencing them as they run their own airline in the simulator.”

Insight vs. Hindsight

Interestingly, Sterman created the simulator model before People Express’ dramatic collapse, suggesting the possibility of using flight simulators to pinpoint problems or lapses in an organization before they become life-threatening. In contrast to a typical case study that is retrospective in nature, management flight simulators encourage taking a prospective view of a case. Instead of stopping at the question “what happened?” players test out alternate strategies that pursue the question “what could happen?” By formulating and testing many different strategies, players gain greater insight into the case.

That type of thinking is at the heart of other flight simulators. The Hanover Insurance Claims Learning Laboratory (Systems Thinking in Action, April/May 1990) features a flight simulator that was created to help claims managers reevaluate the way they allocate resources in the departments. Other management flight simulators currently under development revolve around specific issues — service quality management, real estate development, and new product lifecycle management — rather than companies.

Learning from Prototypes

“In order to learn how to create learning organizations, managers need to study prototype organizations — ones that have gone a little way towards become learning organizations but have failed.” Peter Senge argued in his book The Fifth Discipline. People Express Airline, he said, was one such prototype. It had some of the components of a learning organization — shared vision and a commitment to personal mastery — but no concept of the whole system. With the aid of tools like management flight simulators, managers can now study and learn from innovative organizations such as People Express.

For information about obtaining a copy of the People Express Management Flight Simulator, contact John Sterman, E52-562, MIT Sloan School of Management, 50 Memorial Drive, Cambridge, MA 02139, (617) 253-1559., FAX (617) 253-6466

Further reading: Peter Senge and Colleen Lannon, “Managerial Micro Worlds,” Technology Review, July 1990. Jolie Solomon, “Now, Simulators for Piloting Companies,” Wall Street Journal, July 31, 1989.

The post “Flying” People Express Again appeared first on The Systems Thinker.

In an attempt to capture learnings, we make our best efforts to take time out to reflect. For example, we may institutionalize project “postmortems,” or have an internal consultant study and document lessons learned. Or, we may focus on the “front end” by conducting training in balancing inquiry and advocacy, understanding systems archetypes, or engaging in dialogue.

All of these approaches have the potential to shift us out of our reactive ruts. But they do not automatically become part of an organization’s working habits; we must devote time, resources, and infrastructure to tend to and nurture them. More often than we care to admit, “lessons learned” collect dust on the shelf because we just don’t have the time to translate others’ hard-won insights into our next high-priority project. And sometimes our new reflection skills and techniques are just “out of sync” with our workflow — we don’t have time for them when we need them, and when we do have time, other priorities beckon us.

THE EMERGENT LEARNING PROCESS

“Learning from experience” is mostly done retrospectively. Engaging in emergent learning means taking an intentional, evolutionary approach to learning “through” experience — by conducting iterative experiments using a group’s real work as the experimental field. Taking this approach often produces new and powerful learning simultaneously to making headway on key business issues

Emergent learning practices offer us a pragmatic, low-overhead approach to making the time and space for organizational learning habits to grow. In the process, they help teams and business units develop “islands of mastery,” or growing areas of expertise in their increasingly complex working environments. And the practices help sponsors identify incremental wins and build a business case for the value of organizational learning.

What Is Emergent Learning?

Emergent learning is the ongoing exploration of a locally defined arena of action through intentional, iterative learning experiments. The goal of emergent learning is for a group of people — perhaps a team or business unit — to master performance in arenas of key importance to their business. The focus of these learning experiments might be improving the organization’s ability to fulfill its basic mission (such as, for a police department, reducing crime), managing escalating costs, creating successful strategic alliances, or bringing projects in on time and under budget. An experiment might involve comparing two recent strategic alliances, forming conclusions about these experiences, and testing the conclusions on a new project. Or for a group of project managers, an experiment might mean getting clients involved in projects at different times and in different ways to see how these variables affect the decision-making process But in each case, the two characteristics that distinguish emergent learning from how we usually approach simply “learning from experience” are that it is iterative and intentional. Teams repeat emergent learning experiments in parallel or in close enough succession to be able to compare and contrast performance from instance to instance. They purposefully define experiments in advance of the experience, not in retrospect, as in a “post-mortem.” These intentional iterations make learning from experience active and evolutionary, rather than a static, one-time review.

Simply put, today’s working environments are often too complex and fast moving to give us the time and space we need to focus our full attention on learning. Consequently, the practical reality for many of us is that only those learning practices that require little time will actually take root (see “Rethinking Time” by Peter M. Senge in The Dance of Change, Doubleday/Currency, 1999). By weaving learning into the real-time priorities and real work challenges of a business unit or team, an emergent learning approach bypasses the need to stop what we’re doing in order to learn

In fact, a team may develop extraordinary emergent learning practices without ever thinking of it as “learning.” Emergent learning often looks a lot more like locally driven strategic planning or problem-solving than like what we usually think of as training. Groups self-organize to focus on improving their performance, rather than stepping into a classroom setting where the attention centers on the instructor’s expertise. On the other hand, because of its iterative nature, it differs from what we traditionally think of as planning or problem-solving by focusing on mastery (performance over time), rather than on accomplishment (performance today) (see “Comparing Training, Planning, and Emergent Learning” on p. 3).

Emergent Learning in Practice

Here’s an example of an emergent learning process based on a group’s real work needs and conducted in real time: The executive team of a large regional vocational school expressed its frustration at once again needing to downsize because of escalating costs. In years past, members had rolled up their sleeves and done the painful work of identifying possible staffing and program cuts. When all was said and done, they had at least felt a sense of accomplishment at having taken hard but necessary steps to solve the problem.

After the third downsizing this decade, they made a determined effort to escape from what they had come to see as a vicious cycle by taking steps to shift their focus from short-term crisis resolution to developing long-term solutions through emergent learning.

The team defined an arena on which to focus: its cost structure. Facing obvious and painful failures in trying to solve recurring financial problems, members recognized how little they really understood their costs. They made a commitment to “master” the cost arena — to develop a richer, shared understanding of what drives costs, and to be able to consistently manage them. They had a discussion to articulate the key variables or criteria that would indicate success in this arena.

The team then identified a few repeatable contexts that could easily provide opportunities for reflection: weekly staff meetings and executive reporting. Because these activities were already on their plates, they provided a relatively quick and easy way for team members to test their mental models about what was driving costs. Because they were recurring, the group could easily review the results of experiments that they planned to conduct on a regular basis, and gradually evolve a real mastery of the issue.

This process may look like nothing more than good problem-solving. But it demonstrates a subtle shift from accomplishment to mastery

To get started, team members shared their beliefs and understanding about what contributed to the school’s cost structure. Then they very deliberately turned these statements into hypotheses to test in learning experiments. Each member considered what projects he or she was involved in or what data he or she had that would serve as the basis for conducting experiments. For example, the head of programs was curious about whether his assumptions about the direct relationship between class size, perceived program quality, and costs would hold up. The head of facilities had questions about whether previous cuts in headcount might have actually resulted in increased maintenance costs.

Initially, they simply added brief reviews of cost trends (such as compensation, legal fees, and supplies) to their weekly meetings, and a discussion of 12-month cost patterns to the monthly and quarterly executive reports. Over time, through several iterations, they began to see new relationships and investigate such dynamics as the relationship between facilities maintenance, compensation, and legal costs. In staff meetings, they reflected on the potential causes of changes in costs and described experiments that they had tried. (At one meeting, the head of facilities reported about asking his team what they would do if he went on sabbatical for a year. The creative responses that he got inspired some of his peers to try the same experiment.)

At each iteration, the results of just-completed learning experiments became the “ground truth” on which they reflected in order to plan for the next learning experiments (see “The Emergent Learning Process” on p. 1). With the benefit of their peers’ perspectives, team members teased out unspoken assumptions, lessons learned, and so on. They began to question the measures that they had relied on in the past and realized that they needed more powerful and timely cost indicators. They acknowledged how delays in feedback — in the form of unanticipated cost increases — affected their ability to manage expenses. These sessions inevitably led to new questions and new experiments.

Beyond Problem-Solving

This process may look like nothing more than good problem-solving. But it demonstrates a subtle shift from accomplishment to mastery. With this new mindset, everyone on the school’s executive team worked under the assumption that they would run through the learning cycle at least several times. Over time, as they cycled through iterations of this process, their learning experiments got more specific and they asked better and better questions. They also developed finer distinctions about costs and the dynamics that cause them to rise. In addition, they identified early indicators that a problem was brewing. As a result, their sense of confidence in being able to tackle something as complex as escalating cost structures grew.

On the other hand, if the team had continued to focus on problem-solving rather than on learning, they might have replaced downsizing with another, perhaps equally short-term, “solution.” By simply abandoning their first approach to the problem, they may have failed to develop a true understanding of why downsizing did not solve the problem. Or they might have chosen to “downsize harder,” triggering even steeper cost problems as the school struggled with the loss of skilled personnel

By taking an emergent learning approach, the team also created a compelling context for drawing on the tools of organizational learning. For example, they began to see that they had fallen into a “siege” mentality regarding saving their favorite function from the chopping block. So the group sought training in balancing inquiry and advocacy, recognizing that their ineffective communication habits were affecting their ability to explore alternative theories and solutions. They also studied systems thinking to begin to grasp the drivers of costs and to understand the behavior of reinforcing processes. In this way, they developed expertise as they needed it and as it made sense for addressing their current business challenges — not as it was deemed necessary by a training department or corporate mandate.

Simplicity and Localness

The best emergent learning practices track a few simple variables within an experimental field that is as local as possible. In the example above, the executive team initially tracked operating costs (variables) within the different departments (experimental fields). Each participant made a series of small changes to the work that they were already doing in these areas.

Over the long term, these intentional, iterative experiments at the operational level often generate new and unpredicted, but remarkably powerful, changes in behavior. For example, the Boston Police Department uses simple three-month charts of major crimes, district-by-district, to understand and influence crime trends, such as a rise in burglaries in a particular neighborhood. Over time,

this disciplined approach to managing crime has inspired district police to go out of their way to meet local teens and attend community meetings, not because it’s their job, but because they see that making a personal connection is critical to grasping what is fundamentally driving trends in crimes.

The U. S. Army’s After Action Reviews (AARs), which emerged from its intensive two-week training simulations in the Mojave Desert, are another example of a practice that is so simple and local in design that it spread on its own, without being mandated from above. In an AAR, soldiers take an hour after a military encounter (simulated or real) to analyze what caused any differences between what they intended to accomplish and what actually happened. In addition, they identify strengths to sustain and weaknesses to improve in the next encounter. AARs have become so ingrained in the organization’s culture that almost anything is now seen as a learning opportunity—, “Let’s AAR that.”

Committing to Learning Experiments

As shown in these examples, opportunities for emergent learning are everywhere. The seeds for it can be found in what Barry Dym calls “forays” — small, local initiatives that are exceptions to the more established patterns of working together (see “Forays: The Power of Small Changes” by Barry Dym, V9N7). They can also spring up in “communities of practice” — informal groups that join together to develop a shared repertoire of resources. To reap the benefits of emergent learning, members of these groups must shift from following the traditional professional association model — holding abstract conversations based on expert presentations — to making the commitment to study their own performance in a concretely defined field of experiments (see “Communities of Practice: Learning as a Social System” by Etienne Wenger, V9N5).

Nortel Networks’ Competitive Analysis Guild (CA Guild) is an apt example of a self-organized community of practice that has been able to make that shift. The CA Guild gathers members from across organizational boundaries to share knowledge about Nortel’s competitors and build their competitive intelligence skills. Guild membership outlives project assignments and creates a “virtual neighborhood” of likeminded individuals.

Some Guild practices look like those of traditional professional associations: monthly meetings with formal presentations and a Web site with announcements of upcoming events. But the Guild has also created some activities that are developing emergent qualities. For example, any Nortel Networks employee can use the Guild Web site to seek information about competitors from members. The sharing of questions and answers through the network is an iterative process. Participants have reported that they have become more sensitive to early indicators of important actions by competitors.

The Guild also views industry trade shows as a natural experimental field. At any given industry trade show, there may be 30 or more Nortel Networks employees wandering the floor. The Guild developed a procedure to focus these employees on a learning agenda. After each show, not only does the Guild take away good data, but it also reflects on and refines its trade-show practices. Over time and with iterations, this approach turns good intelligence-gathering into emergent learning.

Islands of Mastery

Peter Senge has commented that, “I have never seen a successful organizational learning program rolled out from the top. Not a single one. Conversely, every change process that I’ve seen that was sustained and that spread has started small. Usually these programs start with just one team” (Fast Company, May 1999). Emergent learning builds the organizational learning “habit” from the bottom up, by focusing a team on mastering performance in an arena that is important to them. The venue may be big and “strategic,” such as demonstrating leadership during a merger, or it may be small and “tactical,” such as planning food for faculty meetings. Whatever the level, as the team disciplines itself to focus its attention on its performance in this one arena in an iterative way, a lot of what previously seemed like erratic, unpredictable results can begin to make sense (see “Conducting Learning Experiments”).

Emergent learning builds the organizational learning “habit” from the bottom up

And so, an island of mastery begins to emerge from the sea of complexity. And as one arena of action starts to make sense, the group naturally expands its field of inquiry into other arenas. In turn, team members’ confidence in being able to master their business challenges grows. They become better able to clarify their priorities, articulate their own theory of success, test their hypotheses, and make a strong case in support of their thinking.

This self-reinforcing cycle of curiosity and growing competence can have an almost addictive quality — it makes people thirsty to learn more. As people develop a learning discipline and begin to search for fundamental solutions, they almost automatically take a systems perspective, collaborate more effectively with others, and challenge their existing mental models.

In this way, pairing emergent learning practices with traditional training can help the tools and techniques of organizational learning find a natural home. As internal and external practitioners, we can look for opportunities to turn events and projects that we are currently working on into learning experiments. We can do more to identify and support naturally occurring emergent learning practices, and make it a priority to notice and publicize results. And we can also help business units, teams, and communities of practice create new emergent learning practices. In the process, we will build natural advocates for organizational learning, complete with their own compelling stories to tell.

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Applying archetypes such as “Shifting the Burden,” “Fixes That Fail,” and “Limits to Growth” to a specific problem can be a confusing and difficult process, especially if you believe there is one “right” way to use them. We can actually talk about using the archetypes in three different ways:

1. as different lenses;
2. as structural pattern templates;
3. as dynamic scripts (or theories).

By distinguishing between these different types of use, we can focus on increasing our capability in any one of the three ways, rather than being frustrated by trying to do everything at once. In this article, we will focus on using archetypes as lenses for gaining different perspectives on an issue.

I’ll See It When I Believe It

Many of us at one time or another have said “I’ll believe it when I see it,” suggesting that we have more faith in things that we can see and touch. If, for example, there are 100 cases of beer in inventory, you and I can count them and both agree on that number. On the other hand, if we ask why we have 100 cases, our opinions will likely be very different and may be colored by our personal beliefs.

For example, if I think the 100 cases of inventory are a result of poor production scheduling, I will tend to find evidence to support that view. Or, if I think that individual error is responsible for overstocking, I will focus on finding individuals to blame rather than look for any larger systemic forces that may be at work. We don’t believe what we see as much as we see what we believe. Because we can easily fall into this trap, having tools such as the archetypes to help us look at broader systemic issues can be helpful for expanding our perspective.

Seeing Through Systemic Lenses

In many ways, using an archetype is like putting on a pair of eyeglasses. If we look at a situation through the lens of the “Shifting the Burden” storyline, we will ask different questions and focus on different things than if we were using the “Tragedy of the Commons” archetype. It is not a question of which is “right,” but, rather, what different insight each archetype offers.

Using the archetypes as lenses requires a basic understanding of the main lessons, key elements, and outcomes or high-leverage actions that are embodied in each archetype (see “Systems Archetypes at a Glance,” August 2011). This level of understanding allows us to go into a situation, identify potential storylines at work, explore their implications, and gain some initial understanding of the problem under study.

Boat Buyback Dilemma

For example, consider the problem of fish depletion in coastal waters. In order to address the dangers of overfishing and eventual depletion of certain species, the U. S. government launched a pilot program to buy boats back from fishermen.

The overfishing problem has all the classic features of a “Tragedy of the Commons” archetype (see “Too Many Boats on the Horizon,” September 1994). A large number of players are competing for a single resource. The incentive is for each individual fisherman to catch as many fish as possible. However, the combined total of their efforts will eventually hurt everyone, as fish stocks become depleted. The irony of the situation is that despite the devastation in the long term, it is in no individual’s interest to stop fishing in the short term. The leverage in a “Tragedy of the Commons” structure is to have a single governing authority manage the commons. From this perspective, the boat buyback program can be seen as an appropriate role for the government as resource manager.

TRYING ON DIFFERENT EYEGLASSES

If we look at the same situation through the lens of another archetype, however, we can see some other potentially relevant issues. For example, we know that the storyline of a “Shifting the Burden” archetype is that a problem symptom cries out to be fixed. In such situations, we have a tendency to implement a solution that alleviates the symptom in the short term rather than to invest in a more lasting solution. Implementing a quick fix reduces the pressure to examine the deeper structures that may be at the root of the problem.

From this perspective, we might be concerned that the government bailout will send the signal that Uncle Sam will provide a safety net whenever the fishing industry develops over-capacity. Therefore, when fishing stocks replenish, fishermen may be less concerned about taking risks and expanding their fleet. Over time, the fix may become so entrenched that it will turn into a permanent Band-Aid that will shift the wrong kind of responsibility to the government. In this case, the “Shifting the Burden” archetype reveals how the short-term solution shifts the burden of risk and over-extension from the individual to the government.

Productive Conversations and Deeper Inquiry

The buyback example illustrates how the archetypes can be used to gain different perspectives on an issue. Rather than spending time figuring out which archetype best matches your particular situation or trying to get your arrows to go in the right direction, you can use the archetypes to begin a general inquiry into the problem.

To see which lenses may be relevant, try using the questions listed in the accompanying sidebar to see what insight each archetype can add to your problem (see “Trying on Different Eyeglasses”). Once you have selected the most pertinent archetype(s), you can use those archetype(s) to develop action plans that will address the problem systemically.

Looking at the world through the lenses of archetypes puts our primary focus on systemic structures and not on individuals. This is particularly important at the initial stage of problem diagnosis, because it enables you to engage people in the process more easily without triggering defensiveness. This process of “trying on” different stories leads us to ask different kinds of questions and, ultimately, enables us to have more productive conversations.

Daniel H. Kim is co-founder of Pegasus Communications, founding publisher of The Systems Thinker newsletter, and a consultant, facilitator, teacher, and public speaker committed to helping problem-solving organizations transform into learning organizations.

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Barry’s death left a gap in the field of system dynamics as well. As the founder of High Performance Systems (now isee systems) and the driving force behind the popular ithink® and STELLA® systems thinking–based software products, he made computer modeling accessible to people in business and education. At his memorial service, several speakers commented on what Barry’s life had meant to them. Peter Senge spoke about both the importance and the incompleteness of Barry’s work, noting that it was “up to us” to continue this important effort.

Since Barry’s death, I have spent a lot of time reflecting on his life and contribution to the field of system dynamics. In this article, I identify five operating principles that guided Barry’s work, especially in the realm of public policy. These principles are also applicable in business, education, and other areas of inquiry. By way of summary, I also offer a few thoughts about the nature of Barry’s legacy and how we might build on that legacy.

A Broad-Brush Conceptual Framework

To gain a deep understanding of Barry’s work, it is first necessary to have some sense for where he was coming from. What motivated his activities? What were his ideas regarding the real value of system dynamics?

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

Fortunately, Barry left a good paper trail that documents his thinking. For example, the STELLA and ithink user guides (HPS, 2003) do an excellent job of presenting Barry’s view on how to “do” system dynamics. In The “Thinking” in Systems Thinking: Seven Essential Skills (Pegasus Communications, 2000), Barry identified the key competencies behind the effective practice of systems thinking.

These resources shed light on Barry’s fundamental belief, which provided the motivating force for many of his professional endeavors. I like to phrase it this way:

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

This belief is an assertion that the primary value of system dynamics comes from the process not the products of that process (although Barry would readily agree that products were important, too!). It’s also an assertion that as more people use the framework, language, and tools of systems thinking and system dynamics to generate insight—and act accordingly —the more likely we will be to solve the big problems facing the world today.

Over the time that I collaborated with Barry, this deeply held assumption was never very far out of sight. It would often come to the surface in the context of a formal presentation, essay, or paper. Consider, for example, Barry’s contribution to the 1985 System Dynamics conference in Keystone, Colorado, in which he introduced the STELLA software. The paper he presented was entitled “STELLA: Software for Bringing System Dynamics to the Other 98%.” The title clearly reflects Barry’s fundamental belief that everyone should be using these tools.

Or consider the paper Barry presented at the 1994 conference in Sterling, Scotland, provocatively titled, “System Dynamics/Systems Thinking: Let’s Just Get On With It.” In the paper, Barry asserts that system dynamics is “quite unique, quite powerful, and quite broadly useful as a way of thinking and/or learning. It’s also capable of being quite transparent —leveraging the way we learn biology, manage our businesses, or run our personal lives.”

Barry devoted a huge part of his life to turning this deeply held belief into reality, through a variety of products and services, including software, learning environments, workshops, and specific client deliverables. The common theme in these efforts was increasing the base of people who could partake in the process of gaining value by doing system dynamics.

A simple graphic that Barry and I developed for use in our workshops gives a clear picture of what he saw as the relative value of investing in various levels of analysis (see “The Return on Investment of System Dynamics”).

It relates effort or time expended to the value or utility that one can expect to derive from that effort. As the curve shows, there is significant value to be gained from simple “conversational” uses of the fundamental thinking skills. Examples would include drawing a behavior over time graph to cast a problem in dynamic terms, characterizing an issue in generic terms in order to recognize patterns over time, or asking operational questions such as “how does this work?” (For details about the different systems thinking and system dynamics tools referenced in this article, go to www.pegasuscom.com/lrnmore.html and click on a term or topic.)

Another jump in value/utility can come at relatively low cost from creating a simple stock and flow map. A third increase in value can be added, again at relatively low cost in terms of time or effort, by transforming a map into a computer-based simulation model, perhaps with a simple interface to facilitate controlled experimentation.

Note that, once you move past simpler applications, diminishing returns can quickly begin to set in. In our experience, as the complexity of the model increases, the amount of effort, skill, and time required to underwrite that complexity increases disproportionately relative to the amount of value derived! Out at the end of the curve, adding complexity may well result in negative returns. The implication: You don’t need to build huge, complex models in order to derive value. Simple, straightforward uses of the framework, language and tools can add significant value at relatively low investment!

Five Principles

This section distills what I believe are key principles that guided Barry’s public policy efforts. The principles fall into three broad categories, associated with the three activities that Barry viewed as fundamental to any modeling effort:

THE RETURN ON INVESTMENT OF SYSTEM DYNAMICS

There is significant value to be gained at relatively low cost from the application of basic system dynamics skills. Once you move past simpler applications, diminishing returns can quickly set in. As the complexity of the model increases, the amount of effort, skill, and time required to underwrite that complexity increases disproportionately relative to the amount of value derived!

Building

1. The Principle of Operational Thinking
2. The Principle of Irreducible Essence

Simulating

3. The Principle of Controlled Experimentation

Communicating

4. The Principle of Mental Model Confrontation
5. The Principle of Controversial Topics

1. The Principle of Operational Thinking This principle was at the bedrock of Barry’s work. Barry himself viewed operational thinking as the key thinking skill required for the effective application of system dynamics.

Operational thinking entails getting to the essence of how a process works. It involves asking questions about key accumulations, or stocks, and flows in the system. For example, “What is being produced?”, “How is this activity generated?”, “What resources are consumed in the process of generating the flow?” These are questions about the physical relationships among different parts of a dynamics system that work together to determine its dynamic behavior. The effort is one of building understanding of how it works rather than simply listing the factors that influence the process.

The benefit of operational thinking is that it facilitates the identification of levers for changing system performance. If you have a clear picture of how the process works, you are in a solid position to ask focused questions about alternate proposed policy interventions and more accurately think through the implications of a proposed initiative. If, on the other hand, your thinking simply results in a laundry list of factors that influence the process, your efforts to identify levers for actually changing performance may well be limited.

Barry used an excellent illustration of operational thinking in his presentation at the 2001 Pegasus Conference. This event took place shortly after the September 11 terrorist attacks. Issues associated with international terrorism were very much on the minds of participants at the conference. One part of a storytelling progression within Barry’s presentation is shown in “The Inflows and Outflows of Terrorism” on p. 4.

This stock and flow map nicely captures the essence of the processes through which people become terrorists, and through which terrorist activity is generated. Note the salient features:

• The number of terrorists is represented by a stock; terrorist activity is represented as a flow. From this map, you can identify two fundamental ways to reduce terrorist activity: Either reduce the number of terrorists or make terrorists less productive.
• The options for directly attacking the problem are clearly mapped (eliminating terrorists, eliminating supporters, and implementing defensive initiatives).
• The diagram captures both the inflows and the outflows to the terrorist stock; that is, the factors that lead people to become terrorists as well as those that cause them to stop their activities. In so doing, it identifies the levers for long-term improvement in the performance of the system.

THE INFLOWS AND OUTFLOWS OF TERRORISM

The diagram captures both the inflows and the outflows to the terrorist stock; that is, the factors that lead people to become terrorists as well as those that cause them to stop their activities. In so doing, it identifies the levers for long-term improvement in the performance of the system.

2. The Principle of Irreducible Essence This principle is a variation of “Keep it simple, stupid.” Einstein worded this tenet as:, “A good explanation is one that is as simple as possible, but not simpler.” Occam’s razor is another version:, “A simple explanation is to be favored over a more complex one.” These views, along with the principle of irreducible essence, recognize that we must simplify in order to make sense of the world—it’s impossible to hold all the relationships that exist in our heads. The challenge is to preserve the relevant essence of that part of the world upon which we wish to act in our models.

The usefulness of this principle is twofold. First, it enforces a mental discipline that can lead to improved clarity about an issue. Second, irreducible essence leads to explanations that are accessible to both experts and nonexperts on a given topic. As a result, following this principle can lead to a significantly larger audience of people who can derive value from the effort.

Barry’s “Stories of the Month,” published on the HPS web site 2001–2003, provided many examples of the principle of irreducible essence in practice. These stories typically used a simple stock and flow map or a small simulation model to provide a systems perspective on current events in the news. A story that Barry was working on at the time of his death, entitled “Hot Air and Greenhouse Gases,” was motivated by some sloppy statements about global warming coming out of the White House in the summer of 2002. Among other things, these statements contended that the president had a plan that would reduce greenhouse emissions while sustaining economic growth. The implicit claim was that this plan would result in a reversal of global warming trends.

In response to these statements, Barry could have developed an elaborate model of greenhouse gases, or he could have pointed people to large, detailed models produced by others on the topic. Instead, he began working on a simple model and story (see “Growth, Gases, and Warming”).

This diagram is stark in its simplicity. It provides just enough of the relevant essence of the issue to get at the dynamics of the greenhouse effect. It includes just enough structure to facilitate investigation of the interaction between reduced greenhouse emissions (for example, through “green technology”) and increases in the level of economic activity that serves as the base for generating greenhouse emissions.

3. The Principle of Controlled Experimentation The principle of controlled experimentation entails making changes in a model one at a time to learn why it behaves in a particular way under particular conditions. Through such controlled experiments, users build understanding of the connections between structure (how the process is put together) and behavior (how it performs over time). They can compare their assumptions about the situation to the computer simulation and modify their mental models in response to what they learn.

Simple, controlled experiments can also create the activity basis for building shared understanding. A sequence of controlled experiments can yield extremely productive conversations, particularly when participants compare the results of the experiments to what they had predicted would happen. They can then discuss differences of opinion, identify commonalities of thought, and surface tacit assumptions.

Less directly, controlled experiments build an individual’s capacity to accurately trace dynamics and to make structural/behavioral connections. Barry was a firm believer that humans aren’t very good at doing mental simulations of anything except the simplest of systems. Nevertheless, he believed that people could build their capacity to play out dynamics in their heads through sustained practice. Indeed, this was one of the motivations behind the “Story of the Month” concept.

Many of the stories reflected the principle of controlled experimentation, including the first one that HPS produced. This story came about because Barry was in California at the time of the run-up in energy prices that took place in April 2001. Everywhere he went, he read news articles about organizations that planned to pass on increased energy prices to consumers. This practice raised an interesting systems question: Is it possible for everyone to pass on costs? Or is there some self-limiting process at work?

We developed a simple story to address the issue. The first part of the story looks at what producers do in response to a step-increase in energy costs. In the model, a simple balancing process is at work. In an experiment with a step-increase in energy costs, producer profits initially decrease. Producers then raise prices in order to bring profitability back to desired levels. When taken in isolation, this balancing process keeps profits at desired levels by passing on increased energy costs to consumers.

The next part of the story involves expanding the model boundary just a bit, to consider what consumers do in response. For consumers, an increase in prices means a decrease in purchasing power. This in turn can lead to upward pressure on wages. It’s another balancing process. This loop works to keep purchasing power in line with desired levels by driving wages upward.

It’s important to note, however, that wages are a cost to producers, and so an increase in wages can undermine producer profitability. In an experiment with the expanded model, a step-increase in energy costs leads to price increases, which causes wages to increase, which creates a further round of price increases! A reinforcing feedback process, latent within the structure of the system, underwrites a wage-price spiral!

By using controlled experiments in a simple progression, it’s possible to build understanding, stimulate good conversations, and strengthen mental simulation muscles.

GROWTH, GASES, AND WARMING

This diagram facilitates investigation of the interaction between greenhouse emissions and the level of economic activity that serves as the base for generating those emissions.

4. The Principle of Mental Model Confrontation Like the principle of controlled experimentation, the principle of mental model confrontation is simple but powerful. The premise? Whenever possible, bring the prevailing mental model to the surface of the discussion. Explore the dynamic implications of that mental model. Then, provide an alternative mental model (often in the form of a stock and flow diagram) that offers richer explanations, more robust policy propositions, or improved insight into the issue at hand.

The process of confronting the default mental model is a key part of creating a compelling case for changed behavior—often the desired outcome of work in public policy. When there are multiple, conflicting mental models, the principle of mental model confrontation can be used to facilitate communication among key stakeholders. There’s learning to be had from systematically comparing, testing, and evaluating underlying assumptions!

In late September 2001, Barry put together a “Story of the Month” on terrorism. This story nicely illustrates the principle of mental model confrontation. In it, Barry begins by “surfacing the mental model underly- ing [the rhetoric of the Bush administration in response to the September 11 attacks, for example, ‘leading the world to victory in a war against terrorism’] so you can critically examine its implicit assumptions.”

Next, Barry builds upon this simple mental model to offer a critique of the prevailing thinking. This richer structure—very similar to the one he developed for the 2001 Pegasus Conference—sheds light on longer-term difficulties for the “war on terrorism.” Over the long haul, a reinforcing loop associated with the terrorist recruiting process, as turbocharged by increasing anger at US-led actions, can lead to a rapid growth in both the number of terrorists and the frequency of terrorist acts.

Later in this story, Barry offers a systems thinking–based alternative to looking at the situation. The alternative consists of two components: a defensive component that minimizes current threats, and an offensive component that gets to what Barry sees as the root cause of terrorism. Building it up a piece at a time, Barry ends up with a map that shifts from a focus on “winning the war” to building tolerance of another’s viewpoint, managing anger, defusing hatred, and maybe even adjusting one’s position. By initially confronting the mental model that appeared to be prevalent in the Bush administration, Barry presents a systems thinking– based alternative.

5. The Principle of Controversial Topics This principle flows directly out of Barry’s deeply held view that anyone could (and should be able to) use the language, framework, and tools of system dynamics in a productive way. He believed strongly that an informed layperson could generate insight into any topic of interest. For Barry, controversial or “hot” topics were especially important to pursue, because they’re often the most confusing or perplexing, and therefore have the most potential for benefiting from the use of system dynamics!

I’ve interspersed several of these controversial topics through this paper. To make the point very clearly, I’ll introduce one more issue that Barry tackled in his “Story of the Month” series. In response to the tragedy at Columbine High School and at other schools in the United States, Barry put together the “Guns at School” story. He wrote, “Until we have a solid grip on the relationships responsible for producing and maintaining this scary phenomenon, we have scant hope of doing much to effectively address it.” His story was an effort to come to grips with these relationships.

The story begins with a brief history of gun-related school violence and then incrementally develops a stock and flow map that seeks to explain the phenomenon. The map depicts the progressive build-up of alienation and rage, relating these emotions to the acquisition and use of guns within a student population.

Against this backdrop, Barry developed a set of policy-based experiments around three kinds of potential actions: gun-related initiatives (such as improved screening of gun purchasers, disarming students with guns, and restricting student access to guns), media initiatives (anti-copycat practices that limit news about school shootings), and student coping skills initiatives (trainings in rage, alienation, and humiliation management).

Barry’s real legacy in public policy work resides in the mindset along with the principles that he employed.

Readers are prompted first to conduct one-at-a-time controlled experiments with different interventions. Then, in a second round, they are encouraged to create a “policy cocktail” to find the most effective set of interventions. The intent of these experiments is to provoke thought and stimulate discussion by exploring the relationships that drive this pressing social issue. Is the topic controversial? Yes! Is the story helpful in shedding light? Absolutely!

Barry’s Legacy

Barry did not have a huge publication record in the realm of public policy. Most of his work was done in the context of client work or, more recently, in presentations of the “Story of the Month” column. I do not think that Barry’s work, by itself, is where his legacy resides. Rather, as befitting the teacher that he was, Barry’s real legacy in public policy work resides in the mindset along with the principles that he employed.

For those of us who wish to carry on the work, I believe that there is much to glean from this legacy. For me, the primary lessons are:

• Maybe not everyone can apply system dynamics to public policy issues, but there is a large population of people who could derive value, at some level, who currently are not. Those people need access to systems tools, concepts, and frameworks.
• Most people/organizations are on the steep part of the effort/value curve. They therefore can derive significant value from conversational uses of system dynamics, simple stock and flow maps, and simple models with interfaces.
• The five principles aren’t rocket science—although there is some art associated with their application. I have found them helpful guideposts for my own work. You may find them useful as you seek to apply systems thinking in practical ways in your own context.

While it is beyond my ken to consider how one might replace someone like Barry, I believe that it is possible to carry on his work. It will require sustained effort and application, but it can be achievable. The world will be better for our efforts to do so.

Steve Peterson (steve@evans-peterson.com) is an independent consultant based in West Lebanon, NH, where his work focuses on the practical application of system dynamics across a broad range of application areas. Before starting his own practice, he worked closely with Barry Richmond, both at Dartmouth College and at High Performance Systems, Inc., where he was an integral part of the development team responsible for the ithink® and STELLA® software products.

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