RIGOR VS. SUPPORT

The Facilitative Modeling approach for making important decisions combines high levels of analytical rigor with high levels of stakeholder support.

Common Decision-Making Approaches

Because of this level of complexity in all aspects of organizational life, organizations usually rely on what I refer to as the “shoot from-the-hip” approach for making important decisions. You’ve seen this technique if you’ve ever been in a team meeting in which a decision must be made today. Some members of the group toss out their ideas; most participants stay silent. Eventually, the team leader contributes his or her opinion, and everyone agrees. Decision made! Most meeting participants later bemoan the “poor” decision, claiming they won’t support it. The result? The new policy dies on the vine prior to implementation, leaving the organization the same as it was before.

In analyzing “shoot-from-the-hip” decisions, we observe that they lack strength in at least two major areas: analytical rigor and stakeholder support (see “Rigor vs. Support”). This isn’t a novel observation: Organizations have struggled with these two shortcomings for years and have devised various ways to overcome them.

1. The Technological Approach Before making a major decision, in order to increase the level of analytical rigor (or understanding of the issues), managers often rely on analysts and their toolkit — what I call the Technological Approach. Organizations adopting the Technological Approach generally do so because they’ve fallen victim to the mindset that they must find the perfect answer. The idea is that if you throw enough analysis at an issue, you can completely understand everything and uncover an ideal solution. These organizations think the answer must be found in the numbers.

To process the data they generate, organizations subscribing to the Technological Approach employ spreadsheets and statistical techniques. Some even build large simulation models to test nearly infinite possible scenarios. However, these tools can obscure the assumptions underlying the analysis. And because decision-makers aren’t privy to these hidden assumptions, they cannot compare them to their own mental models — so they do not trust the resulting recommendations. This lack of trust in the analysis is a major factor in why, although usually carefully applied, the Technological Approach rarely generates the support needed to lead to effective policy-making.

2. The Stakeholder Approach In contrast, proponents of a Stakeholder Approach often put technology aside and instead try to build knowledge and support through stakeholder involvement. Well-known techniques that follow this approach include Future Search, Open Space Technology, the World Café, various forms of dialogue — even some facilitated mapping sessions using causal loop diagrams and systems archetypes. These methodologies share an underlying mindset — by getting representation from different players in “the system,” everyone will gain a broader view of the problem at hand. Further, by allowing participants to express divergent perspectives in an unconstrained fashion, the Stakeholder Approach lets them formulate creative, systemic recommendations.

Whether trying to define the problem or to generate solutions, people applying these processes (if only implicitly) tend to follow a model of interaction described by Interaction Associates as the Open-Narrow-Close model. During the Open phase, participants get all of the data on the table while defining the problem; if they’re generating solutions, this is the stage in which creative solutions spring forth from the group’s collective wisdom. During the Narrow phase, contributors take an overwhelming list of choices (problems or solutions) and narrow them down to a few to consider further. During the Close phase, they actually choose which problems to tackle or solutions to implement and how to do so. Managers then often assign groups to each of the major action items identified during this stage and give them their blessing to “go forth and implement.”

The Stakeholder Approach includes processes that build broad support — unlike what often occurs in the Technological Approach. Plus, it helps those involved to see the system from a broad spatial and sometimes temporal perspective. These results are necessary and important for creating effective changes in any system.

A major weakness of the Stakeholder Approach, however, is that the processes used to narrow and choose

APPROACHES FOR IMPLEMENTING SYSTEMS THINKING

Facilitative Modeling serves as a middle ground between the Technological Approach and the Stakeholder Approach.

among the resulting divergent issues/strategies lack rigor and usually

rely on the assumption that, simply by having enough stakeholder representation, the group will make excellent decisions. But as Irving Janis learned by studying extremely poor decisions (such as the Bay of Pigs fiasco and the escalation of the Vietnam War, which he described in his book Groupthink), groups with very high average IQs can function well below expectations.

Barry Richmond of High Performance Systems, Inc. created a simple example called the Rookie-Pro exercise that also illustrates this point. Despite working with a much simpler human. resource system than that found in most organizations, only 10 to 15 percent of individuals can guess the system’s future behavior — even after lengthy discussion! So the assumption that the collective wisdom of the group will surface in a way that leads to optimal decision-making is tenuous at best.

In addition, the framework employed to guide team members in narrowing and choosing among different options doesn’t help to determine if elements of the proposed solutions need to be implemented at different times and in varying degrees. The result is that the organization often chooses to put the same amount of resources and effort into each action item. Nor does the Stakeholder Approach determine if the issues are interconnected — different groups may be separately implementing policies that should be done together or, even worse, are mutually exclusive.

Facilitative Modeling

The good news is that there is a way to both rigorously understand (or

even reduce) complexity and improve stakeholder support! Practitioners are often drawn to the field of systems thinking because of its promise to build collective understanding — to get everyone on the same page. Even so, these managers can be pulled between the Technological Approach (big simulation models created by experts) or the Stakeholder Approach (facilitated sessions using causal loop diagrams or systems archetypes). But there’s a middle ground — a large range of activities that I refer to as “Facilitative Modeling” — where tremendous power resides (see “Approaches for Implementing Systems Thinking”).

Facilitative Modeling is a Technological Approach, because it uses computer simulation and the scientific method to build understanding. It is also a Stakeholder Approach, because it requires the input of the important stakeholder groups, uses a common language so everyone can get on the same page, and creates small, simple, and easy-to-understand models. The models don’t generate the answer; rather they facilitate rigorous discussion. Facilitative Modeling usually culminates in a facilitated multi-stake-holder session in which the participants generate common understanding and make well-informed decisions.

Overview of the Process

In the Facilitative Modeling process, a group of stakeholders identifies and addresses an issue critical to their collective success. The issue is often one that has been resistant to organizational efforts to “fix” it. After choosing the area for exploration, the group sets the agenda for a facilitate

session. In preparation for that meeting, several individuals in the group serve as a modeling team and develop (alone or working with a modeler) a series of simple systems thinking simulation models that clearly articulate important components of the issue. These components may include the historical trend for that issue, the future implications if the trend continues, possible interventions, and the unintended consequences of some of these solutions. The models are deliberately kept small so that stakeholders will understand them and the development process remains manageable.

However, it’s not enough just to make models! In fact, building useful models is probably less than half of what makes a Facilitative Modeling initiative successful. The process requires the modeling team and perhaps others to create additional materials for the facilitated session, such as workbooks for tracking experiments and writing reflections, as well as CDs of the models for after the session. A facilitator and/or design team needs to carefully plan various aspects of the session, such as appropriate questions, suggested experiments to run on the model, and a mix of small and large group discussion.

The facilitated session represents the culmination of the process. During the gathering, teams of two to four people explore the models on computers. The session includes large group interludes and debriefs between exercises. And at the end of the session, participants discuss and agree on

next steps based on the insights that emerged during the event (see “The Facilitative Modeling Process”).

Facilitative Modeling in Action

Using the Facilitative Modeling Process outlined above, a nonprofit organization recently explored potential issues associated with implementing new funding policies. This organization was responsible for improving the health and welfare of the poor population in a community by giving funds to other local nonprofits to provide services. Originally, the organization had determined which organizations to fund and how much funding to supply by analyzing the services that the target organization would provide; in recent years, it had settled into just increasing the amount of funding incrementally over the previous year’s figure. To create more accountability among the local organizations and improve outcomes in the community, the nonprofit had decided to apply a performance driven approach to funding (that is, base funding on projected improvements to performance indicators and then renew the funding if the community experienced noticeable improvement in those areas).

Some members of the organization, as well as members of an important partner group, were concerned about the potential barriers to implementing this updated approach and were eager to understand possible unintended consequences that might result from the change. They agreed that a Facilitative Modeling approach would be an excellent way to surface and discuss these issues in a way that would give all stakeholders shared insight. In little more than five days of working with a facilitator and a few representatives from the organization and its partner, the team developed three small “conversational” models for a one-day facilitated session.

At the beginning of the session, the group adopted a set of ground rules to guide their interactions. Once participants agreed to the guidelines, they began by experimenting with the first model. The purpose of this initial simulation was to surface and discuss the potential dynamics associated with implementing the new funding approach. Allowing “sub groups” to work with the models at their own speed often increases their level of understanding. However, even those with some skill at reading stock and flow diagrams similar to the one shown here can be quickly overwhelmed by maps. The simulation included a function that let the sub groups slowly unfurl pieces of the map so that they more easily followed its logic (see “The First Map” on p. 5).

The map shown here represents one way to look at the different organizations affected by the nonprofit’s funding decisions. The language of stocks and flows is ideally suited for looking at this issue. The three stocks at the top of the diagram (the rectangles labeled “Resistant,” “Not Committed,” and “Committed”) represent groups of organizations. Currently, because the new approach has yet to be implemented, all organizations would belong in the “Not Committed” stock. Eventually, as the new funding approach is made into policy, organizations would begin to move into the “Committed” or “Resistant” stocks. Obviously, if possible, the funding group wanted to avoid any organizations becoming “Resistant.”

At the session, the individual groups discussed the meaning of each of the stocks. What does it mean to be “Committed”? “Resistant”? They mulled over the question, What number of “Resistant” organizations would pose a problem for the program as a whole? Can “Committed” organizations become “Resistant”? Is it realistic to assume (as the model does) that “Resistant” organizations never become “Committed”?

Talking about the diagram helped he sub groups, and eventually the entire group, reach consensus about how organizations might become committed or resistant to the changed funding policies. For many of the participants, it was the first time they had discussed the potential that some of their client organizations might resist the changes! By working with the model, the group was able to surface an unpleasant concept in a way that allowed them to grapple with its implications for their changed strategy.

They then entered different values into the model to experiment with how the funding organization might allocate its resources in the coming months. How much effort should they put into developing the performance-driven funding program? How much into explaining the program to the funded organizations? And how much of each should they do prior to officially announcing the program? After announcing it? In short, the group wrestled with the systemic or “chestration” (a concept developed by Barry Richmond) of resources the magnitude and timing of efforts required to successfully implement the strategy.

The group concluded that, in the first phase of development, they should apply most of their efforts to designing the new policy. Doing so builds the “Clarity of the Program,” which is useful in preventing “Doubts About the New Approach” down the road. They realized that they would need to allocate at least some resources in the first phase to working with the client groups and addressing their doubts about the change. This process would also help them to refine the approach (see “Implementation Timetable” on page 6). The next phase would require additional work with the other stakeholder groups to explain the program prior to release. The third and fourth phases would involve implementation; this is when the nonprofit’s staff members would spend most of their time addressing the doubts of the affected organizations.
The group realized that the exact numbers of organizations in each category wouldn’t be the same in real life as in the simulation, but that the stories described by the model were consistent with what they now expected might happen when overhauling their approach to funding. In keeping with the need for systemic orchestration the group concluded that their allocation of strategic resources must shift over time, depending on which phase they were in (for example, in the second phase, they would need to apply some resources to program development and even more to working with stakeholders).

Working with Subsequent Models

In Facilitative Modeling, each model tends to add to the understanding generated by previous ones. Because the performance-based funding approach would require implementing a new IT system, the second model helped participants explore how a funded organization would need to allocate resources in order to develop a new IT system and build its staff ’s capacity to use it. The third model served as the capstone exercise, because it required participants to explore how client organizations might allocate their resources across the following needs: providing services, building and maintaining the IT system, investing in staff skill development, and collaborating with partner organizations.

THE FIRST MAP

The three stocks at the top of the diagram (the rectangles labeled “Resistant,” “Not Committed,” and “Committed”) represent groups of organizations. As the new funding approach is made into policy, organizations would begin to move from the “Not Committed” stock into the “Committed” or “Resistant” stocks.

During the large-group debrief of the third model, the nonprofit’s senior director said that he didn’t like one dynamic that he experienced with the model. In all cases, after the funding change, the youth population’s sense of disconnection from the community initially worsened, even when the simulated strategies encouraged a majority of client agencies to be committed to the shift and to effectively implement performance-based approaches to providing services. When he experimented with the model, the director kept trying to avoid this “worse-before-better” dynamic. Through probing questions, the group learned that it wasn’t that he didn’t expect this behavior to happen, he just wished it wouldn’t!

IMPLEMENTATION TIMETABLE

By using the model to explore the magnitude and timing of efforts required to successfully implement the strategy, the group concluded that, in the first phase of development, they should focus on designing the new policy.

This revelation led to an interesting discussion of what is often an undiscussable in the public sector: that policies designed to improve social systems often take time before they lead to noticeable improvements and that there is often conspicuous degradation of performance in the interim. The director expressed that it was political suicide to admit that things might actually get worse before improving. Ultimately, through the facilitated discussion, he came to understand that regardless of whether he wanted to admit that such a dynamic might occur, it was inevitable, given the long delays before activities such as IT development and skill-building would have a positive effect on services. Through this admission, he and his staff were then able to explore options for mitigating the effects of this unavoidable dynamic.

Ultimately, the nonprofit’s staff left the session with useful insight in several areas. First, they all understood that some of their client organizations might resist the new approach. Second, they realized that it would be helpful for them to include those organizations in developing the program. Third, the group agreed that building staff skills was likely to be a more challenging impediment to successful implementation of the changed approach than developing the IT infrastructure. Finally, they accepted that systemwide implementation would require orchestrating a series of activities that, even in the best of circumstances, would cause a “worse-before-better” dynamic. All of these insights were just the beginnings of an ongoing dialogue, and all were facilitated by using small models to focus the conversation.

The Value of Facilitative Modeling

As shown in the example above, there is a powerful place for small models in a facilitated environment. The process used for developing good systems thinking models increases the rigor of the analysis and captures the benefits of a Technological Approach. At the same time, by keeping models small, Facilitative Modeling improves on the benefits of a Stakeholder Approach and increases the likelihood that all participants end up in alignment. Moreover, the Facilitative Modeling approach uses a language — stocks and flows — that is more representative of reality than other visual mapping languages. For this reason, the participants are able to discuss and come to a novel understanding of the assumptions built into the model. Running the simulation provides an essential test of the group’s understanding and facilitates further conversations about the likelihood of different results. The computer-generated “microworld” creates a safe environment for experimentation.

The post Facilitative Modeling: Using Small Models to Generate Big Insights appeared first on The Systems Thinker.

I strongly believe that stock and flow diagrams offer a deeper understanding of a system than do causal loop diagrams. Nevertheless, in the past, I found it difficult to get more than a small handful of clients to develop the facility to build them. Despite its obvious benefits, the rigor of the stock and flow language comes at a price—it is more difficult to learn. While companies that create simulation software have made enormous advances in their products, vastly simplifying the model-building process, we still have a long way to go in learning how to help people develop the facility to create even simple models.

High Performance Systems, the creators of the ithink® software, have stuck firmly to their belief that a true understanding of the dynamics of any system requires an appreciation of the underlying stock and flow structure. For that reason, their software does not provide the facility to build CLDs. The best they offer are “Loop Pads,” which they describe as “. . . simple pictures that identify the cause and effect processes that work to generate dynamic behavior patterns.” To display these pictures, you have to build the stock and flow model first.

The challenge is to find more effective ways of helping clients develop an understanding of the structural dynamics of the system they are studying, while acknowledging that they usually find CLDs an easier place to start than stock and flow diagrams. To that end, I have developed an approach to model building that uses ithink in a slightly unorthodox way to start clients at a relatively easy place and move them quickly to a more sophisticated understanding of a given system using stocks and flows. Paradoxically, this technique capitalizes on the software’s unwillingness to let users draw CLDs.

From Feedback Loops . . .

To follow this process, you must use version 6.0 of the ithink software, which allows you to minimize the size of the converter icon. Start by changing the defaults to set the converters to small. Doing so lets you use the converters as you would the variables in a CLD. Then use the text box facility, which is one of the objects on the menu bar, to create the polarity signs (, “+” or “-,” which correspond to “s” and “o”). For example:

So far so good. Up to this point, we are simply building a CLD in which “resources allocated to process improvement” are influenced by current “performance” and “desired performance.” However, when we try to create a causal link between “resources allocated to process improvement” and “process errors,” an error message appears indicating that such an action would create a circular connection.

The nature of the mathematics that underlies the stock and flow language means that the software is unable to calculate the value of any converter or flow when they loop back on themselves. As the help files state: “In drawing connector linkages, you may encounter an alert which tells you that circular connections are not allowed. Mechanically, this alert means that you have attempted to create a chain of converters or flows, such that one converter or flow ultimately depends upon itself. The software cannot resolve the resultant simultaneous equations.”

. . . to Stocks and Flows

To get past this barrier, we must create at least one stock somewhere in the loop. This process forces us to look more closely at the structure of the loop we are creating and identify one or more stocks. Every feedback loop has an accumulation—it is this accumulation that generates the feedback dynamics. In this example, the issue for the team was how actual performance levels drove “resource allocation to process improvement.” With this in mind, we can now make a simple modification to the CLD by converting the variable “performance” into a stock.

We are now a step closer to gaining a deeper understanding of the feedback processes involved in this structure. We have done so, however, by beginning with a process that clients are familiar and comfortable with and then moving to a structural understanding through one simple step. How we develop the model from this point forward depends on your goals. We could stay with this loop and simply develop the stock and flow structure for each variable.

Going into Greater Detail

We also might want to explore a certain part of the structure in greater detail. For example, we might be interested in the dynamics involved in a process-improvement program. In this case, the team realized that the resource allocation decisions were not only determined by actual performance but by the gap between actual and desired performance:

On the other hand, we may want to develop a loop to explore the impact of process quality. One possibility could be:

Once again, when we try to close the loop by connecting “investing in process quality” with “process quality,” we will receive an error message that circular connections are not allowed. How we respond to this message depends on what we are trying to understand with the model. If we want to examine the financial implications in more detail, we could begin to unravel the structure underlying the variable called “profits.” For example:

The key point is that we always anchor the model development process in something the client is familiar and comfortable with—the development of CLDs. We then force the software to highlight a logical error to provide a stepping stone to unfolding the stock and flow structure. I have found that, using this technique, more clients are able to develop an ability to create their own stock and flow models than before. Prior to using this approach, I found that clients viewed CLDs and stock and flow diagrams as separate and distinct languages. Since I’ve implemented this process, I have noticed that they have begun to see the similarities, rather than the differences, between the two. As a result people are less mystified when working with stocks and flows, seeing them embedded in the feedback loops of CLDs.

David Rees is the director of High Performance Learning Systems, a consultancy firm specializing in applying systems thinking principles and tools in public and private sector organizations. He is also a research fellow at the Centre for the Design of Innovative Systems at UNITEC in Auckland, New Zealand.

The post Anchoring Model Development in Causal Loop Diagrams appeared first on The Systems Thinker.

For example, in August, White House Science Adviser John Marburger briefed a Senate panel on climate change, saying, “We know we have to make very large changes if this turns out to be a problem. The consequences of human-induced global warming could be quite severe.” Yet at the same briefing, the administration stood behind its “wait and see” policy, articulated by President Bush in February: we should only “slow the growth of greenhouse gas emissions,

and—as the science justifies—stop, and then reverse that growth.” Climate change could be severe, and yet we should wait before acting. How can U. S. leadership reconcile these two seemingly contradictory statements?

Climate As a Delayed System

MIT professor John Sterman and Harvard’s Linda Booth Sweeney explain that this “wait and see” approach makes sense if you believe the world’s climate to be a nondelayed, responsive system in which a change in human activity has an immediate effect. Their recent experiments confirm that many highly competent people instinctively see climate as behaving this way. Most of their business-school student subjects thought that if humans reduced emissions of GHGs, the storehouse of those gases in the atmosphere would promptly decline and global temperature would follow.

CO2 IN THE ATMOSPHERE

Carbon dioxide (CO2) in the atmosphere, the primary heat-trapping gas, can be thought of as a stock or accumulation. The stock is now at its highest level in almost half a million years. To reduce the ecological and economic changes from producing global warming, we need to lower the level of the stock by reducing the inflow (CO2 emission rate) to less than the outflow (Net CO2 removal rate).

The inflow is currently about double the outflow. If we were to reduce the inflow by, say, 20 percent, it would still be greater than the outflow and the level of CO2 would continue to rise, albeit at a slower rate. No wonder the students predicted incorrectly—it is counterintuitive to think that the CO2 emission rate can go down while the level of CO2 in the atmosphere continues to go up! Nevertheless, it’s true. If the removal rate were constant, we would need to cut the inflow rate by more than 50 percent to finally begin to lower the CO2 level. The bottom line is this: If we, as the Bush administration says, “slow the growth of greenhouse gas emissions, and—as the science justifies—stop, and then reverse that growth,” it could still take many decades for levels of CO2 in the atmosphere to decline.

A Robust Plan

If we believe Mr. Marburger that the effects of climate change could be “quite severe,” we need a robust plan.

The Bush administration’s plan would work well if the climate had short delays. But the plan is not robust when managing a slow-responding system like our climate; the possibility of negative, irreversible effects from waiting are too high.

We see two important steps. 1. Teach ourselves the basic mechanics of our climate.

Prudence leads us to act now to educate ourselves about the dynamics of the climate system and to address the source of the problem with practical measures. If Sterman and Booth Sweeney are right, our generally poor intuition about the climate enables many of us to accept a “wait and see” approach. For our society to engage in an effective public discourse about global warming, we need to ground ourselves in the basics of the climate inflows, levels, and outflows. Then we can evaluate the impact of national-level proposals and really understand the challenge that we face in stabilizing the climate.

2. Act now to reduce GHG emissions. The best way to deal with a slow-moving system in which we know we will eventually need to make a change is to begin the change as early as possible. We need not initially focus on retooling our entire industrial base; we can begin with the significant reduction in emissions available through hybrid cars, better designed industrial motors, fuel cells, and renewable energy production. Such improvements could come at relatively low cost, improve our short-term economic vitality, and reduce energy dependence.

How can we get the process started? We suggest designing incentives and rewards that would unleash people’s tremendous capacity for innovation. A similar outpouring of new ideas came as a result of the ban on CFCs to prevent additional damage to the ozone layer. Let’s introduce similar mechanisms into our market system to encourage technological and behavioral changes for reducing GHG emissions.

Prudence leads us to act now to educate ourselves about the dynamics of the climate system and to address the source of the problem with practical measures. These actions will not be easy—technologically, culturally, or politically. But they are certainly easier than navigating a barge while pretending it will handle like a Ferrari.

The post We Can’t Afford to “Wait and See” on Climate Changes appeared first on The Systems Thinker.

Characteristics of Accumulators and Flows

Accumulators and flows are basic building blocks that allow us to represent dynamic phenomena in a more precise way than systems archetypes and causal loop diagrams (see “Accumulators: Bathtubs, Bathtubs Everywhere…” V21N7). Accumulators are things that accumulate over time—water in a bathtub, interest in an IRA account, world population—and are represented in accumulator and flow diagrams by a box. Flows, on the other hand, are things that increase or decrease the levels of an accumulator—water flowing in or out of the bathtub, money flowing in or out of an IRA, births and deaths affecting population—and are represented by a circle with a faucet on top.

TIP: One way to differentiate between accumulators and flows is by their measurement: accumulators are measured in units, while flows are measured in units over time. Money saved in an IRA, for example, is measured in dollars, while the interest flow is measured in dollars per year.

Understanding accumulator and flow structures is important because almost all complex dynamic behavior in organizations occurs as a result of accumulations of material or informational flows. Identifying accumulators, therefore, focuses our attention on managing the most important variables in a system. At the same time it helps us understand how our decision-making process can regulate the flows into and out of those variables.

Filling Orders as Accumulators and Flows

Mapping a system in terms of accumulators and flows can help us visually represent the structure of a system and identify the source of problems. If we are experiencing a growing backlog of unfilled orders, for example, we might begin sketching out the system by identifying the most visible accumulations in our order fulfillment system. “Order Backlog” would be an obvious starting point. If we then asked how the accumulation of our order backlog could be changed, we might identify flows such as “New Orders” (which increase the backlog) and “Orders Filled” (which decrease the backlog).

From this simple accumulator and flow diagram, we can next flesh out the picture and add detail using our understanding of past history. For example, if the order backlog remains high, delivery delays usually go up. Customers will grow impatient and may threaten to take their business elsewhere. One way to respond to this problem is to expedite certain orders—which pleases angry customers, but also takes time away from order fulfillment and leads to an increase in the order backlog and more angry customers (R1). This series of events can be captured by adding a causal loop to our initial accumulator and flow diagram (see “Impact of Order Backlog”).

Viewing the situation from an accumulator and flow perspective makes it clear that there are only two ways to reduce the backlog—decrease the inflows or increase the outflows. We either have to slow down or stop marketing for a while, or ramp up capacity to increase the order fulfillment rate. The accumulator and flow diagram poses the situation in a clear and concise way and focuses our attention on addressing the key flows.

IMPACT OF ORDER BACKLOG

If the order backlog remains high, delivery delays usually go up. Customers will grow impatient and may threaten to take their business elsewhere. One way to respond to this problem is to expedite certain orders—which pleases angry customers, but also takes time away from order fulfillment and leads to an increase in the order backlog and more angry customers (R1).

Accumulators as Memory

Mapping in terms of accumulators and flows reminds us that accumulators don’t simply disappear just because we stop a particular flow. If, for example, we were to stop all marketing efforts today, it would still take time for orders from previous marketing campaigns to stop flooding in, which means we need to address our capacity issues regardless of how we change our marketing efforts. When managing an accumulator and flow structure, it is important to remember that accumulators are a little like elephants—they never forget what they have been given to remember.

Kellie T. Wardman is resource director of YMCA of the USA. Kellie was publications director of Pegasus Communications. She holds an MFA in creative writing from Emerson College. Go to her blog at http://kelliewardman.com.

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.

The post Managing with Accumulators and Flows appeared first on The Systems Thinker.

The answer is … “it depends.” It depends on the underlying theory on which the current tools and methods are based. If our management practices are based on transient or situation-specific phenomena, they are likely to require revision whenever the circumstances change. If, on the other hand, they are based on a structural understanding, the situation may change, but the tools will still apply.

Where Are the Cows?

Barry Richmond of High Performance Systems [now I see systems] tells this story: “While perusing a well-known economic journal, I came across an article which described a model that had been constructed to forecast U. S. milk production. The model was of the Y=f(Xi) form [Y =Y0 + a1X1 + a2X2 +…+ anXn], where the Xi’s included such things as: last year’s milk production, interest rates, spending on cattle feed, GNP growth, and other macroeconomic factors. As the article detailed, the model performed quite well as a predictive device—at least in terms of its ability to ‘track history.’ The obvious thing about this model, that would bother both dairy farmers and people who were partial to operational specifications, is: ‘where’s the cows?!’ Simply stated, if you’ve got no cows, you’ve got no milk! Crude, but true.”

How does all this talk about cows relate to our vice president’s question? Well, imagine that an epidemic swept over the country and killed all the cows. What would the above model predict for next year’s milk production? The answer would most likely look a lot like the number for last year’s milk production, which is clearly incorrect. The model must be abandoned.

“Unfair,” you might say. “It’s not that the model is wrong. It’s just that the world has changed dramatically since the model was originally built and the changes must now be added.” But what has really changed? Yes, the cows are now dead, but the basic fact that milk comes from cows, and that without cows there can be no milk, is as true now as it was before the mass decimation. From a structural perspective, the nature of the world has not changed at all. The model was inadequate because it was based on situation-specific data that has now changed.

Structural Thinking

When we look at the world through a structural lens, we are interested in understanding how things actually work. We are less interested in correlational relationships and more interested in the causal structures that produce the observed behavior. This is not to say that nonstructural models aren’t valuable. Regression models, for example, have many applications and are useful for identifying correlation, explaining sources of variance, and extrapolating from historical data. Those models are inadequate, however, for gaining insight into how a system actually operates.

MILK PRODUCTION MODEL

If we wanted to create a structural representation of milk production, we would begin with the central accumulator “Milk Cows.” Milk production is determined by the number of cows and the amount of milk per cow. To create our hypothetical scenario of an epidemic, we would simply enter zero for the number of milk cows. The resulting annual milk production would also be zero.

If we were to look at the milk production model from a structural viewpoint, we would start with the basic fact that milk comes from cows. Therefore, cows are the central accumulator in the model—the number of cows accumulates over time, as cows are born, mature, and become milk cows (see “Milk Production Model”).

Depending on the scope of our study, we may be interested in representing the lifecycle of all cows, or just milk cows. In this case, we will focus our attention on the flow of cows from birth through maturity into the milk cow accumulator. The annual milk production is then determined by the number of milk cows at any one time and the amount of milk per cow. Of course, there are many other factors that affect milk production, such as food supplies, milk demand, and dairy farmers. These factors could also be added to our diagram in the form of additional accumulators and flows.

The resulting model can then be simulated on a computer to see how annual milk production behaves over time. To create our hypothetical epidemic scenario, for example, we would simply put zero for the stock of cows. In that event, the annual milk production would also equal zero. Because this model is tied to the structure of the system, not just historical data, it would not have to be thrown out even if all of the cows suddenly died.

Levels of Explanation

We live in the world of events. As a result, we encounter and navigate through the rapids of life on an event by-event basis. But this does not mean that we must act on each event as if it were an isolated occurrence. We can look at patterns of behavior over time and try to glean lessons from the past that will improve our ability to handle present situations. That is the purpose of forecasting models.

Forecasting models, like the economist’s milk production model described above, attempt to provide information about the future by looking at the past. But in many ways, managing on the basis of forecasts is a lot like trying to drive a car by looking through the rearview mirror. When does it work best? When the road is straight and there are no obstacles in the way. When does it fail? The rest of the time! When using a forecasting model, you only realize you have missed a turn once you see the cliff’s edge behind you and feel the sensation of free fall hit your stomach.

Forecasting provides very little insight into what actually produces the observed behavior. Consequently, it allows us to anticipate and react to changes only if they do not deviate too much from past behavior. Models, on the other hand, capture the structural forces at work and are therefore less situation-dependent. To come back to the vice president’s question, structural thinking provides a more stable basis of understanding that will last even through times of turbulent change.

Generic Thinking Skills

SALES GROWTH MODEL

If we replace the names of the variables in the “Milk Production Model” with those listed above, we can create a model that explores sales growth. The same generic resource development structure can be used to describe both processes.

If we begin to view the world through a structural perspective, another benefit emerges—the ability to transfer insight. This ability to see similar structures occurring in diverse settings is referred to as “generic thinking,” and the structures themselves are referred to as “generic structures.”

For example, if we take the “Milk Production Model” and substitute “hires” for “births,”, “trainees” for “calves,” and “sales managers” for “milk cows,” we can transform the milk cow model into a model that can be used to explore the structural forces that influence annual sales (see “Sales Growth Model”). The same generic resource development structure underlies both models. Although we may debate whether it takes longer to produce a milk cow or a sales manager, we can agree that the structure of both processes is fundamentally the same.

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.

For further reading about structural thinking and the other critical thinking skills included under the systems thinking umbrella, see Barry Richmond’s The Thinking in Systems Thinking: Seven Essential Skills (Pegasus Communications, 2000).

The post Structural Thinking: The World According to Accumulators and Flows appeared first on The Systems Thinker.

When we run the bath water, we can visually see the water accumulating in the tub (see “Bathtubs and Accumulators”). We know we have to keep an eye on the water level so it won’t overflow. When we take showers, however, the accumulation process is virtually eliminated. Water flows out of the showerhead, over our bodies, and out the drain. Where does the water go? We hardly give it any thought.

BATHTUBS AND ACCUMULATORS

The core building blocks of dynamic thinking tools are reinforcing and balancing loops. The analogous elements in the structural thinking set of tools are accumulators and flows. An accumulator (or stock) is represented by a rectangular box, and the flow (or rate) is represented by a pipe with a directional arrow, a valve, and a circle. The circle and the box each contain a timeline graph as a visual reminder that the dynamics of the two are intimately connected. For example, the constant flow from the circle to the box as indicated in the diagram must produce the straight linear rise in the water level. No other behavior is possible for that structure as it is drawn.

Showerhead vs. Bathtub Thinking

Taking showers disconnects us from experiencing one of nature’s most basic structures—accumulators. Lakes and ponds are accumulators of various water flows. Global warming has been attributed to the cumulative effects of burning fossil fuels. Plants are accumulators of energy and nutrition. Displacement, velocity, and acceleration can be represented in terms of accumulators. That is, displacement represents the accumulation of past velocity, and velocity is an accumulation of past acceleration.

If we use showerhead thinking, we are less conscious of accumulations. Flows of materials such as water, fuel, or energy simply “go away” somewhere. But from a bathtub—or systems —perspective, there is no “away.” Everything accumulates somewhere. Forgetting about that “somewhere” can lead to disastrous results.

When Just-in-Time (JIT) manufacturing first hit the U. S., for example, many companies implemented it using a showerhead perspective. The basic concept of JIT is to manage a steady flow of materials through a factory with minimal accumulations of inventory at each step. Many companies that instituted JIT tried to minimize their own accumulations by demanding that their suppliers provide them with materials just when they needed them and not any sooner.

The problem with the above approach, of course, is that the flow of materials has to accumulate somewhere, and it was accumulating in the suppliers’ warehouses. The JIT flow was accomplished by shifting the accumulations to suppliers, severely straining the relationship between suppliers and manufacturers. Bathtub thinking would have highlighted the fact that unless the entire flow from raw materials to final customer worked together, there would be undesirable accumulations for somebody in the system.

Invisible Bathtubs

When’s the last time you actually let a bathtub overflow? Probably not in a long time. Of course, we all know not to let the water run indefinitely, because the tub has a limited capacity. The tub’s dimensions are obvious and so is the rising water line. But suppose the bathtub is invisible, and so is the water once it leaves the faucet. And suppose you are not in the bathroom to keep an eye on the tub—you are off answering phone calls and dealing with the latest crisis at the office. How will you know when the bathtub is getting full or already overflowing?

Flows are easy to keep track of because they involve action, and actions are easy to measure—how many products to ship, how many people to hire, for example. Some accumulations are also very visible, such as order backlogs or bulging inventories. There are, however, many accumulations that are not tangible but nonetheless very real. These possess the same behavioral characteristics as physical accumulators and flows, but they are like invisible bathtubs—we can never tell for sure whether they are overflowing or not.

Identifying Accumulations

STRESS ACCUMULATOR

Increasing work pressure can lead to an increased number of stressful events, which adds to the accumulation of stress.

So how can you locate the “invisible bathtubs” lurking in your company? For every flow (action, decision, policy), try to figure out what, if anything, is accumulating and what are the implications of those accumulations.

For example, as workload outstrips capacity and work pressures become excessively high (see “Stress Accumulator”), you should question whether those pressures simply come and go or whether their effects are accumulating somehow. For example, extra pressure may generate more stressful events, which will accumulate into increasing levels of stress. High stress levels will then lead to lower productivity, which further reduces work capacity and leads to more stressful events. This reinforcing loop of accumulating stress is intangible, yet all too real for many people.

If you look at the situation from the accumulator viewpoint and trace out the reinforcing loop, it becomes clear why typical stress reduction efforts do not work very well. Each round of stressful events produces more stress, like compound interest in a savings account. And coping mechanisms are like savings withdrawals—unless you withdraw more than you are earning in interest, the account balance never goes down. Likewise, if the stress “withdrawal” rate (coping mechanisms) are not exceeding the stress “interest” rate (stressful events), then the best you can do is learn to live with the higher stress level. From the accumulator perspective, the high-leverage action would be to “close the account” by reducing or eliminating the real source of stress.

Loop Diagrams vs. Accumulators and Flows

If causal loop diagrams and systems archetypes are such powerful tools, why do we need to bother with accumulators and flows? Both tools have their unique strengths. Tools like systems archetypes capture and communicate dynamic issues in a concise way, but they do not provide a detailed representation of the structure producing the dynamics.

There are cases when tracing through a loop diagram can be confusing. For example:, “Savings and interest form a reinforcing loop where higher savings balance leads to higher interest payments, which leads to still higher savings (see “From Loop Diagrams to Accumulators and Flows”). If we start making withdrawals, the balance goes down and interest payments decrease, but savings does not decrease. It still increases but at a decreased rate.” Sound confusing? That’s where the accumulator and flow diagram can help you actually visualize how that loop works in terms of the flow of money into and out of the account.

FROM LOOP DIAGRAMS TO ACCUMULATORS AND FLOWS

A free market economy is a lot like a seesaw with supply at one end and demand on the other. The dynamics that result from trying to balance supply and demand are produced by two balancing loops that try to stabilize on a particular price. Due to the presence of significant delays, a cycle of overshoot and collapse occurs.

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.

The post Accumulators: Bathtubs, Bathtubs Everywhere… appeared first on The Systems Thinker.

A Stock and Flow Depiction

Fortunately, analogies and simple, everyday examples can help us understand what’s really going on. For instance, a common way to illustrate some of the core behaviors of systems is through the example of a bathtub. A bathtub—or a stock—is something that holds different levels of water. That level is affected by the rates at which water both enters and drains from the tub— these are know as flows. A stock can be anything that accumulates, such as money in a bank account or greenhouse gases in the atmosphere, while a flow affects the way that the stock changes over time, such as through deposits or withdrawals—of money or pollutants. Recognizing how changes in flows affect associated stocks can go a long way toward improving our decision-making processes.

An Attention-Grabbing Illustration

Weight-loss Scenario

In a recent Op-Ed piece in The New YorkTimes, columnist Gail Collins chooses an attention-grabbing image to dramatize the Bush administration’s policies on climate change—and to shed light on the stocks and flows involved. According to Collins, in a press conference in April, the president announced that the U. S. is on track for reducing the growth of greenhouse gas emissions by 18 percent by 2012. Sounds promising, until you consider what that statistic actually means.

To put this goal in more tangible terms, Collins translates it into something that many of us can relate to— weigh gain. She quips, “Suppose that two years after taking office, George W. Bush discovered that because of the stress of his job, he had gained 40 pounds and was tipping the scales at 220.” If the president set a goal of reducing the rate at which he was gaining weight by 18 percent, to be achieved within the next decade, by 2012, he would weigh 400 pounds.

In the press conference, Bush made an additional commitment of stopping the growth of U. S. greenhouse gas emissions entirely by 2025. To continue with the tongue-in-cheek scenario, Collins asks the reader, “Imagine it’s 2025, and you’ve got a 486-pound ex-president being wheeled in to accept the congratulations of the world on his excellent physical fitness program.” Not such impressive results, unless the president is looking to launch a career as a sumo wrestler after leaving the White House.

Reality Check

Collins’s point is that, under this policy, greenhouse gases will continue to flood into the atmosphere, just a little more slowly than they might have otherwise. But without testing the implications of various strategies, we often make unrealistic assumptions about the results they may produce. We end up relying on the goodwill of politicians to spell out the effects that their policies are designed to produce—or on the media to connect the dots for us. Looking at how proposed laws, regulations, and other interventions might play out over time can help us better judge their effectiveness— and might lead us to advocate for alternative approaches instead.

Janice Molloy is content director at Pegasus Communications and managing editor of The Systems Thinker. Thanks to Bill Harris of Facilitated Systems (www.facilitatedsystems.com) for serving as a thought partner and creating the diagrams.

The post A Weighty Take on Stocks and Flows appeared first on The Systems Thinker.

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.

The post Applying System Dynamics to Public Policy: The Legacy of Barry Richmond appeared first on The Systems Thinker.