In systems thinking terms, a delay is when the effect of an action occurs after a break in time. The break may be seconds or years, but in real life, waiting out a delay without intervening can seem interminable. We live with a multitude of system delays in our lives and they can be frustrating.

• The time between planting seeds and harvesting vegetables or flowers
• The time between starting a manufacturing process and having a finished, functioning product
• The time between arriving at the check-out line at the supermarket and heading home with groceries in the car
• The movement from summer to fall to winter to spring
• The time between the first inkling of a creative idea and the completion of the painting/novel/software program
• The movement of children through developmental stages
• The ups and downs of the stock market

“Do Something”— The Struggle for Control

When we act and don’t immediately see results, we feel compelled to do more. In our organizations today, we believe that one of the best ways to improve a system’s performance is to manage its delays, which often means reducing or eliminating them. A good example is offered by Logli Supermarkets in Rockford, Illinois. Logli sells more groceries than any other supermarket in Illinois. Reasons for their success are obvious to any customer. With 23 check-out lines available at all times and a system of free drive-up service, where teams of efficient young people load groceries into your car, the delay from entering the check-out line to driving home is all but eliminated.

Sometimes managing a delay means making it more palatable, which is why decorating physicians’ offices has become a popular, new interior design niche. When patients find waiting to see the doctor comfortable and interesting, they are less likely to complain about how long it’s taking.

But most of the time, when we try to manage delays, we are in crisis mode. We move quickly, coming up with fixes that may have negative, unintended consequences. Much of the time we don’t even realize that we’re experiencing a delay. When we act and don’t immediately see results, we feel compelled to do more before we even experience the outcome of our initial intervention. Doing something, anything, reduces our anxiety and makes us feel more in control, even if we’re really making things worse over the long run. But acting in these circumstances can lead to over-correction, much like what happened when I caused the houseboat to zig and zag all over the river.

“Do Nothing”— Trusting the Process

So how can we overcome our impulse to act, whatever the consequences? A good first step may be to see and acknowledge the delays in the system. For example, when we reach a juncture where our performance seems to have plateaued or a problem symptom isn’t improving, we can say, “We may have hit a classic delay.” Especially if we can’t change a delay, we must respect and trust it. If our patience is still wearing thin, we can ask a few questions before taking action:

• “If we do something, what will happen? Will we create additional delays or problems down the line?”
• “If we do nothing, what will happen?”
• “What can we do to live with our anxiety while we figure out the best response?”

A second approach to managing delays is to manage yourself. Sitting on my hands and breathing deeply eventually helped me stop over-correcting the houseboat. Reading about and talking with other parents about typical behavior for a 13-year-old helped me survive my daughter Lisa’s early teens without either going crazy or taking rash action that might have caused more problems.

Managing delays in creative projects (including software development) can be tricky. Start by accepting the need for incubation and “soak” time in a creative process and build latitude into the schedule. Creative people almost always underestimate how long a project will take, because they already have a vision of the finished product. Also, many of the most creative solutions come after a period of inattention to the problem or sleep, when the limbic region of the brain is active. If we press forward too aggressively and feel pressure to create now, we never access these powerful thought processes.

Thus, managing a delay may mean doing something counter-intuitive for a while: nothing. We are a very “doing” culture, and many of us have a hard time sitting back and waiting. This kind of inaction in the face of an ongoing challenge requires a great deal of trust in the process.

When aerospace manufacturer Woodward Governor sought to reduce delays in the production of aircraft engine controls, after several failed interventions, the organization finally decided to stop work-arounds. Previously, if a group on the assembly line was missing certain parts, they borrowed them from other teams. Over time, this pattern of borrowing backfired. It was hard to keep track of parts borrowed from various projects. They were seldom replaced in a timely way. So when the original team needed the borrowed parts back, they had to spend time tracking them down and often resorted to borrowing them from somewhere else in the plant—another time-consuming work-around. In their eagerness to keep products rolling, workers had unintentionally slowed down the entire plant.

To reduce delays, people had to be willing to do nothing. When they were short of parts, instead of borrowing, they waited to receive a new shipment of inventory. After a while, to everyone’s amazement, the plant began to meet deadlines consistently. As they finished orders on time, they stopped having a backlog of work. At first employees felt uncomfortable, because they worried that the work was running out. In a short time, however, they got used to this more regular stream of activity and found their jobs much less stressful. Workers were happy about going home earlier. Customers were delighted with the on-time deliveries. Woodward Governor had successfully managed the delays in their manufacturing system.

In some cases, the best response to a system delay is to say the Serenity Prayer. (This may seem corny, but it can help.) “God grant me the serenity To accept the things I cannot change, The courage to change the things I can, And the wisdom to know the difference.”

When we stop spending energy trying to change things that are not going to change no matter what we do, we have more energy to work on those things on which we can have an impact.

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Weighing the Risk

In recent years, groups that oppose vaccinations because of their potential health risks have sprung up. For instance, some activists claim that the mumps, measles, rubella vaccine is linked to autism, although medical groups studying the possible connection have concluded that the vaccine is not to blame. Anti-immunization groups also doubt the government’s ability to oversee vaccine safety, pointing to, among other things, its delay in banning mercury from injections, despite the fact that it can impair children’s cognitive development.

In response to such concerns, more and more people are choosing not to vaccinate. When weighing the risk of contracting vaccine-preventable diseases against that of experiencing one of the rare catastrophic reactions to the vaccine itself, they are banking on current low levels of infection and deciding to avoid the injections.

Health officials acknowledge that vaccines can cause side effects, ranging from mild (temporary pain at the injection site) to serious (between 1960 to 1999, 8 to 10 children a year in the U. S. contracted paralytic polio from the oral polio vaccine).

But they also point out that as more people avoid immunization, the incidence of certain serious diseases is bound to rise. As just one example, the Consumer Reports article cites the case of Mary Catherine Walther, who contracted Hib meningitis on her first birthday. Her local hospital in Tennessee hadn’t treated a case of the illness for eight years, since the introduction of a vaccine against it. Fortunately, the toddler recovered.

THE SWING OF RELATIVE RISK

As the incidence of a disease rises, people’s perception of the risk to their own health increases. Under these conditions, they are more likely to overlook the vaccine’s side effects. Use of the vaccine reduces the incidence of the disease. When infection rates fall, people’s concerns about vaccine safety grow. If enough people choose not to use the vaccine, the disease begins to spread again.

One reason that formerly dormant diseases can reappear is that they haven’t yet been eradicated worldwide. Travelers from countries where immunization programs have been limited can carry a disease to other regions. Or such illnesses could reemerge through more diabolical means. In June, a simulation exercise depicted a smallpox attack by terrorists that infected 24 people in Oklahoma. After an imaginary two weeks, 16,000 people in 25 states were infected; 1,000 were dead; and 10 other countries reported cases. Following these trends, within three weeks, there would be 300,000 victims, a third of whom would die. Without continued vigilance, such an epidemic could also happen with other serious illnesses that we have long thought were cured.

Relative Risk

The pendulum swing between concerns about disease to concerns about the vaccines themselves represents a classic balancing process (see “The Swing of Relative Risk”). When the threat of a specific disease is high, the vaccine’s desirability rises, regardless of safety concerns. When incidences of the disease are few and far between, people start raising questions about the vaccine’s side effects.

Rather than writing off such concerns as irrational, by recognizing this dynamic, public-health officials can anticipate and manage them through ongoing investments in vaccine safety, education, and immunization programs around the world. In fact, officials might consider activists’ skepticism to be a positive force, in that it keeps pressure on manufacturers and governmental agencies to continually improve these life-saving products. After all, no one wants the cure to be worse than the sickness.

Janice Molloy is managing editor of The Systems Thinker.

The post The Risk of the Cure in Public Health appeared first on The Systems Thinker.

• Quickly capturing your hypotheses about the causes of dynamics;
• Eliciting and capturing the mental models of individuals and teams;
• Communicating the important feedback processes you believe are responsible for a problem.

The conventions for drawing CLDs are simple but should be followed faithfully. Think of CLDs as musical scores: At first, you may find it difficult to construct and interpret these diagrams, but with practice, you will soon be sight-reading. In this article, I present some important guidelines that can help you make sure your CLDs are accurate and effective in capturing and communicating the feedback structure of complex systems.

Avoid Ambiguity in Labeling Causal Links

AMBIGUITY OF LINKS

To be effective, your CLD should not include any ambiguous causal links. Ambiguous polarities usually mean there are multiple causal pathways that you should show separately.

People sometimes argue that a specific link in a CLD can be either positive or negative, depending on other parameters or on where the system is operating. For example, we might draw a diagram that relates a firm’s revenue to the price of its product and then argue that the link between price and company revenue can be either positive or negative, depending on the elasticity of demand (see “Ambiguity of Links”). A higher price means less revenue if a 1 percent increase in price causes demand to fall more than 1 percent. This link would be labeled with a negative sign. But less elastic demand might mean a 1 percent increase in price causes demand to fall less than 1 percent, so revenues would then rise, resulting in a positive link polarity.

When you have trouble assigning a clear and unambiguous sign to a link, it usually means there is more than one causal pathway connecting the two variables. You should make these different pathways explicit in your diagram. The correct diagram for the impact of price on revenue would show that price has at least two effects on revenue: (1) it determines how much revenue is generated per unit sold (a positive link), and (2) it affects the number of units sold (usually a negative link).

Is It Reinforcing or Balancing?

There are two methods for determining whether a loop is reinforcing or balancing: the fast way and the right way. The fast way, which you may have learned when you first started working with CLDs, is to count the number of negative links—represented by “-” or “o”—in the loop (see “‘+’ and ‘-’ Vs. ‘s’ and ‘o’”). If the number is even, the loop is reinforcing; if the number is odd, the loop is balancing. However, this method can sometimes fail, because it is all too easy to mislabel a link’s polarity or miscount the number of negative links.

The right way is to trace the effect of a small change in one of the variables around the loop. Pick any variable in the loop. Now imagine that it has changed (increased or decreased), and trace the effect of this change around the loop. If the change feeds back to reinforce the original change, it is a reinforcing loop. If it opposes the original change, it is a balancing loop. This method works no matter how many variables are in a loop and no matter where you start.

Make the Goals of Balancing Loops Explicit

All balancing loops have goals, which are the system’s desired state. Balancing loops function by comparing the actual state to the goal, then initiating a corrective action in response to the discrepancy between the two. It is often helpful to make the goals of your balancing loops explicit, usually by adding a new variable, such as “desired product quality” (see Desired Product Quality in “Explicit Goals”). The diagram shows a balancing loop that affects the quality of a company’s product: The lower the quality, the more quality improvement programs the company initiates, which, if successful, correct the quality shortfall.

EXPLICIT GOALS

Making goals explicit in balancing loops encourages people to ask questions about how the goals are formed. For example, what drives a company’s desired level of quality?

Making goals explicit encourages people to ask how the goals are formed; for instance, who determines desired product quality and what criteria do they use to make that determination? Hypotheses about the answers to these questions can then be incorporated in the diagram. Goals can vary over time and respond to pressures in the environment, such as customer input or the quality of competing products.

Making the goals of balancing loops explicit is especially important when the loops capture human behavior—showing the goals prompts reflection and conversation about the aspirations and motives of the actors. But often it is important to represent goals explicitly even when the loop doesn’t involve people at all.

Represent Causation Rather Than Correlation

Every link in your diagram must represent what you and your colleagues believe to be causal relationships between the variables. In a causal relationship, one variable has a direct effect on another; for instance, a change in the birth rate alters the total population. You must be careful not to include correlations between variables in your diagrams. Correlations between variables reflect a system’s past behavior, not its underlying structure. If circumstances change, if previously dormant feedback loops become dominant, or if you experiment with new decisions and policies, previously reliable correlations among variables may break down.

ICE-CREAM SALES AND MURDERS

Causal loop diagrams must include only what you believe to be genuine causal relationships, never correlations, no matter how strong.

For example, though sales of ice cream are positively correlated with the murder rate, you may not include a link from ice-cream sales to murder in your CLD. Such a causal link suggests that cutting ice-cream consumption would slash the murder rate and allow society to cut the budget for police and prisons. Obviously, this is not the case: Both ice-cream consumption and violent crime tend to rise in hot weather. But the example illustrates how confusing correlations with causality can lead to terrible misjudgments and policy errors (see “Ice Cream Sales and Murders”).

While few people are likely to attribute murders to the occasional double-dip cone, many correlations are more subtle, and it is often difficult to determine the underlying causal structure. A great deal of scientific research seeks the causal needles in a huge haystack of correlations: Can eating oat bran reduce cholesterol, and if it does, will your risk of a heart attack drop? Does economic growth lead to lower birth rates, or is the lower rate attributable to literacy, education for women, and increasing costs of child-rearing?

Do companies with serious quality improvement programs earn superior returns for stockholders?

Scientists have learned from experience that reliable answers to such questions are hard to come by and require dedication to the scientific method—controlled experiments; randomized, double-blind trials; large samples; long-term followup studies; replication; statistical inference; and so on. In social and human systems, such experiments are difficult, rare, and often impossible. You must take extra care to determine that the relationships in your CLDs are causal, no matter how strong a correlation may be.

John D. Sterman is the J. Spencer Standish Professor of Management at the Sloan School of Management of the Massachusetts Institute of Technology and director of MIT’s System Dynamics Group.

This article is part of a 2-part series. Click here to view the second part.

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