Thinking in Systems: A Primer

by Donella H. Meadows

A System with Delays—Business Inventory

Try taking a shower sometime where there’s a very long pipe between the hot- and cold-water mixer and the showerhead, and you’ll experience directly the joys of hot and cold oscillations because of a long response delay.

A delay in a balancing feedback loop makes a system likely to oscillate.

Delays are pervasive in systems, and they are strong determinants of behavior. Changing the length of a delay may (or may not, depending on the type of delay and the relative lengths of other delays) make a large change in the behavior of a system.

We’re always on the alert to see where delays occur in systems, how long they are, whether they are delays in information streams or in physical processes.

Economies are extremely complex systems; they are full of balancing feedback loops with delays, and they are inherently oscillatory.

A Renewable Stock Constrained by a Nonrenewable Stock—an Oil Economy

Growth in a constrained environment is very common, so common that systems thinkers call it the “limits-to-growth” archetype.

In physical, exponentially growing systems, there must be at least one reinforcing loop driving the growth and at least one balancing loop constraining the growth, because no physical system can grow forever in a finite environment.

A quantity growing exponentially toward a constraint or limit reaches that limit in a surprisingly short time.

The higher and faster you grow, the farther and faster you fall, when you’re building up a capital stock dependent on a nonrenewable resource.

Renewable Stock Constrained by a Renewable Stock—a Fishing Economy

Nonrenewable resources are stock-limited. The entire stock is available at once, and can be extracted at any rate (limited mainly by extraction capital). But since the stock is not renewed, the faster the extraction rate, the shorter the lifetime of the resource.

Renewable resources are flow-limited. They can support extraction or harvest indefinitely, but only at a finite flow rate equal to their regeneration rate. If they are extracted faster than they regenerate, they may eventually be driven below a critical threshold and become, for all practical purposes, nonrenewable.

overshoot and adjustment to a sustainable equilibrium, overshoot beyond that equilibrium followed by oscillation around it, and overshoot followed by collapse of the resource and the industry dependent on the resource.

Neither renewable nor nonrenewable limits to growth allow a physical stock to grow forever, but the constraints they impose are dynamically quite different. The difference comes because of the difference between stocks and flows.

Chances are good that you may have observed one of three characteristics: resilience, self-organization, or hierarchy.

Resilience arises from a rich structure of many feedback loops that can work in different ways to restore a system even after a large perturbation. A single balancing loop brings a system stock back to its desired state. Resilience is provided by several such loops, operating through different mechanisms, at different time scales, and with redundancy—one kicking in if another one fails.

There are always limits to resilience.

I think of resilience as a plateau upon which the system can play, performing its normal functions in safety.

Loss of resilience can come as a surprise, because the system usually is paying much more attention to its play than to its playing space. One day it does something it has done a hundred times before and crashes.

Systems need to be managed not only for productivity or stability, they also need to be managed for resilience—the ability to recover from perturbation, the ability to restore or repair themselves.

Awareness of resilience enables one to see many ways to preserve or enhance a system’s own restorative powers.

This capacity of a system to make its own structure more complex is called self-organization.

Like resilience, self-organization is often sacrificed for purposes of short-term productivity and stability.

Self-organization produces heterogeneity and unpredictability.

Systems often have the property of self-organization—the ability to structure themselves, to create new structure, to learn, diversify, and complexify. Even complex forms of self-organization may arise from relatively simple organizing rules—or may not.

Complex systems can evolve from simple systems only if there are stable intermediate forms.

Hierarchies are brilliant systems inventions, not only because they give a system stability and resilience, but also because they reduce the amount of information that any part of the system has to keep track of.

many systems are not meeting our goals because of malfunctioning hierarchies.

When a subsystem’s goals dominate at the expense of the total system’s goals, the resulting behavior is called suboptimization.

Hierarchical systems evolve from the bottom up. The purpose of the upper layers of the hierarchy is to serve the purposes of the lower layers.

Resilience, self-organization, and hierarchy are three of the reasons dynamic systems can work so well.

Everything we think we know about the world is a model. Our models do have a strong congruence with the world. Our models fall far short of representing the real world fully.

You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long-term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities and delays. You are likely to mistreat, misdesign, or misread systems if you don’t respect their properties of resilience, self-organization, and hierarchy.

Systems fool us by presenting themselves—or we fool ourselves by seeing the world—as a series of events.

The behavior of a system is its performance over time—its growth, stagnation, decline, oscillation, randomness, or evolution.

Structure determines what behaviors are latent in the system. A goal-seeking balancing feedback loop approaches or holds a dynamic equilibrium. A reinforcing feedback loop generates exponential growth. The two of them linked together are capable of growth, decay, or equilibrium. If they also contain delays, they may produce oscillations. If they work in periodic, rapid bursts, they may produce even more surprising behaviors. System structure is the source of system behavior. System behavior reveals itself

as a series of events over time.

Systems thinking goes back and forth constantly between structure (diagrams of stocks, flows, and feedbac...

Flows go up and down, on and off, in all sorts of combinations, in response to stocks, not to other flows.

Nonlinearities are important not only because they confound our expectations about the relationship between action and response. They are even more important because they change the relative strengths of feedback loops. They can flip a system from one mode of behavior to another.

Many relationships in systems are nonlinear. Their relative strengths shift in disproportionate amounts as the stocks in the system shift. Nonlinearities in feedback systems produce shifting dominance of loops and many complexities in system behavior.

Disorderly, mixed-up borders are sources of diversity and creativity.

There is no single, legitimate boundary to draw around a system.

There are no separate systems. The world is a continuum. Where to draw a boundary around a system depends on the purpose of the discussion—the questions we want to ask.

Too often, universities are living monuments to boundary rigidity.

At any given time, the input that is most important to a system is the one that is most limiting.

There are layers of limits around every growing plant, child, epidemic, new product, technological advance, company, city, economy, and population.

Insight comes not only from recognizing which factor is limiting, but from seeing that growth itself depletes or enhances limits and therefore changes what is limiting.