Thinking in Systems: A Primer

by Donella H. Meadows

Systems theorists used to think that self-organization was such a complex property of systems that it could never be understood. Computers were used to model mechanistic, “deterministic” systems, not evolutionary ones, because it was suspected, without much thought, that evolutionary systems were simply not understandable. New discoveries, however, suggest that just a few simple organizing principles can lead to wildly diverse self-organizing structures.

“God created the universe with the earth at its center, the land with the castle at its center, and humanity with the Church at its center”—the organizing principle for the elaborate social and physical structures of Europe in the Middle Ages.

“God and morality are outmoded ideas; people should be objective and scientific, should own and multiply the means of production, and should treat people and nature as instrumental inputs to production”—the organizing principles of the Industrial Revolution.

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. The resulting complex forms will naturally be hierarchic. That may explain why hierarchies are so common in the systems nature presents to us. Among all possible complex forms, hierarchies are the only ones that have had the time to evolve.

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. In hierarchical systems relationships within each subsystem are denser and stronger than relationships between subsystems. Everything is still connected to everything else, but not equally strongly.

The original purpose of a hierarchy is always to help its originating subsystems do their jobs better. This is something, unfortunately, that both the higher and the lower levels of a greatly articulated hierarchy easily can forget. Therefore, 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. Just as damaging as suboptimization, of course, is the problem of too much central control. If the brain controlled each cell so tightly that the cell could not perform its self-maintenance functions, the whole organism could die.

To be a highly functional system, hierarchy must balance the welfare, freedoms, and responsibilities of the subsystems and total system—there must be enough central control to achieve coordination toward the large-system goal, and enough autonomy to keep all subsystems flourishing, functioning, and self-organizing.

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. Promoting or managing for these properties of a system can improve its ability to function well over the long term—to be sustainable. But watching how systems behave also can be full of surprises.

The interactions between what I think I know about dynamic systems and my experience of the real world never fails to be humbling. They keep reminding me of three truths: Everything we think we know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models. So are the ways I picture the world in my head—my mental models. None of these is or ever will be the real world. Our models usually have a strong congruence with the world. That is why we are such a successful species in the biosphere. Especially complex and sophisticated are the mental models we develop from direct, intimate experience of nature, people, and organizations immediately around us. However, and conversely, our models fall far short of representing the world fully. That is why we make mistakes and why we are regularly surprised. In our heads, we can keep track of only a few variables at one time. We often draw illogical conclusions from accurate assumptions, or logical conclusions from inaccurate assumptions. Most of us, for instance, are surprised by the amount of growth an exponential process can generate. Few of us can intuit how to damp oscillations in a complex system.

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.

A system is a big black box Of which we can’t unlock the locks, And all we can find out about Is what goes in and what comes out. Perceiving input-output pairs, Related by parameters, Permits us, sometimes, to relate An input, output and a state. If this relation’s good and stable Then to predict we may be able, But if this fails us—heaven forbid! We’ll be compelled to force the lid! —Kenneth Boulding,2 economist