Thinking in Systems: A Primer

by Donella H. Meadows

A system* is an interconnected set of elements that is coherently organized in a way that achieves something. If you look at that definition closely for a minute, you can see that a system must consist of three kinds of things: elements, interconnections, and a function or purpose.

Purposes are deduced from behavior, not from rhetoric or stated goals.

The information delivered by a feedback loop can only affect future behavior; it can’t deliver the information, and so can’t have an impact fast enough to correct behavior that drove the current feedback.

Productivity and stability are the usual excuses for turning creative human beings into mechanical adjuncts to production processes.

Self-organization produces heterogeneity and unpredictability. It is likely to come up with whole new structures, whole new ways of doing things. It requires freedom and experimentation, and a certain amount of disorder. These conditions that encourage self-organization often can be scary for individuals and threatening to power structures.

Justus von Liebig came up with his famous “law of the minimum.” It doesn’t matter how much nitrogen is available to the grain, he said, if what’s short is phosphorus. It does no good to pour on more phosphorus, if the problem is low potassium.

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

Bounded rationality means that people make quite reasonable decisions based on the information they have. But they don’t have perfect information, especially about more distant parts of the system.

Economic theory as derived from Adam Smith assumes first that homo economicus acts with perfect optimality on complete information,

and second that when many of the species homo economicus do that, their actions add up to the best possible outcome for everybody. Neither of these assumptions stands up long against the evidence.

Democracy works better without the brainwashing power of centralized mass communications.

What’s appropriate when you’re learning is small steps, constant monitoring, and a willingness to change course as you find out more about where it’s leading.

Another part of us recognizes instinctively that nature designs in fractals, with intriguing detail on every scale from the microscopic to the macroscopic. That part of us makes Gothic cathedrals and Persian carpets, symphonies and novels, Mardi Gras costumes and artificial intelligence programs, all with embellishments almost as complex as the ones we find in the world around us.