Thinking in Systems: A Primer

by Donella H. Meadows

Every person we encounter, every organization, every animal, garden, tree, and forest is a complex system. We have built up intuitively, without analysis, often without words, a practical understanding of how these systems work, and how to work with them.

Hunger, poverty, environmental degradation, economic instability, unemployment, chronic disease, drug addiction, and war, for example, persist in spite of the analytical ability and technical brilliance that have been directed toward eradicating them. No one deliberately creates those problems, no one wants them to persist, but they persist nonetheless. That is because they are intrinsically systems problems—undesirable behaviors characteristic of the system structures that produce them.

No one understands all the relationships that allow a tree to do what it does. That lack of knowledge is not surprising. It’s easier to learn about a system’s elements than about its interconnections.

If a government proclaims its interest in protecting the environment but allocates little money or effort toward that goal, environmental protection is not, in fact, the government’s purpose. Purposes are deduced from behavior, not from rhetoric or stated goals.

An important function of almost every system is to ensure its own perpetuation.

System purposes need not be human purposes and are not necessarily those intended by any single actor within the system. In fact, one of the most frustrating aspects of systems is that the purposes of subunits may add up to an overall behavior that no one wants.

Keeping sub-purposes and overall system purposes in harmony is an essential function of successful systems.

Another term for alignment, in a way

A system generally goes on being itself, changing only slowly if at all, even with complete substitutions of its elements—as long as its interconnections and purposes remain intact.

All models, whether mental models or mathematical models, are simplifications of the real world.

All are wrong. Some useful.

The human mind seems to focus more easily on stocks than on flows. On top of that, when we do focus on flows, we tend to focus on inflows more easily than on outflows. Therefore, we sometimes miss seeing that we can fill a bathtub not only by increasing the inflow rate, but also by decreasing the outflow rate. Everyone understands that you can prolong the life of an oil-based economy by discovering new oil deposits. It seems to be harder to understand that the same result can be achieved by burning less oil.

a company can build up a larger workforce by more hiring, or it can do the same thing by reducing the rates of quitting and firing. These two strategies may have very different costs.

Stocks generally change slowly, even when the flows into or out of them change suddenly. Therefore, stocks act as delays or buffers or shock absorbers in systems.

Stocks allow inflows and outflows to be decoupled and to be independent and temporarily out of balance with each other.

Re toyota production anthe path to minimizing stock by resducing variationa in flow.

shortcut: The time it takes for an exponentially growing stock to double in size, the “doubling time,” equals approximately 70 divided by the growth rate (expressed as a percentage). Example: If you put $100 in the bank at 7% interest per year, you will double your money in 10 years (70 ÷ 7 = 10). If you get only 5% interest, your money will take 14 years to double.

Dynamic systems studies usually are not designed to predict what will happen. Rather, they’re designed to explore what would happen, if a number of driving factors unfold in a range of different ways.

QUESTIONS FOR TESTING THE VALUE OF A MODEL Are the driving factors likely to unfold this way? If they did, would the system react this way? What is driving the driving factors?

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

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

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.

Resilience is not the same thing as being static or constant over time. Resilient systems can be very dynamic. Short-term oscillations, or periodic outbreaks, or long cycles of succession, climax, and collapse may in fact be the normal condition, which resilience acts to restore! And, conversely, systems that are constant over time can be unresilient. This distinction between static stability and resilience is important. Static stability is something you can see; it’s measured by variation in the condition of a system week by week or year by year. Resilience is something that may be very hard to see, unless you exceed its limits, overwhelm and damage the balancing loops, and the system structure breaks down.

Wonder if this relates to why higher variability in heart rate (HRV) is good.

Large organizations of all kinds, from corporations to governments, lose their resilience simply because the feedback mechanisms by which they sense and respond to their environment have to travel through too many layers of delay and distortion.

Which is why it's important to build effective ways of decentralization.

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.

The agricultural revolution and all that followed started with the simple, shocking ideas that people could stay settled in one place, own land, select and cultivate crops.

Scott would argue that that's not how it started. It's more about domination and power.

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.

So the stability of the intermediate form is required to make ot more likely that the largel whole could form. Otherwise it wohuld be less likely for all the pieces to be in the right place at the right time.

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.

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.

And that’s one reason why systems of all kinds surprise us. We are too fascinated by the events they generate. We pay too little attention to their history. And we are insufficiently skilled at seeing in their history clues to the structures from which behavior and events flow.

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.

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

Aligned with Goldrat's theory of constraints

Rich countries transfer capital or technology to poor ones and wonder why the economies of the receiving countries still don’t develop, never thinking that capital or technology may not be the most limiting factors.

Simplle interventions that don't recognize the complexity of the system and don't follow an experimental approach that would be appropriate for the complexity of the system. Also: all complex systems evolve from less complex systems that work.

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.

To shift attention from the abundant factors to the next potential limiting factor is to gain real understanding of, and control over, the growth process.

If a city meets the needs of all its inhabitants better than any other city, people will flock there until some limit brings down the city’s ability to satisfy peoples’ needs.6 There always will be limits to growth. They can be self-imposed. If they aren’t, they will be system-imposed.

I had wanted to make history move ahead in the same way that a child pulls on a plant to make it grow more quickly. I believe we must learn to wait as we learn to create. We have to patiently sow the seeds, assiduously water the earth where they are sown and give the plants the time that is their own. One cannot fool a plant any more than one can fool history.

If you’re worrying about oscillations that take weeks, you probably don’t have to think about delays that take minutes, or years. If you’re concerned about the decades-long development of a population and economy, you usually can ignore oscillations that take weeks. The world peeps, squawks, bangs, and thunders at many frequencies all at once. What is a significant delay depends—usually—on which set of frequencies you’re trying to understand.

Changing the length of a delay may utterly change behavior. Delays are often sensitive leverage points for policy, if they can be made shorter or longer. You can see why that is. If a decision point in a system (or a person working in that part of the system) is responding to delayed information, or responding with a delay, the decisions will be off target. Actions will be too much or too little to achieve the decision maker’s goals. On the other hand, if action is taken too fast, it may nervously amplify short-term variation and create unnecessary instability. Delays determine how fast systems can react, how accurately they hit their targets, and how timely is the information passed around a system. Overshoots, oscillations, and collapses are always caused by delays.

We teach this point by playing games in which students are put into situations in which they experience the realistic, partial information streams seen by various actors in real systems. As simulated fishermen, they overfish. As ministers of simulated developing nations, they favor the needs of their industries over the needs of their people. As the upper class, they feather their own nests; as the lower class, they become apathetic or rebellious. So would you.

Seeing how individual decisions are rational within the bounds of the information available does not provide an excuse for narrow-minded behavior. It provides an understanding of why that behavior arises.

Taking out one individual from a position of bounded rationality and putting in another person is not likely to make much difference. Blaming the individual rarely helps create a more desirable outcome. Change comes first from stepping outside the limited information that can be seen from any single place in the system and getting an overview. From a wider perspective, information flows, goals, incentives, and disincentives can be restructured so that separate, bounded, rational actions do add up to results that everyone desires.

Re Deming and statistical control and management needing to work on the system not punishing individuals.

The bounded rationality of each actor in a system may not lead to decisions that further the welfare of the system as a whole.

Being less surprised by complex systems is mainly a matter of learning to expect, appreciate, and use the world’s complexity.

The alternative to overpowering policy resistance is so counterintuitive that it’s usually unthinkable. Let go. Give up ineffective policies. Let the resources and energy spent on both enforcing and resisting be used for more constructive purposes. You won’t get your way with the system, but it won’t go as far in a bad direction as you think, because much of the action you were trying to correct was in response to your own action.

The most effective way of dealing with policy resistance is to find a way of aligning the various goals of the subsystems, usually by providing an overarching goal that allows all actors to break out of their bounded rationality.

Re shared goal in four steps to change without drama

Some people think the fall of the communist Soviet Union has disproved the theories of Karl Marx, but this particular analysis of his—that market competition systematically eliminates market competition—is demonstrated wherever there is, or used to be, a competitive market.

Whoever gets an edge can grow faster to get an even bigger edge and will eventually push out the competition.

Addiction is finding a quick and dirty solution to the symptom of the problem, which prevents or distracts one from the harder and longer-term task of solving the real problem. Addictive policies are insidious, because they are so easy to sell, so simple to fall for.

The problem can be avoided up front by intervening in such a way as to strengthen the ability of the system to shoulder its own burdens. This option, helping the system to help itself, can be much cheaper and easier than taking over and running the system

Also the idea behind coaching in leadership. It avoids creating a dependency and weakening own capabilities.

If you are the intervenor, work in such a way as to restore or enhance the system’s own ability to solve its problems, then remove yourself.

Systems, like the three wishes in the traditional fairy tale, have a terrible tendency to produce exactly and only what you ask them to produce. Be careful what you ask them to produce. If the desired system state is national security, and that is defined as the amount of money spent on the military, the system will produce military spending. It may or may not produce national security. In fact, security may be undermined if the spending drains investment from other parts of the economy, and if the spending goes for exorbitant, unnecessary, or unworkable weapons. If the desired system state is good education, measuring that goal by the amount of money spent per student will ensure money spent per student.

This is why triangulation is needed and goals need to initiative specific not long lasting KPIs.

GNP is a measure of throughput—flows of stuff made and purchased in a year—rather than capital stocks, the houses and cars and computers and stereos that are the source of real wealth and real pleasure. It could be argued that the best society would be one in which capital stocks can be maintained and used with the lowest possible throughput, rather than the highest. Although there is every reason to want a thriving economy, there is no particular reason to want the GNP to go up.

If you define the goal of a society as GNP, that society will do its best to produce GNP. It will not produce welfare, equity, justice, or efficiency unless you define a goal and regularly measure and report the state of welfare, equity, justice, or efficiency.