Thinking In Systems Quotes

“Designing a system for intrinsic responsibility could mean, for example, requiring all towns or companies that emit wastewater into a stream to place their intake pipes downstream from their outflow pipe.”
― Donella H. Meadows, Thinking in Systems

“I have yet to see any problem, however complicated, which, when looked at in the right way, did not become still more complicated.”
― Donella H. Meadows, Thinking in Systems: A Primer

“Once we see the relationship between structure and behavior, we can begin to understand how systems work, what makes them produce poor results, and how to shift them into better behavior patterns.”
― Donella H. Meadows, Thinking in Systems: A Primer

“Managers do not solve problems, they manage messes. —RUSSELL ACKOFF,1 operations theorist”
― Donella H. Meadows, Thinking in Systems: A Primer

“have yet to see any problem, however complicated, which, when looked at in the right way, did not become still more complicated. —POUL ANDERSON1”
― Donella H. Meadows, Thinking in Systems: A Primer

“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—something liberal politicians don’t seem to understand. The secret is to begin not with a heroic takeover, but with a series of questions. Why are the natural correction mechanisms failing? How can obstacles to their success be removed? How can mechanisms for their success be made more effective?”
― Donella H. Meadows, Thinking in Systems: A Primer

“Clouds stand for the beginnings and ends of flows. They are stocks—sources and sinks—that are being ignored at the moment for the purposes of simplifying the present discussion. They mark the boundary of the system diagram. They rarely mark a real boundary, because systems rarely have real boundaries. Everything, as they say, is connected to everything else, and not neatly. There is no clearly determinable boundary between the sea and the land, between sociology and anthropology, between an automobile’s exhaust and your nose. There are only boundaries of word, thought, perception, and social agreement—artificial, mental-model boundaries.”
― Donella H. Meadows, Thinking in Systems: A Primer

“When someone tells you that population growth causes poverty, you’ll ask yourself how poverty may cause population growth.”
― Donella H. Meadows, Thinking in Systems: A Primer

“Try thinking about that yourself. The more you do, the more you’ll begin to see feedback loops everywhere.”
― Donella H. Meadows, Thinking in Systems: A Primer

“For example, a nation wanting to increase its birth rate might ask why families are having few children and discover that it isn’t because they don’t like children. Perhaps they haven’t the resources, the living space, the time, or the security to have more.”
― Donella H. Meadows, Thinking in Systems: A Primer

“These conditions that encourage self-organization often can be scary for individuals and threatening to power structures. As a consequence, education systems may restrict the creative powers of children instead of stimulating those powers. Economic policies may lean toward supporting established, powerful enterprises rather than upstart, new ones. And many governments prefer their people not to be too self-organizing.”
― Donella H. Meadows, Thinking in Systems: A Primer

“I have yet to see any problem, however complicated, which, when looked at in the right way, did not become still more complicated. —POUL ANDERSON1”
― Donella H. Meadows, Thinking in Systems: A Primer

“It takes time for a plant or a forest or a democracy to grow; time for letters put into a mailbox to reach their destinations; time for consumers to absorb information about changing prices and alter their buying behavior, or for a nuclear power plant to be built, or a machine to wear out, or a new technology to penetrate an economy. We are surprised over and over again at how much time things take. Jay Forrester used to tell us, when we were modeling a construction or processing delay, to ask everyone in the system how long they thought the delay was, make our best guess, and then multiply by three. (That correction factor also works perfectly, I have found, for estimating how long it will take to write a book!)”
― Donella H. Meadows, Thinking in Systems: A Primer

“The behavior of a system is its performance over time—its growth, stagnation, decline, oscillation, randomness, or evolution. If the news did a better job of putting events into historical context, we would have better behavior-level understanding, which is deeper than event-level understanding. When a systems thinker encounters a problem, the first thing he or she does is look for data, time graphs, the history of the system.”
― Donella H. Meadows, Thinking in Systems: A Primer

“Notice that the labels in Figure 9, like all the diagram labels in this book, are direction-free. The label says “stored energy in body” not “low energy level,” “coffee intake” not “more coffee.” That’s because feedback loops often can operate in two directions. In this case, the feedback loop can correct an oversupply as well as an undersupply. If you drink too much coffee and find yourself bouncing around with extra energy, you’ll lay off the caffeine for a while. High energy creates a discrepancy that says “too much,” which then causes you to reduce your coffee intake until your energy level settles down. The diagram is intended to show that the loop works to drive the stock of energy in either direction.”
― Donella H. Meadows, Thinking in Systems: A Primer

“Everything we think and 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. None of these is or ever will be the real world.”
― Donella H. Meadows, Thinking In Systems: A Primer

“Purposes are deducted from behavior, not from rhetoric or stated goals.”
― Donella H. Meadows, Thinking In Systems: A Primer

“...systems with similar feedback structures produce similar dynamic behaviors, even if the outward appearance of these systems is completely dissimilar”
― Donella H. Meadows, Thinking In Systems: A Primer

“A delay in a feedback process is critical relative to rates of change in the stocks that the feedback loop is trying to control. Delays that are too short cause overreaction, “chasing your tail,” oscillations amplified by the jumpiness of the response. Delays that are too long cause damped, sustained, or exploding oscillations, depending on how much too long.”
― Donella H. Meadows, Thinking in Systems: A Primer

“Delays in feedback loops are critical determinants of system behavior. They are common causes of oscillations. If you’re trying to adjust a stock (your store inventory) to meet your goal, but you receive only delayed information about what the state of the stock is, you will overshoot and undershoot your goal. The same is true if your information is timely, but your response isn’t. For example, it takes several years to build an electric power plant that will likely last thirty years. Those delays make it impossible to build exactly the right number of power plants to supply rapidly changing demand for electricity. Even with immense effort at forecasting, almost every electricity industry in the world experiences long oscillations between overcapacity and undercapacity. A system just can’t respond to short-term changes when it has long-term delays. That’s why a massive central-planning system, such as the Soviet Union or General Motors, necessarily functions poorly.”
― Donella H. Meadows, Thinking in Systems: A Primer

“You can often stabilize a system by increasing the capacity of a buffer.5 But if a buffer is too big, the system gets inflexible. It reacts too slowly. And big buffers of some sorts, such as water reservoirs or inventories, cost a lot to build or maintain.”
― Donella H. Meadows, Thinking in Systems: A Primer

“People care deeply about such variables as taxes and the minimum wage, and so fight fierce battles over them. But changing these variables rarely changes the behavior of the national economy system. If the system is chronically stagnant, parameter changes rarely kick-start it. If it’s wildly variable, they usually don’t stabilize it. If it’s growing out of control, they don’t slow it down.”
― Donella H. Meadows, Thinking in Systems: A Primer

“The amount of land we set aside for conservation each year. The minimum wage. How much we spend on AIDS research or Stealth bombers. The service charge the bank extracts from your account. All of these are parameters, adjustments to faucets. So, by the way, is firing people and getting new ones, including politicians. Putting different hands on the faucets may change the rate at which the faucets turn, but if they’re the same old faucets, plumbed into the same old system, turned according to the same old information and goals and rules, the system behavior isn’t going to change much.”
― Donella H. Meadows, Thinking in Systems: A Primer

“The GNP lumps together goods and bads. (If there are more car accidents and medical bills and repair bills, the GNP goes up.) It counts only marketed goods and services. (If all parents hired people to bring up their children, the GNP would go up.) It does not reflect distributional equity. (An expensive second home for a rich family makes the GNP go up more than an inexpensive basic home for a poor family.) It measures effort rather than achievement, gross production and consumption rather than efficiency. New light bulbs that give the same light with one-eighth the electricity and that last ten times as long make the GNP go down. 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.”
― Donella H. Meadows, Thinking in Systems: A Primer

“These examples confuse effort with result, one of the most common mistakes in designing systems around the wrong goal.”
― Donella H. Meadows, Thinking in Systems: A Primer

“Diversification is not guaranteed, however, especially if the monopolizing firm (or species) has the power to crush all offshoots, or buy them up, or deprive them of the resources they need to stay alive. Diversification doesn’t work as a strategy for the poor.”
― Donella H. Meadows, Thinking in Systems: A Primer

“How do you break out of the trap of success to the successful? Species and companies sometimes escape competitive exclusion by diversifying. A species can learn or evolve to exploit new resources. A company can create a new product or service that does not directly compete with existing ones. Markets tend toward monopoly and ecological niches toward monotony, but they also create offshoots of diversity, new markets, new species, which in the course of time may attract competitors, which then begin to move the system toward competitive exclusion again.”
― Donella H. Meadows, Thinking in Systems: A Primer

“The hopeful immigrant to Germany expects nothing but benefit from that country’s generous asylum laws, and has no reason to take into consideration the fact that too many immigrants will inevitably force Germany to toughen those laws. In fact, the knowledge that Germany is discussing that possibility is all the more reason to hurry to Germany!”
― Donella H. Meadows, Thinking in Systems: A Primer

“THE TRAP: POLICY RESISTANCE When various actors try to pull a system stock toward various goals, the result can be policy resistance. Any new policy, especially if it’s effective, just pulls the stock farther from the goals of other actors and produces additional resistance, with a result that no one likes, but that everyone expends considerable effort in maintaining. THE WAY OUT Let go. Bring in all the actors and use the energy formerly expended on resistance to seek out mutually satisfactory ways for all goals to be realized—or redefinitions of larger and more important goals that everyone can pull toward together.”
― Donella H. Meadows, Thinking in Systems: A Primer

“During the 1930s, Sweden’s birth rate dropped precipitously, and, like the governments of Romania and Hungary, the Swedish government worried about that. Unlike Romania and Hungary, the Swedish government assessed its goals and those of the population and decided that there was a basis of agreement, not on the size of the family, but on the quality of child care. Every child should be wanted and nurtured. No child should be in material need. Every child should have access to excellent education and health care. These were goals around which the government and the people could align themselves. The resulting policy looked strange during a time of low birth rate, because it included free contraceptives and abortion—because of the principle that every child should be wanted. The policy also included widespread sex education, easier divorce laws, free obstetrical care, support for families in need, and greatly increased investment in education and health care.4 Since then, the Swedish birth rate has gone up and down several times without causing panic in either direction, because the nation is focused on a far more important goal than the number of Swedes.”
― Donella H. Meadows, Thinking in Systems: A Primer