Thinking In Systems
16 Nov 2024| Book | Thinking In Systems |
| Author | Donella H. Meadows and Diana Wright |
| Published | December 3, 2008 |
Essense of systems thinking
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. As our world continues to change rapidly and become more complex, systems thinking will help us manage, adapt, and see the wide range of choices we have before us. It is a way of thinking that gives us the freedom to identify root causes of problems and see new opportunities.
System = elements connected together to serve a purpose
So, what is a system? A system is a set of things—people, cells, molecules, or whatever—interconnected in such a way that they produce their own pattern of behavior over time. The system may be buffeted, constricted, triggered, or driven by outside forces. But the system’s response to these forces is characteristic of itself, and that response is seldom simple in the real world.
The behavior of a system cannot be known just by knowing the elements of which the system is made.
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.
A system is more than the sum of its parts. It may exhibit adaptive, dynamic, goal-seeking, self-preserving, and sometimes evolutionary behavior.
Connections are information flows
Many of the interconnections in systems operate through the flow of information. Information holds systems together and plays a great role in determining how they operate.
Systems and purpose
Purposes are deduced from behavior, not from rhetoric or stated goals.
Systems can be nested within systems. Therefore, there can be purposes within purposes. The purpose of a university is to discover and preserve knowledge and pass it on to new generations. Within the university, the purpose of a student may be to get good grades, the purpose of a professor may be to get tenure, the purpose of an administrator may be to balance the budget. Any of those sub-purposes could come into conflict with the overall purpose—the student could cheat, the professor could ignore the students in order to publish papers, the administrator could balance the budget by firing professors. Keeping sub-purposes and overall system purposes in harmony is an essential function of successful systems. I’ll get back to this point later when we come to hierarchies.
The least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.
Changing just one leader at the top—from a Brezhnev to a Gorbachev, or from a Carter to a Reagan—may or may not turn an entire nation in a new direction, though its land, factories, and hundreds of millions of people remain exactly the same. A leader can make that land and those factories and people play a different game with new rules, or can direct the play toward a new purpose. And conversely, because land, factories, and people are long-lived, slowly changing, physical elements of the system, there is a limit to the rate at which any leader can turn the direction of a nation.
Systems and stocks
A stock is the memory of the history of changing flows within the system.
A stock takes time to change, because flows take time to flow. That’s a vital point, a key to understanding why systems behave as they do. Stocks usually change slowly. They can act as delays, lags, buffers, ballast, and sources of momentum in a system. Stocks, especially large ones, respond to change, even sudden change, only by gradual filling or emptying.
Systems thinkers use graphs of system behavior to understand trends over time, rather than focusing attention on individual events. We also use behavior-over-time graphs to learn whether the system is approaching a goal or a limit, and if so, how quickly. The variable on the graph may be a stock or a flow. The pattern—the shape of the variable line—is important, as are the points at which that line changes shape or direction. The precise numbers on the axes are often less important. The horizontal axis of time allows you to ask questions about what came before, and what might happen next. It can help you focus on the time horizon appropriate to the question or problem you are investigating.
Feedback loops
People monitor stocks constantly and make decisions and take actions designed to raise or lower stocks or to keep them within acceptable ranges. Those decisions add up to the ebbs and flows, successes and problems, of all sorts of systems. Systems thinkers see the world as a collection of stocks along with the mechanisms for regulating the levels in the stocks by manipulating flows. That means system thinkers see the world as a collection of “feedback processes.”
Of course, in real systems feedback loops rarely come singly. They are linked together, often in fantastically complex patterns. A single stock is likely to have several reinforcing and balancing loops of differing strengths pulling it in several directions. A single flow may be adjusted by the contents of three or five or twenty stocks. It may fill one stock while it drains another and feeds into decisions that alter yet another. The many feedback loops in a system tug against each other, trying to make stocks grow, die off, or come into balance with each other. As a result, complex systems do much more than stay steady or explode exponentially or approach goals smoothly—as we shall see.
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. A person in the system who makes a decision based on the feedback can’t change the behavior of the system that drove the current feedback; the decisions he or she makes will affect only future behavior.
Complex behaviors of systems often arise as the relative strengths of feedback loops shift, causing first one loop and then another to dominate behavior.
The central question of economic development is how to keep the reinforcing loop of capital accumulation from growing more slowly than the reinforcing loop of population growth—so that people are getting richer instead of poorer.
One of the central insights of systems theory, as central as the observation that systems largely cause their own behavior, is that systems with similar feedback structures produce similar dynamic behaviors, even if the outward appearance of these systems is completely dissimilar. A population is nothing like an industrial economy, except that both can reproduce themselves out of themselves and thus grow exponentially. And both age and die. A coffee cup cooling is like a warmed room cooling, and like a radioactive substance decaying, and like a population or industrial economy aging and dying. Each declines as the result of a balancing feedback loop.
Delays within a system
A delay in a balancing feedback loop makes a system likely to oscillate.
Changing the delays in a system can make it much easier or much harder to manage. You can see why system thinkers are somewhat fanatic on the subject of delays. 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. We can’t begin to understand the dynamic behavior of systems unless we know where and how long the delays are. And we are aware that some delays can be powerful policy levers. Lengthening or shortening them can produce major changes in the behavior of systems.
Biological systems are incredibly resilient, adaptable and “anti-fragile”
The human body is an astonishing example of a resilient system. It can fend off thousands of different kinds of invaders, it can tolerate wide ranges of temperature and wide variations in food supply, it can reallocate blood supply, repair rips, gear up or slow down metabolism, and compensate to some extent for missing or defective parts. Add to it a self-organizing intelligence that can learn, socialize, design technologies, and even transplant body parts, and you have a formidably resilient system—although not infinitely so, because, so far at least, no human body-plus-intelligence has been resilient enough to keep itself or any other body from eventually dying.
The most marvelous characteristic of some complex systems is their ability to learn, diversify, complexify, evolve. It is the ability of a single fertilized ovum to generate, out of itself, the incredible complexity of a mature frog, or chicken, or person. It is the ability of nature to have diversified millions of fantastic species out of a puddle of organic chemicals. It is the ability of a society to take the ideas of burning coal, making steam, pumping water, and specializing labor, and develop them eventually into an automobile assembly plant, a city of skyscrapers, a worldwide network of communications.
Science as a system
Science knows now that self-organizing systems can arise from simple rules. Science, itself a self-organizing system, likes to think that all the complexity of the world must arise, ultimately, from simple rules. Whether that actually happens is something that science does not yet know.
Hierarchical systems
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.5
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.
Models are incredibly useful, even though they’re imperfect
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.
We know a tremendous amount about how the world works, but not nearly enough. Our knowledge is amazing; our ignorance even more so. We can improve our understanding, but we can’t make it perfect.
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.
Limiting factors or bottlenecks
It was with regard to grain that 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. Bread will not rise without yeast, no matter how much flour it has. Children will not thrive without protein, no matter how many carbohydrates they eat. Companies can’t keep going without energy, no matter how many customers they have—or without customers, no matter how much energy they have. This concept of a limiting factor is simple and widely misunderstood.
At any given time, the input that is most important to a system is the one that is most limiting.
Economics evolved in a time when labor and capital were the most common limiting factors to production. Therefore, most economic production functions keep track only of these two factors (and sometimes technology). As the economy grows relative to the ecosystem, however, and the limiting factors shift to clean water, clean air, dump space, and acceptable forms of energy and raw materials, the traditional focus on only capital and labor becomes increasingly unhelpful.
Limits of growth within a finite environment
For any physical entity in a finite environment, perpetual growth is impossible. Ultimately, the choice is not to grow forever but to decide what limits to live within. If a company produces a perfect product or service at an affordable price, it will be swamped with orders until it grows to the point at which some limit decreases the perfection of the product or raises its price. 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. No physical entity can grow forever. If company managers, city governments, the human population do not choose and enforce their own limits to keep growth within the capacity of the supporting environment, then the environment will choose and enforce limits.
To him that hath shall be given. The more the winner wins, the more he, she, or it can win in the future. If the winning takes place in a limited environment, such that everything the winner wins is extracted from the losers, the losers are gradually bankrupted, or forced out, or starved. Success to the successful is a well-known concept in the field of ecology, where it is called “the competitive exclusion principle.” This principle says that two different species cannot live in exactly the same ecological niche, competing for exactly the same resources. Because the two species are different, one will necessarily reproduce faster, or be able to use the resource more efficiently than the other. It will win a larger share of the resource, which will give it the ability to multiply more and keep winning. It will not only dominate the niche, it will drive the losing competitor to extinction. That will happen not by direct confrontation usually, but by appropriating all the resource, leaving none for the weaker competitor.
Escaping competition (See “Blue Ocean Strategy”)
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.
Importance of information flows
Missing information flows is one of the most common causes of system malfunction. Adding or restoring information can be a powerful intervention, usually much easier and cheaper than rebuilding physical infrastructure.
My impression is that we have seen, for perhaps a hundred and fifty years, a gradual increase in language that is either meaningless or destructive of meaning. And I believe that this increasing unreliability of language parallels the increasing disintegration, over the same period, of persons and communities.
Embracing errors is the only way to learn
Error-embracing is the condition for learning. It means seeking and using—and sharing—information about what went wrong with what you expected or hoped would go right. Both error embracing and living with high levels of uncertainty emphasize our personal as well as societal vulnerability. Typically we hide our vulnerabilities from ourselves as well as from others. But … to be the kind of person who truly accepts his responsibility … requires knowledge of and access to self far beyond that possessed by most people in this society.