This is a book summary of Thinking in Systems by Donella Meadows (the top book on Amazon for “System Theory” with 2,500+ reviews):
If you’re looking for an intro to Donella Meadows, here’s a lecture she gave in 1977:
- This book summary was originally published in Jan 2021. It was completely updated in Dec 2022 after receiving an email from the book publisher (Chelsea Green Publishing) on behalf of the copyright owner (The Academy for Change) who said the original summary contained too much quoted content. The updated summary is now 75% shorter than the original summary, and the vast majority of the remaining content has been paraphrased.
- All content is organized into my own themes (not the author’s chapters).
- Emphasis has been added in bold for readability/skimmability.
Book Summary Contents: Click a link to jump to a section below
An Intro to Systems Thinking: Thinking in Systems by Donella Meadows (Book Summary)
About the book Thinking in Systems
“This book is about that different way of seeing and thinking. It is intended for people who may be wary of the word ‘systems’ and the field of systems analysis, even though they may have been doing systems thinking all their lives.”
- “Donella Meadows (1941–2001) was a scientist trained in chemistry and biophysics (Ph.D., Harvard University), followed by a research fellowship at MIT. There she worked with Jay Forrester, the inventor of system dynamics.”
- “This book has been distilled out of the wisdom of thirty years of systems modeling and teaching carried out by dozens of creative people, most of them originally based at or influenced by the MIT System Dynamics group.”
- “Today, it is widely accepted that systems thinking is a critical tool in addressing the many environmental, political, social, and economic challenges we face around the world. Systems, big or small, can behave in similar ways, and understanding those ways is perhaps our best hope for making lasting change on many levels.” (from the editor, Diana Wright)
What is Systems Thinking?
“We are complex systems—our own bodies are magnificent examples of integrated, interconnected, self-maintaining complexity. Every person we encounter, every organization, every animal, garden, tree, and forest is a complex system.”
Understanding why problems exist and persist:
- Hunger, poverty, environmental degradation, economic instability, unemployment, chronic disease, drug addiction, and war 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.
- They will yield only as we reclaim our intuition, stop casting blame, see the system as the source of its own problems, and find the courage and wisdom to restructure it.
- transcends disciplines and cultures.
- overarches history.
- gives us the freedom to identify root causes of problems.
- will help us manage, adapt, and see the wide range of choices we have before us.
- goes back and forth constantly between structure (diagrams of stocks, flows, and feedback) and behavior (time graphs).
- see the world as a collection of feedback processes.
- see the world as a collection of stocks along with the mechanisms for regulating the levels in the stocks by manipulating flows.
- looks for data, time graphs, and the history of the system.
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.”
- is an interconnected set of elements that is coherently organized in a way that achieves something.
- is more than the sum of its parts and can be embedded in systems (which are embedded in yet other systems).
- may exhibit adaptive, dynamic, goal-seeking, self-organizing, self-preserving, and self-repairing behavior.
- can change, adapt, respond to events, seek goals, mend injuries, and attend to their own survival in lifelike ways (although they may contain or consist of nonliving things).
- is resilient, and many of them are evolutionary.
A system consists of:
- Parts we are most likely to notice. Often least important in defining the unique characteristics of the system (unless changing an element also results in changing interconnections/relationships or function/purpose).
- Many interconnections operate through the flow of information. If the interconnections/relationships change, system behavior may be greatly altered.
3. Function (for a nonhuman system) / Purpose (for a human system):
- The least obvious part of the system (the best way to deduce the system’s function/purpose is to watch for a while to see how the system behaves). Often the most crucial determinant of the system’s behavior (a change in function/purpose changes a system profoundly even if every element and interconnection remains the same).
Keep in mind:
A system’s stocks, flows, & feedbacks:
- the foundation of any system.
- a store, a quantity, an accumulation of material or information that has built up over time.
- the elements of the system that you can see, feel, count, or measure at any given time (does not have to be physical).
- the present memory of the history of changing flows within the system.
- usually changes slowly; responds to change, even sudden change, only by gradual filling or emptying (a stock takes time to change, because flows take time to flow).
- changes stocks over time.
- goes up and down, on and off, in all sorts of combinations (in response to stocks, not to other flows).
- can’t react instantly to another flow (it can react only to a change in a stock, and only after a slight delay to register the incoming information).
Keep in mind:
- the interconnections, the information part of the system.
- opens up the idea that a system can cause its own behavior.
- closed chain of causal connections from a stock, through a set of decisions / rules / physical laws / actions that are dependent on the level of the stock, and back again through a flow to change the stock.
- can only affect future behavior (it can’t deliver a signal fast enough to correct behavior that drove the current feedback).
Types of feedback loops:
- Balancing feedback loops (e.g. a thermostat): goal-seeking or stability-seeking, equilibrating, tries to keep a stock at a given value or within a range of values, opposes whatever direction of change is imposed on the system, both sources of stability and sources of resistance to change.
- Reinforcing feedback loops (e.g. living renewable resources like fish, trees, grass): vicious or virtuous circles, amplifying, self-enhancing, self-multiplying, snowballing, enhances whatever direction of change is imposed on it.
Additional system concepts to know:
When one loop dominates another, it has a stronger impact on behavior (the feedback loops that dominate the system will determine the 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.
Every stock is a delay. Most flows have delays. Delays are pervasive and ubiquitous in systems, and they are strong determinants of behavior.
- 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.
Many relationships in systems are nonlinear. A nonlinear relationship is one in which the cause does not produce a proportional effect.
- Nonlinearities are important not only because they confound our expectations about the relationship between action and response. They are even more important because they change the relative strengths of feedback loops. They can flip a system from one mode of behavior to another.
At any given time, the input that is most important to a system is the one that is most limiting. 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.
- Whenever one factor ceases to be limiting, growth occurs, and the growth itself changes the relative scarcity of factors until another becomes 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. Any physical entity with multiple inputs and outputs is surrounded by layers of limits. As the system develops, it interacts with and affects its own limits. The growing entity and its limited environment together form a coevolving dynamic system.
There are no separate systems. The world is a continuum. Everything is connected to everything else, and not neatly. There is no clearly determinable boundary between anything.
- There are only boundaries of word, thought, perception, and social agreement—artificial, mental-model boundaries. There is no single, legitimate boundary to draw around a system. We have to invent boundaries for clarity and sanity; and boundaries can produce problems when we forget that we’ve artificially created them. Remember that boundaries are of our own making, and that they can and should be reconsidered for each new discussion, problem, or purpose.
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.
- 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. It’s amazing how quickly and easily behavior changes can come, with even slight enlargement of bounded rationality, by providing better, more complete, timelier information.
Properties & Truths of Systems
“Why do systems work so well? Consider the properties of highly functional systems—machines or human communities or ecosystems—which are familiar to you. Chances are good that you may have observed one of three characteristics: resilience, self-organization, or hierarchy.”
3 properties of systems:
Measure of a system’s ability to survive and persist within a variable environment—the ability to recover, restore, and repair themselves.
- arises from a rich structure of many feedback loops that can work in different ways to restore a system even after a large perturbation.
- is not the same thing as being static or constant over time (resilient systems can be very dynamic).
The capacity of a system to make its own structure more complex—systems changing themselves by creating whole new structures and behaviors.
- ability to learn, diversify, complexify, evolve.
- produces heterogeneity and unpredictability.
- requires freedom and experimentation, and a certain amount of disorder.
An arrangement of systems and subsystems—the world is organized in subsystems aggregated into larger subsystems, aggregated into still larger subsystems.
- if subsystems can largely take care of themselves, regulate themselves, maintain themselves, and yet serve the needs of the larger system, while the larger system coordinates and enhances the functioning of the subsystems, a stable, resilient, and efficient structure results.
- not only do they give a system stability and resilience, but they also reduce the amount of information that any part of the system has to keep track of.
- evolve from the bottom or lowest level up—the purpose of the upper layers of the hierarchy is to serve the purposes of the lower layers (the original purpose of a hierarchy is always to help its originating subsystems do their jobs better; when a subsystem’s goals dominate at the expense of the total system’s goals, the resulting behavior is called sub-optimization).
- 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).
3 truths about systems:
1. Everything we think we know about the world is a model:
Every word and every language is a model. So are the ways I picture the world in my head—mental models. None of these is or ever will be the real world.
2. Our models usually have a strong congruence with the world:
Especially complex and sophisticated are the mental models we develop from direct, intimate experience of nature, people, and organizations immediately around us.
3. Our models fall far short of representing the world fully:
That is why we make mistakes and why we are regularly surprised. We often draw illogical conclusions from accurate assumptions, or logical conclusions from inaccurate assumptions.
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