Book notes: “Thinking in Systems: A Primer” by Donella H. Meadows
(LM → my personal comments)
On Systems
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. 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.
LM: To the limit, everything is a system problem.
“I have yet to see any problem, however complicated, which, when looked at in the right way, did not become still more complicated.”
LM: Watch out for people that say things are easy! They may just don’t understand the problem enough.
Purposes are deduced from behavior, not from rhetoric or stated goals.
LM: Watch what is really happening, not what people are saying. First principles thinking.
Keeping sub-purposes and overall system purposes in harmony is an essential function of successful systems.
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.
A change in purpose changes a system profoundly, even if every element and interconnection remains the same.
To ask whether elements, interconnections, or purposes are most important in a system is to ask an unsystemic question. All are essential. All interact. All have their roles. But the least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.
LM: “The least obvious” because it’s not in the physical world → You can’t see the purpose of a university, you can see the professors, the students and their interactions.
Stocks, flows, feedback processes and oscillation
A stock is the memory of the history of changing flows within the system.
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.
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. 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.
Changes in stocks set the pace of the dynamics of systems. Industrialization cannot proceed faster than the rate at which factories and machines can be constructed and the rate at which human beings can be educated to run and maintain them. Forests can’t grow overnight. Once contaminants have accumulated in groundwater, they can be washed out only at the rate of groundwater turnover, which may take decades or even centuries.
That means system thinkers see the world as a collection of “feedback processes.”
Example: The more I practice piano, the more pleasure I get from the sound, and so the more I play the piano, which gives me more practice.
LM: Where can I add a positive feedback process?
A delay in a balancing feedback loop makes a system likely to oscillate.
Awesome explanation of the system we call economy
In the big picture, one store’s inventory problem may seem trivial and fixable. But imagine that the inventory is that of all the unsold automobiles in America. Orders for more or fewer cars affect production not only at assembly plants and parts factories, but also at steel mills, rubber and glass plants, textile producers, and energy producers. Everywhere in this system are perception delays, production delays, delivery delays, and construction delays. Now consider the link between car production and jobs — increased production increases the number of jobs allowing more people to buy cars. That’s a reinforcing loop, which also works in the opposite direction — less production, fewer jobs, fewer car sales, less production. Put in another reinforcing loop, as speculators buy and sell shares in the auto and auto-supply companies based on their recent performance, so that an upsurge in production produces an upsurge in stock price, and vice versa. That very large system, with interconnected industries responding to each other through delays, entraining each other in their oscillations, and being amplified by multipliers and speculators, is the primary cause of business cycles. Those cycles don’t come from presidents, although presidents can do much to ease or intensify the optimism of the upturns and the pain of the downturns. Economies are extremely complex systems; they are full of balancing feedback loops with delays, and they are inherently oscillatory.
Resilience of a system
Because resilience may not be obvious without a whole-system view, people often sacrifice resilience for stability, or for productivity, or for some other more immediately recognizable system property.
LM: Fragile vs Robust vs Antifragile. From this perspective, people often go for Robust instead of going for Antifragile
Just-in-time deliveries of products to retailers or parts to manufacturers have reduced inventory instabilities and brought down costs in many industries. The just-in-time model also has made the production system more vulnerable, however, to perturbations in fuel supply, traffic flow, computer breakdown, labor availability, and other possible glitches.
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.
Systems Hierarchies
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.
Paraphrased from Herbert Simon, Hierarchies are brilliant systems inventions, not only because they give a system stability and resilience, but also because they reduce the amount of information that any part of the system has to keep track of.
When a subsystem’s goals dominate at the expense of the total system’s goals, the resulting behavior is called suboptimization. Just as damaging as suboptimization, of course, is the problem of too much central control.
To 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.
LM: For products squads to work they need autonomy → fewer dependencies, higher autonomy → it boils down to drawing the responsibility boundary of each squad in order to minimize dependencies
On learning and modeling the world
The acquisition of knowledge always involves the revelation of ignorance — almost is the revelation of ignorance. Our knowledge of the world instructs us first of all that the world is greater than our knowledge of it.
1. 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.
2. 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.
3. 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.
You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities and delays. You are likely to mistreat, misdesign, or misread systems if you don’t respect their properties of resilience, self-organization, and hierarchy.
LM: when tackling a problem: where current boundaries are not working? Where am I limited by my bounded rationality (unknown unknown)?
Path to mastery to analyse a system
Like the tip of an iceberg rising above the water, events are the most visible aspect of a larger complex — but not always the most important. We are less likely to be surprised if we can see how events accumulate into dynamic patterns of behavior. The team is on a winning streak. The variance of the river is increasing, with higher floodwaters during rains and lower flows during droughts. The Dow has been trending up for two years. Discoveries of oil are becoming less frequent. The felling of forests is happening at an ever-increasing rate.
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.
These behavior-based models are more useful than event-based ones, but they still have fundamental problems. First, they typically overemphasize system flows and underemphasize stocks.
But without seeing how stocks affect their related flows through feedback processes, one cannot understand the dynamics of economic systems or the reasons for their behavior.
LM: degrees of mastery when analyzing systems: Event-based model → Behavior-based model (events in time) → Feedback processes based models (events in time emphasized by stocks, flow and feedback loops)
The greatest complexities arise exactly at boundaries. There are Czechs on the German side of the border and Germans on the Czech side of the border. Forest species extend beyond the edge of the forest into the field; field species penetrate partway into the forest. Disorderly, mixed-up borders are sources of diversity and creativity.
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. 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.
Ideally, we would have the mental flexibility to find the appropriate boundary for thinking about each new problem. We are rarely that flexible. We get attached to the boundaries our minds happen to be accustomed to. Think how many arguments have to do with boundaries — national boundaries, trade boundaries, ethnic boundaries, boundaries between public and private responsibility, and boundaries between the rich and the poor, polluters and pollutees, people alive now and people who will come in the future.
I realize with fright that my impatience for the re-establishment of democracy had something almost communist in it; or, more generally, something rationalist. 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.
Bounded rationality
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.
LM: Examples: Fishermen that overfish and destroy their own livelihood → They don’t know how many fish there are, much less how many fish will be caught by other fishermen that same day.
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.
LM: weight on the side of over-communication
Policy making
Policy resistance comes from the bounded rationalities of the actors in a system, each with his or her (or “its” in the case of an institution) own goals. Each actor monitors the state of the system with regard to some important variable — income or prices or housing or drugs or investment — and compares that state with his, her, or its goal. If there is a discrepancy, each actor does something to correct the situation. Usually the greater the discrepancy between the goal and the actual situation, the more emphatic the action will be.
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. If you calm down, those who are pulling against you will calm down too. This is what happened in 1933 when Prohibition ended in the United States; the alcohol- driven chaos also largely ended.
That calming down may provide the opportunity to look more closely at the feedbacks within the system, to understand the bounded rationality behind them, and to find a way to meet the goals of the participants in the system while moving the state of the system in a better direction.
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.
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. Harmonization of goals in a system is not always possible, but it’s an worth looking for. It can be found only by letting go of more narrow goals and considering the long term welfare of the entire system.
There always will be limits to growth. They can be self-imposed. If they aren’t, they will be system-imposed.
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.
Tragedy of the commons (selfish behavior > community behavior)
There are three ways to avoid the tragedy of the commons.
• Educate and exhort. Help people to see the consequences of unrestrained use of the commons. Appeal to their morality. Persuade them to be temperate. Threaten transgressors with social disapproval or eternal hellfire.
• Privatize the commons. Divide it up, so that each person reaps the consequences of his or her own actions. If some people lack the self-control to stay below the carrying capacity of their own private resource, those people will harm only themselves and not others.
• Regulate the commons. Garrett Hardin calls this option, bluntly, “mutual coercion, mutually agreed upon.” Regulation can take many forms, from outright bans on certain behaviors to quotas, permits, taxes, incentives. To be effective, regulation must be enforced by policing and penalties.
LM: tragedy of the commons → where selfish behavior is much more convenient and profitable than community behavior
On correcting a system
Another name for this system trap is “eroding goals.” It is also called the “boiled frog syndrome,” from the old story (I don’t know whether it is true) that a frog put suddenly in hot water will jump right out, but if it is put into cold water that is gradually heated up, the frog will stay there happily until it boils. “Seems to be getting a little warm in here. Well, but then it’s not so much warmer than it was a while ago.” Drift to low performance is a gradual process. If the system state plunged quickly, there would be an agitated corrective process. But if it drifts down slowly enough to erase the memory of (or belief in) how much better things used to be, everyone is lulled into lower and lower expectations, lower effort, lower performance. There are two antidotes to eroding goals. One is to keep standards absolute, regardless of performance. Another is to make goals sensitive to the best performances of the past, instead of the worst. If perceived performance has an upbeat bias instead of a downbeat one, if one takes the best results as a standard, and the worst results only as a temporary setback, then the same system structure can pull the system up to better and better performance.
THE TRAP: DRIFT TO LOW PERFORMANCE
Allowing performance standards to be influenced by past performance, especially if there is a negative bias in perceiving past performance, sets up a reinforcing feedback loop of eroding goals that sets a system drifting toward low performance.
THE WAY OUT
Keep performance standards absolute. Even better, let standards be enhanced by the best actual performances instead of being discouraged by the worst. Use the same structure to set up a drift toward high performance!
The best way out of this trap is to avoid getting in it. If caught in an escalating system, one can refuse to compete (unilaterally disarm), thereby interrupting the reinforcing loop. Or one can negotiate a new system with balancing loops to control the escalation.
LM: when a system is failing , do not begin with a heroic takeover, instead use the following 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?
Again, the best way out of this trap is to avoid getting in. Beware of symptom-relieving or signal-denying policies or practices that don’t really address the problem. Take the focus off short-term relief and put it on long term restructuring. 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. If you are the one with an unsupportable dependency, build your system’s own capabilities back up before removing the intervention. Do it right away. The longer you wait, the harder the withdrawal process will be.
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 and used with the lowest possible throughput, rather than the highest.
Electing Bill Clinton was definitely different from electing the elder George Bush, but not all that different, given that every president is plugged into the same political system.
On Fairness and the trap for logicians
When I became a landlord, I spent a lot of time and energy trying to figure out what would be a “fair” rent to charge. I tried to consider all the variables, including the relative incomes of my tenants, my own income and cash-flow needs, which expenses were for upkeep and which were capital expenses, the equity versus the interest portion of the mortgage payments, how much my labor on the house was worth, etc. I got absolutely nowhere. Finally I went to someone who specializes in giving money advice. She said: “You’re acting as though there is a fine line at which the rent is fair, and at any point above that point the tenant is being screwed and at any point below that you are being screwed. In fact, there is a large gray area in which both you and the tenant are getting a good, or at least a fair, deal. Stop worrying and get on with your life.”
The commonest kind of trouble is that it is nearly reasonable, but not quite. Life is not an illogicality; yet it is a trap for logicians.
Watching what really happens, instead of listening to peoples’ theories of what happens, can explode many careless causal hypotheses.
Remember, always, that everything you know, and everything everyone knows, is only a model. Get your model out there where it can be viewed. Invite others to challenge your assumptions and add their own. Instead of becoming a champion for one possible explanation or hypothesis or model, collect as many as possible. Consider all of them to be plausible until you find some evidence that causes you to rule one out. That way you will be emotionally able to see the evidence that rules out an assumption that may become entangled with your own identity. Getting models out into the light of day, making them as rigorous as possible, testing them against the evidence, and being willing to scuttle them if they are no longer supported is nothing more than practicing the scientific method — something that is done too seldom even in science, and is done hardly at all in social science or management or government or everyday life.
In fact, we don’t talk about what we see; we see only what we can talk about.
