Balancing loop: as X increases, it’s harder and harder for X to keep increasing
Reinforcing loop: as X increases, it’s easier and easier for X to keep increasing
i. Systems zoo: a few examples of common types of systems
One stock with two competing balancing loops: a thermostat
Stock: the amount of heat/temperature in a room
Balancing loop #1: thermostat/heater which tries to keep the room at a temp higher than what it would be
Balancing loop #2: heat will keep leaving the room as long as it’s colder outside
One stock with one reinforcing loop and one balancing loop #1: population
Stock: the population
Reinforcing loop: the birth rate
Balancing loop: the death rate
One stock with one reinforcing loop and one balancing loop #2: industrial economy
Stock: physical capital
Reinforcing loop: products (and resulting profits which can be reinvested)
Balancing loop: depreciation of capital (becoming obsolete and wearing out)
One stock with two competing balancing loops, with delays: inventory of a car dealership
Stock: cars on the lot
Balancing loop #1: car sales decrease the stock
Balancing loop #1: ordering cars from the factory restores the stock
Perception delay: the dealer intentionally waits to get a more accurately gauge sales trends
Response delay: the dealer intentionally splits any adjustment across several orders, instead of ordering as many as projected all at once (again, to account for random dips/spikes)
Delivery delay: delay between putting in an order and receiving the new stock
The existence of delays in a balancing feedback loop can cause oscillations.
“High leverage, wrong direction” … This perverse kind of result can be seen all the time–someone trying to fix a system is attracted intuitively to a policy lever that in fact does have a strong effect on the system. And then the well-intentioned fixer pulls the lever in the wrong direction!
The correct way to make an adjustment can be in a counterintuitive direction. For example, you can think the way to correct for oscillations is to react faster (shorten delays), when in fact the answer might be to react more slowly (lengthen delays, e.g. lengthen the perception delay by waiting longer for trends to smooth out).
A renewable stock constrained by a non-renewable stock: oil economy
Renewable stock: capital
Constraining non-renewable stock: oil in one oil field
Balancing loop #1: depreciation of physical capital
Balancing loop #2: more capital means you can extract oil faster (a reinforcing loop).. but the more oil you extract, the more costly it becomes to extract the remaining oil
Whenever we see a growing entity, whether it be a population, a corporation, a bank account, a rumor, an epidemic, or sales of a new product, we look for the reinforcing loops that are driving it and for the balancing loops that ultimately will constrain it.
According to the dynamics of depletion, the larger the stock of initial resources, the more new discoveries, the longer the growth loops elude the control loops, and the higher the capital stock and its extraction rate grow, and the earlier, faster, and farther will be the economic fall on the back side of the production peak.
A renewable stock constrained by a renewable stock: fishing economy
Renewable stock: capital
Constraining renewable stock: fish
Scenario #1: the industry stabilizes at an equilibrium which can continue indefinitely
Scenario #2: better fishing technology than Scenario #1 that allows you to fish slightly too much, can result in oscillations
Scenario #3: even better fishing technology than Scenario #2 that allows you to fish way too much, can result in a complete wipeout of the fish–as if it were nonrenewable
ii. Why systems work so well
Resilience: feedback loops to restore a system to its desired state
Meta-resilience: a set of feedback loops that can restore or rebuild feedback loops
Meta-meta-resilience (aka self-organization): feedback loops that can learn, create, design, and evolve the feedback loops that restore feedback loops
I don’t think meta-meta-resilience has ever really occurred to me in my life and work, but it seems like that would be the holy grail, and something I should think about.
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.
One of my personal favorite examples of this is that fear of germs/bacteria can result in a weak immune system. (Similar ideas are mentioned in Antifragile.)
What you need to think about may change over time, as self-organizing systems evolve new degrees of hierarchy and integration. The energy systems of nations were once almost completely decomposable one from another. That is no longer true. People whose thinking has not evolved as fast as the energy economy has may be shocked to discover how dependent they have become on resources and decisions halfway around the world.
This reminds me of a Shane Parrish podcast where the interviewee (who I can’t for the life of me remember right now, but maybe it’ll come back someday) points out that we’re good at perceiving change on small time scales, like from one year to another, and ok at perceiving change at extremely long time scales, like over centuries, but bad at perceiving change at that time scale in the middle, where things drift slightly from year to year, but end up vastly different over the course of like 50 years.
Therefore we assume a lot of things are the same as they were at the time when we first learned them (RIP Pluto). It’d be a good exercise to every once and while stop and think, “What was true in the world when I was a teenager that isn’t true anymore? What was true when I was in my 20s that isn’t true anymore?”
iii. Why systems surprise us
We focus too much on specific events, not enough on overall behaviors or trends.
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.
This has always frustrated me about the news, and was even more painful when I was a kid, and didn’t know the context of any news (e.g., what is Hamas and what does that have to do with anything?), plus it was harder to find resources to fill those gaps. It still annoys me today, but I’m better at getting my questions answered, and I’ve resigned myself to the fact that if I actually want to understand it well, I should just read at least a full book.
But also, as a kid, I was frustrated because I assumed that all grownups just knew all the context, and I was the only one left out. But now I realize that’s not true. At any given time, the vast majority of grownups consuming a news item are very much in the dark as to the backstory and trends over a long period of time. If they weren’t in the dark, they wouldn’t be reading or watching the daily news.
Secondly, and more seriously, in trying to find statistical links that relate flows to each other, econometricians are searching for something that does not exist. There’s no reason to expect any flow to bear a stable relationship to any other flow. Flows go up and down, on and off, in all sorts of combinations, in response to stocks, not to other flows.
This is referring to the issues with economic reports.
We’re not great at understanding nonlinear relationships.
We use boundaries to create a system (or mental model), but then later we forget that the boundaries exist and are self-imposed.
When we think in terms of systems, we see that a fundamental misconception is embedded in the popular term “side-effects” … This phrase means roughly “effects which I hadn’t foreseen or don’t want to think about” … Side-effects no more deserve the adjective “side” than does the “principal” effect. It is hard to think in terms of systems, and we eagerly warp our language to protect ourselves from the necessity of doing so.Garrett Hardin, ecologist
We forget that limiting factors are layered. Growth often changes which factor is the limiting factor.
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.
We have a hard time thinking intuitively about systems that have delays.
Bounded rationality: individuals can each make decisions that are rational for them locally, yet which in the aggregate lead to a result that isn’t good for anyone.
iv. System traps and opportunities
When systems have problems that are not just surprising, but perverse–set up in a way that makes the desired state really hard to achieve.
Policy resistance: When a system is composed of subsystems whose goals are at odds with each other. If the system is in a bad state and one actor tries to fix it by pulling harder in their own direction, all other actors just have to exert more effort to get it back into the previous state, which nobody liked anyway. Example: war on drugs. Example: Nicolae Ceausescu’s government trying to increase Romania’s birthrate by outlawing abortion.
The tragedy of the commons: Example: overgrazing, overfishing.
Drift to low performance: A reinforcing loop where low performance leads you to psychologically lower your standards/expectations, so performance drifts lower and lower.
Escalation: Examples: price wars, arms races.
Success to the successful: Example: America.
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.
Shifting the burden to the intervenor–addiction: Addressing the symptom instead of the root cause, such that you become dependent on the thing that addresses the symptom.
Rule beating: Rules that are poorly crafted such that they can incentivize people to do stuff that abides “by the letter, but not the spirit, of the law.”
Seeking the wrong goal: When the measurable results you’re looking for and incentivizing have nothing to do with what you actually want to see. Example: conflating “quality education” with “performance on standardized tests.”
v. Leverage points–places to intervene in a system
From least effective to most effective.
12: Numbers: constants and parameters such as subsidies, taxes, standards
Numbers, the sizes of flows, are dead last on my list of powerful interventions. Diddling with the details, arranging the deck chairs on the Titanic. Probably 90–no 95, no 99 percent–of our attention goes to parameters, but there’s not a lot of leverage in them.
11: Buffers: the sizes of stabilizing stocks relative to their flows
10: Stock-and-flow structures: physical systems and their nodes of intersection
9: Delays: the lengths of time relative to the rates of system changes
8: Balancing feedback loops: the strength of the feedbacks relative to the impacts they are trying to correct
7: Reinforcing feedback loops: the strength of the gain of driving loops
6: Information flows: the structure of who does and does not have access to information
5: Rules: incentives, punishments, and constraints
4: Self-organization: the power to add, change, or evolve system structure
3: Goals: the purpose or function of the system
2: Paradigms: the mindset out of which the system–its goals, structure, rules, delays, parameters–arises
1: Transcending paradigms
To realize that no paradigm is true = enlightenment.
If no paradigm is right, you can choose whatever one will help to achieve your purpose. If you have no idea where to get a purpose, you can listen to the universe.
It is in this space of mastery over paradigms that people throw off addictions, live in constant joy, bring down empires, get locked up or burned at the stake or crucified or shot, and have impacts that last for millennia.
In the end, it seems that mastery has less to do with pushing leverage points than it does with strategically, profoundly, madly, letting go and dancing with the system.
Response to the excuse that you can let low quality stuff pass without being addressed, just because it’s hard to quantify and measure:
Human beings have been endowed not only with the ability to count, but also with the ability to assess quality. Be a quality detector. Be a walking, noisy Geiger counter that registers the presence or absence of quality.