Donella Meadows is famous for her 1972 book, The Limits to Growth, which uses systems dynamics, as developed by Jay Forrester, to make Malthusian predictions about overpopulation.
(It turns out you don’t need sophisticated computer models to conclude that exponential population growth and linear resource growth lead to dystopia — but such scientism sells ideas.)
Her understanding of systems dynamics — which uses feedback loops and stocks and flows in its models — surpasses her understanding of microeconomics — which emphasizes the equilibria brought about by the feedback loops in a more-or-less perfect market.
In Places to Intervene in a System, she explains how unintuitive systems thinking can be — and how the seemingly important numbers are nowhere near as important as the underlying structure of the system:
Numbers (“parameters” in systems jargon) determine how much of a discrepancy turns which faucet how fast. Maybe the faucet turns hard, so it takes a while to get the water flowing. Maybe the drain is blocked and can allow only a small flow, no matter how open it is. Maybe the faucet can deliver with the force of a fire hose. These considerations are a matter of numbers, some of which are physically locked in, but most of which are popular intervention points.Consider the national debt. It’s a negative bathtub, a money hole. The rate at which it sinks is the annual deficit. Tax income makes it rise, government expenditures make it fall. Congress and the president argue endlessly about the many parameters that open and close tax faucets and spending drains. Since those faucets and drains are connected to the voters, these are politically charged parameters. But, despite all the fireworks, and no matter which party is in charge, the money hole goes on sinking, just at different rates.
The amount of land we set aside for conservation. The minimum wage. How much we spend on AIDS research or Stealth bombers. The service charge the bank extracts from your account. All these are numbers, adjustments to faucets. So, by the way, is firing people and getting new ones. Putting different hands on the faucets may change the rate at which they turn, but if they’re the same old faucets, plumbed into the same system, turned according to the same information and rules and goals, the system isn’t going to change much. Bill Clinton is different from George Bush, but not all that different.
Numbers are last on my list of leverage points. Diddling with details, arranging the deck chairs on the Titanic. Probably ninety-five percent of our attention goes to numbers, but there’s not a lot of power in them.
Not that parameters aren’t important — they can be, especially in the short term and to the individual who’s standing directly in the flow. But they rarely change behavior. If the system is chronically stagnant, parameter changes rarely kick-start it. If it’s wildly variable, they don’t usually stabilize it. If it’s growing out of control, they don’t brake it.
Whatever cap we put on campaign contributions, it doesn’t clean up politics. The Feds fiddling with the interest rate haven’t made business cycles go away. (We always forget that during upturns, and are shocked, shocked by the downturns.) Spending more on police doesn’t make crime go away.
However, there are critical exceptions. Numbers become leverage points when they go into ranges that kick off one of the items higher on this list. Interest rates or birth rates control the gains around positive feedback loops. System goals are parameters that can make big differences. Sometimes a system gets onto a chaotic edge, where the tiniest change in a number can drive it from order to what appears to be wild disorder.
Probably the most common kind of critical number is the length of delay in a feedback loop. Remember that bathtub on the fourth floor I mentioned, with the water heater in the basement? I actually experienced one of those once, in an old hotel in London. It wasn’t even a bathtub with buffering capacity; it was a shower. The water temperature took at least a minute to respond to my faucet twists. Guess what my shower was like. Right, oscillations from hot to cold and back to hot, punctuated with expletives. Delays in negative feedback loops cause oscillations. If you’re trying to adjust a system state to your goal, but you only receive delayed information about what the system state is, you will overshoot and undershoot.
Same if your information is timely, but your response isn’t. For example, it takes several years to build an electric power plant, and then that plant lasts, say, thirty years. Those delays make it impossible to build exactly the right number of plants to supply a rapidly changing demand. 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.
A delay in a feedback process is critical relative to rates of change (growth, fluctuation, decay) in the system state that the feedback loop is trying to control. Delays that are too short cause overreaction, 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. At the extreme they cause chaos. Delays in a system with a threshold, a danger point, a range past which irreversible damage can occur, cause overshoot and collapse.
Delay length would be a high leverage point, except for the fact that delays are not often easily changeable. Things take as long as they take. You can’t do a lot about the construction time of a major piece of capital, or the maturation time of a child, or the growth rate of a forest. It’s usually easier to slow down the change rate (positive feedback loops, higher on this list), so feedback delays won’t cause so much trouble. Critical numbers are not nearly as common as people seem to think they are. Most systems have evolved or are designed to stay out of sensitive parameter ranges. Mostly, the numbers are not worth the sweat put into them.
(Hat tip to Joseph Fouché.)