I’ve mentioned many times how I considered the approach to the environment, economy, population, etc by the book Limits to Growth the most comprehensive and valuable, though I hadn’t read much critical of it that didn’t seem politically motivated or starting from disagreement. I’ve wanted to find alternative perspectives.
I found more resources and perspective, especially in two papers in American Scientist written by Brian Hayes. Both articles followed publishing later editions of Limits to Growth, one in 1993, the other in 2012.
The articles speak for themselves, and better than I could speak for them. They’re somewhat scientific, but I think most people could understand them.
The 1993 one is “Balanced on a Pencil Point.” Hayes begins writing about his history with the book, then the model, World3, that drove the book, then about experimenting with it. He finds the model almost impossible to fine tune to avoid catastrophe—hence balanced on a pencil point—speculating that while we may happen to live during a specifically precarious time, one could conclude that the fineness necessary for tuning may be a product of the model, not the earth. Hard to tell with such a simple model and without more data.
The 2012 one is “Computation and the Human Predicament.” In it Hayes recreates the model from scratch and plays with it more. He shares more about its history and the history of the people who created it. He talks about the book in the context of other late 60s early 70s books and criticism of the book and World3. He talks about the models I learned in college that drove my first interest in dynamical systems—the Lotka-Volterra model, which I was pleased to see again after several decades.
His conclusion at the end of the 2012 paper tempers my confidence in World3’s results:
After three immersions in The Limits to Growth, at intervals of 20 years, I feel entitled to state some opinions.
First, the book’s message is worth listening to. There are limits, and exponential growth is unsustainable. A society that measures well-being by the first derivative of GDP is asking for trouble. But I am more optimistic than the Limits authors are about our ability to deal with these issues before the world turns into the set of a Mad Max movie.
As for the mathematical model behind the book, I believe it is more a polemical tool than a scientific instrument. Forrester and the Limits group have frequently said that the graphs drawn by their computer programs should not be taken as predictions of the future, but only as indicating “dynamic tendencies” or “behavior modes.” But despite these disclaimers, Limits is full of blunt statements about the future: “If the present growth trends continue unchanged,… the limits to growth on this planet will be reached sometime in the next one hundred years.” And whether the models are supposed to be predictive or not, they are offered as an explicit guide to public policy. For example, in testimony before a congressional committee in the 1970s Forrester recommended curtailing investment in industrialization and food production as a way of slowing population growth.
It’s possible that Forrester was offering wise advice, and someday we’ll regret not taking it. But when a mathematical or scientific argument is brought forward to justify taking such a painful and troubling action, standards of rigor will surely be set very high.
In an unpublished paper on the testing of system dynamics models, Forrester and a student wrote: “The ultimate objective of validation is transferred confidence in a model’s usefulness as a basis for policy change.” That has yet to happen for World3.
I’m still swayed by Graham Turner’s comparisons with data. I still see the main way to systemic change as through changing a system’s beliefs and goals—in our culture, the relevant ones being growth at any cost, that technology will save us, that markets will save us, and that “you can’t tell me what to do!”
In any case, Hayes’ analysis seems relevant. It suggests the World3 model being less authoritative as the Limits to Growth authors suggest, though still relevant in big ways.
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