We throw a lot of words around without giving much thought to meaning or physical manifestation. We talk about systems, iteration, interdependence, feedback. We slap these labels on all manner of things, but we don’t really consider the real-world implications. For example, we talk about economic systems, the interrelated networks of processes and infrastructures used to meet human needs — often with the side-effect of generating wealth — yet then pretend that most of the inconvenient elements — things that are difficult to quantify, things that are weighted with ethical concerns, and most particularly things that lead to less wealth generation — are insignificant to the system. We call them externalities when we just don’t want to think about them at all.
But physically and mathematically, this is nonsense. A system is all its parts and the processes of relationship between all those parts. If you discard a part, change reverberates throughout the whole system. Quite often the system breaks down. So when we set aside externalities in our system modeling, we render the model meaningless.
Now, because we rarely are able to comprehend all the variables that go into any given system and because we want to understand our world, we use these short-hand models that help us see certain parts of the system. This is fine as long as we remember that we’re getting a limited view of a small bit of the process and all its components. What we learn from this focus we then fold back into our ideas about the whole to see if what we’ve learned actually fits in that whole. If it doesn’t fit, then our ideas are wrong.
This method of studying a sampling from a system is easier to do in physical systems, even when those systems are incomprehensible at human scales of time and space. Say, I want to understand the cross-beds in sandstone rocks. I will come up with a hypothesis about how cross-beds are created and then create experiments that try to replicate cross-beds based on my hypothesis. If, in a lab setting, I am able to make depositional layers that look like cross-beds, then my next step is to go look at natural depositional settings — places where the sandy sediment that will eventually be compressed into rock is being dumped, for example, a river bed — and see if natural processes are like my experimental procedures. If that’s true then I can say that my hypothesis, my idea about how cross-beds happen, is correct for at least the case that I studied in nature. Often I can generalize a bit. I might say that my idea about river bed deposits is applicable to any fluid flow that carries sandy sediment. But before I can have confidence in that wider claim, I’d have to test the idea in the lab and go find natural equivalents like, for example, wind-born sand deposits. And then fold that new data back into my ideas… which generates new ideas…
And so on and so on and so on… This is an iterative process. Each little idea is considered, tested, and then fit back into the real world. If it doesn’t fit, it’s not a valuable idea for that part of the system — though it might be valuable in some other application. This is how we’ve built up our knowledge of the world — which is a system that we just can’t study as a whole. We can’t even study its major parts. So we make models — ideas, mental maps — of the parts that we can study and try to fit those models together, refining the mental map until we can see and understand bigger and bigger portions of the system. However, it is unlikely that we’ll ever be able to understand the system in its entirety because there are so many things that we don’t know. It’s likely there are many things we can’t know. In any case, the gaps in our knowledge are holes in our models, holes that make those models unreliable.
For instance, until recently we thought plants grew best when they were spread apart so that they weren’t “competing against” each other for sunlight and nutrients. This turns out to be exactly wrong. Plants don’t compete; they are literally radically cooperative. They prefer a setting where diverse species, each with their own sets of strengths and weaknesses, live in close contact so that they can nurture each other and help each other to grow. Our models were so wrong because we simply can’t see underground where most of the contact happens, nor can we see the interactions at a microbial scale, nor can we see relationship through long time spans. We can’t see a forest. We can barely see the major parts of a tree. Our mental maps of plant growth were missing crucial components, and we didn’t even know those components existed. So our models never quite worked.
But as I said, it’s easier to test physical models against reality and discover where we’ve made mistakes or where we’re missing information. We could see that our models of forest growth didn’t quite work in a natural setting. We knew we were wrong, that the model was not describing the system. We eventually stumbled upon some of the missing parts — fungi, root interconnectivity, inter-species communication — and refined our models. Now, we can predict system activity a little better, but not with complete accuracy. Because we’ll probably never have all the pieces and processes. Still, we know we don’t know some things and we can work around those gaps. In any case, we have the real thing that we can study and learn from. One might even suggest that we don’t need the models… except that’s what humans do to comprehend our world.
I’m sorry if I bored you with that tedious narrative of our learning process, the scientific method. But a refresher course is never a bad thing. And I feel like perhaps it is time to remind ourselves that the models we create are not the whole systems, are never the whole systems, are always far short of even resembling whole systems. And this is even more true for systems made up of significant amounts of abstraction — like economics.
It is not wrong to label economics a system. It is a real process of relationship between many real physical components. It is based in the real world. But it is also an abstract human idea. In fact, it’s a whole network of abstract human ideas. There are critical parts that are simply not real. They are defined by our common agreements, not by real intrinsic qualities. Money, for example, is not a thing. It is an agreement. It is a place-holder for value, which is another abstraction. Money is nothing but what we say it is. This makes it very unreliable in models. We can’t go out into the real natural world and see how money behaves to test our ideas about it. Our ideas about it are all that it is… Hence our models are as flaky as we are.
And yet we pretend that our models are laws, that they have predictive power, that we can see the system in the present and that we can reliably envision that system in the future. This is sort of absurd just on the face of it. We know we can’t often predict human behavior with much reliability (marketing claims notwithstanding…). How then can we predict a system that is made up of large swaths of nothing but human behavior? But it gets even more ridiculous when we consider the externalities.
These are the parts of the system that we don’t put in our models. Usually this is because we just don’t like these components and processes, but sometimes it’s also very difficult to account for them. For a long time we had no way to measure air pollution and its effects in a system. This is still a very difficult model to build because… well… air is nothing but air. And yet it affects everything. It feeds into every part of every system on the surface of the Earth, but it is measured only with great difficulty and only in small segments of what is a continuous flow that covers the planet in an envelope dozens of miles thick. How do you model air pollution in a way that is manageable enough to fit into an economic model? How do you fit this whole system into another system? Mostly, we don’t. Because we can’t do it well, and we don’t really want to think much about that anyway. Because then we’d have to do something about it, and that something is liable to take value out of the economic system. We’d have to expend effort and resources on remediation and we’d still probably have to stop doing things that create pollution.
Which just about sums up our situation today… but you knew that…
What is less commonly acknowledged is that our atmosphere is a real physical system with real physical processes that do not change to fit our mental maps.
A system is a set of relationships. This means that there is give and take, and there is iteration, or recycling of outputs, if you will. A system adapts itself to this relational flow, swelling here, dwindling there, always seeking balance, input equals output. When a system is perturbed — which can happen for any reason or no reason at all — it will ebb and flow until the perturbation is fixed. Sometimes this means going back to a prior state of balance, more often it means finding a new state where things work and all the components are happy. (For want of a better word…) The point is, this whole business is unstoppable. It will happen. Even if the system is irreparably broken, it will hobble along adapting and changing, creating new components and relationships, until it once again comes to a balanced state. As long as things exist, this will happen.
So apply that idea to the Earth. And economics.
I’m a geologist by training and a gardener by long experience. I think in terms of real-world relationship. I have a very hard time giving much attention to abstractions because they so clearly do not exist except in our minds. (Which also don’t exist…) Maybe it takes someone with my life history to see that quite a lot of what we say about our future just isn’t likely to happen. It is not real-world based. It is not tested against natural, experiential fact. It’s not going to work that way.
This is as true of our ideas about economics as it is about the physical world. Consider the idea that we will continue to make messes with our wealth extraction systems until we basically render the planet sterile. Or even the less extreme idea that we’ll continue to “grow our economic systems” for as long as we want to, that we can plan on economic growth until well into this century, if not forever.
I can’t even type that sentence without laughing at the absurdity. (Or crying…) Grow it on what? What soil will not be depleted if all you do is take and take and take? Economists will say that we can substitute new growth media. We can exchange depleted resources for new ones that will have the same function and value in the system. OK… well… what? What real-world thing will replace oil? We’re all waiting for that magic trick and have been for at least the last fifty years. And then, if once we make that magic, what will be necessary to replace that magical substitute when it, too, is depleted? Which, if growth models are accurate, will happen much quicker than the few decades we’ve taken to deplete the planet’s accessible oil reserves.
In all our grand models, we seem to have externalized one of the basic mechanisms of change in the universe — negative feedback. Because we don’t like it. This is the ebb part of flow. The dwindling. The slowing and diminishing of components and relational processes until a level state is achieved. System happiness. We don’t like to be reminded of the limits that bind this system and keep it stable, so we just flat-out ignore depletion.
Depletion and pollution are both components of our economic system, because our economic system is a part of the real world, in which depletion and pollution are real things. Economics is a set of relationships that exist to meet bodily needs (though it’s been perverted into creating “wealth” for the last few hundred years). Our bodies are real things with real physical needs. So our economic system is based in the real physical world. And it is affected by real-world processes — like the depletion of resources and the increase in pollution. All these variables (note that word!) are part of a system that is seeking balance, and this is principally achieved through negative feedback.
We know what positive feedback is. We don’t like that too much either. But at least it sounds good. Positive. Growth. Enter the output into the system and get even more out. And more. And more. And more. Sounds great! Until there isn’t any room for more and the system implodes. (About where many urban centers are heading right now.)
Negative feedback is the opposite. You put the output back into the system and less comes out. It is the brakes on the system. The waning. We really don’t like those words. There actually isn’t a good word in English for the opposite of growth. It’s not death; it’s diminishment, except with a “goal” of becoming lessened, as growth “aims” to become more. So how do we put this concept-without-a-name into our models?
Pollution and depletion are variable components that form a negative feedback loop. That is, these two things will stop all our economic activity. Take the case of oil. We use it and this use creates pollution which causes expensive disasters and a loss of health and therefore labor. Pollution slows economic activity and removes value from the system as wealth is redirected to clean up all the messes and brokenness.
Meanwhile as we use oil, it is depleted. Maybe not absolutely gone, but certainly we don’t have much left that is cost effective. It costs more and more and more to get the less accessible deposits or to make lower quality oil useful. Economic depletion, that is, running out of the affordable stuff, is even worse than absolute depletion because when something is totally gone, we have more reason to change or find substitutes. With economic depletion, we keep thrashing around trying to use the increasingly expensive resource, spending more and more on that resource and getting less and less out of it until it bankrupts the whole system… it’s the ultimate Red Queen race… So depletion slows economic activity also.
But then that slows pollution and frees up some resources. Which may create a short run of economic growth or at least less de-growth. Which may then stimulate resource use and therefore depletion, while likely also creating more pollution. Which then slows things down. Which then allows a respite from slowing and maybe a bit of growth. Which then, which then, which then… and this is why it is called a negative feedback loop. The trend is down, but it is a spiral. In two dimensions, the graph of activity looks like a stair-case. We’ve all heard about Ugo Bardi’s Seneca Cliff, the idea that growth happens gradually and thus looks like a gently rising curve, but contraction happens fast and looks like a cliff. I think this is generally true. But the cliff is more an escalante. There are many level steps on the way down with abrupt drops in between them.
The difference in these ideas is how we perceive the decline. A cliff would probably be obvious. We may be like Wile E Coyote treading air for the few seconds after he runs off the butte (again) before cartoon gravity kicks in, but then the fall is dramatic and rather undeniable. However, it will be much easier to deny a stair step. We already seem to be doing just that. We may actually be several steps down already. Yet nobody seems to notice that we aren’t getting back up to that former plateau no matter how fast we run.
Negative feedback, in any case, will create a fluctuating downward curve. So it’s a good bet that this is the future we face. It’s not going to be up and up and up. It’s probably also not going to be down and down and down. Not that some of the steps aren’t going to be really sharp and painful, mind you! But there will be periods where it feels like downward change is not happening or is not happening quite as violently fast. Until we hit the valley floor. A new balance. System happiness.
What will not happen is that we’ll be able to keep this mess-making going for long enough to really destroy the Earth. We might be stupid and push buttons that will annihilate ourselves and many of the life-forms on the planet, but that’s not destruction by economic activity. That’s just stupid. (Unfortunately, we have a high capacity for stupid…) But all these models that talk about “business as usual” going on until the 22nd century? Not even remotely possible. We are already standing on the system brakes. We are creating too much mess and using up too many resources. It is already crashing through negative feedback. There is not even going to be a “usual” for many decades. Maybe centuries, if history is any indication. It’s all going to be change, though probably interspersed with lulling periods of leveling off, again if history is any indication. (Rome didn’t fall in a day… drama queen historians notwithstanding…)
So this is where our ideas about economics are being tested against reality. We are going to see if our ideas fit what actually is physically possible. I predict that we’re going to find out “the hard way” that our models are not in alignment with the real world. I suspect that we’re going to see a paring down of abstraction, a redefining of many of the variables in terms of limits, probably a return to the basic goals of meeting bodily needs. I’m pretty sure that even if the economists don’t get there, the planet is going to do all this for us. So we might as well get used to the idea and find our own, maybe gentler way to system happiness.
In any case, it is my deep and abiding hope that system happiness will also create a good deal more human happiness. And that is a hypothesis I would love to test. Any volunteers?
©Elizabeth Anker 2022
Teaser photo credit: The fly-ball governor is an early example of negative feedback. By R. Routledge – Image from "Discoveries & Inventions of the Nineteenth Century" by R. Routledge, 13th edition, published 1900., Public Domain, https://commons.wikimedia.org/w/index.php?curid=231047
