Limits To Growth is kind of a test case for whether people got the memo about the academic research crisis or not (which incorporates the replication crisis, but is wider than it).
The World3 model came out of social studies and isn't even really science, given that the authors never tried to obtain evidence for their hypothesis before publishing. World3 is a toy with no predictive value, which is why reimplementing it in HTML is possible: it's just a handful of equations. Such a thing never could make accurate predictions, which was extremely obvious at the time and pointed out by many (e.g. Nordhaus, Smil), but this didn't stop its authors being feted as great scientists regardless.
There's a lot of similarities here with COVID modelling in the sense that these sorts of academic modelling exercises all seem to suffer the same kinds of problems and social failure modes:
• Over-simplified to the point that laypeople can easily spot what's missing.
• Guesses pulled out of thin air are treated as having equal value to measured data.
• An implicit assumption that the population is purely static and doesn't react to changing circumstances in any way. When later the predictions turn out to be wrong it's argued that they would have been right if not for the modelling causing behavioral changes, making the entire exercise unfalsifiable pseudo-science by its own premise.
• Either never validated against reality or actually falsified, but academics pretend otherwise.
• Predictions phrased vaguely enough that what most people would consider to be falsification is later argued to be within the bounds of one of the many possible scenarios.
The uselessness of such modelling exercises is evident in the fact that despite at least 50+ years of investment by academia and government into them, there are no academic computerized models widely being used to predict the course of the society or economy.
The assumption that human behavior and economic system behavior are static is to me the biggest flaw in virtually all of these models that I have seen. It also underlies adjacent panics that arise based on this kind of thinking, like the overpopulation panic. We can see how wrong that was in retrospect, though it may have been right if all behaviors and systems remained static.
I wish the present day underpopulation panic people would get the memo. They are making the exact same errors around mindless extrapolation of present behaviors and trends. Instead of “with these birth rates we will be eating people in 50 years” it’s “with these birth rates we will go extinct!”
There are things to be very concerned about. What happens when you take current behavior and trends and drag it forward on a spreadsheet is not one of them. “If our company continues this growth trajectory we will be worth more than the entire global economy in 25 years!” Every single one of these models and panics is a version of that thinking.
I think a more useful question when faced with such predictions is “how will this not happen?”
This is what the field of complex systems and complexity economics tries to deal with but it doesn't seem like complexity economics has taken off.
With economics too there is a problem that a correct prediction that is acted on will be gamed and become a self defeating seemingly "wrong" prediction.
I think the 2022 recession calls were probably a good example.
If everyone believes there is going to be a recession in the future and everyone acts to avoid the recession we can't know the counter factual in the economy of not predicting the recession to know what was the right prediction.
I have read this process referred to reflexivity but even a best selling trading book by George Soros himself didn't get this idea to stick in the popular mind.
It seems like prediction is not even the right word to use or we should have a different word for a prediction that can influence the outcome of what it is predicting. Then there is even strong and weak versions of this process that we haven't bothered to separate from the proper word prediction in the sense of weather prediction. That the prediction is basically independent of what it is trying to predict.
Sure it took off. There's no conceptual problem with your predictions affecting people such that your inputs change.
This is why I was careful in my post to talk about public sector models (academic, government). There are lots of private sector actors successfully modelling aspects of society and the economy: actuaries, quants, even day traders. They're all acting on the predictions of a private model in a dynamic and even adversarial environment, yet they succeed. The problems here are not on the conceptual level, they're to do with the prevalence of sub-zero intellectual standards in the public sector. Nobody forces academics to make a big fuss over models that have never been validated, or which have massive CIs, or which generate so many or such long term 'scenarios' that they're effectively unfalsifiable.
All these things are the kind of pseudo-scientific modelling practices that result in firings or bankruptcies when done in the "real world". Government subsidies keep the low standards going in the public sector. Ultimately, ending this farce requires the social studies to be defunded, as is now happening in NZ and hopefully soon in the rest of the world.
No, it's a test case for whether people understand exponential growth. The very first chapter of the book goes into this.
The inputs don't matter that much when you are growing exponentially. By the time you get pricing signals, you are already cooked.
Its like bacteria in a petri dish, one day you are at 12% full, then, 25%, then 50%, then 100%, then you die off.
We are already seeing the early signs of the system collapse with climate change and we have more and more people pumping more and more carbon into the atmosphere.
The keystone of modern society is agriculture, which was only possible because the climate stabilize 20k years ago. We are destabilizing that climate and are in uncharted territory. We had the opportunity to do a course correction when the book was published but people like you down played the ramifications. In the last 50 years we have done nothing and are now cooked.
Bacteria in a petri dish aren't a useful analogue for the real world. It's a fully artificial environment.
Bluntly, "you don't understand exponential growth" is the sort of thing said by people who don't understand it themselves. I'm really sick of this meme. COVID was full of nonsense being talked about exponential growth, and I was calling it out from the start (e.g. here on HN [1]). A huge number of people are confused about this topic, probably because so many of the self-proclaimed experts presented by media and government are also confused.
Limits To Growth and similar socialist/green arguments have several problems with the concept:
1. An implicit assumption that populations naturally grow exponentially until they run out of resources. They don't. There are many periods in human history where populations were stable for thousands or even tens of thousands of years, or where they actively shrunk despite not fully exploiting their resources. Europe after the fall of the Roman Empire, the history of the Australian Aboriginals and the whole modern developed world are examples of this.
2. An assumption that GDP cannot grow forever, even though it can. GDP isn't a natural resource, it's a number on a ledger. Nothing stops GDP growing exponentially because it's basically a multiplier of population and productivity (broadly defined as "technology"), and there's no known limit on technological progress.
3. To try and make (2) seem more plausible they ignore the possibility of technological progress reducing resource requirements. This is the problem Malthus had. But technology fundamentally changes the relationship between man and his environment, eliminating some constraints whilst creating others. Before fission was harnessed, nobody cared about uranium extraction. The resource was ignored by all arguments about limits to human growth. After reactors were invented uranium limits suddenly became relevant and coal limits became much less relevant, but uranium is abundant and power plants need hardly any, so this basically eliminated electricity generation as a meaningful constraint on human growth. For the societies that choose to use it, at least.
I learned these lessons the hard way early on in life. It was 2004 and there was one of the semi-regularly occurring peak oil scares happening at that time, which a friend turned me on to. The argument for peak oil at first seems irrefutable: oil is a finite resource, fields run dry, we're using lots of it and in many use cases there's no known replacement. On top of that, in 2004 world production wasn't increasing despite prices growing to never before seen levels, which gave the appearance of unsolvable supply constraints, and there were signs that the secretive Saudis could not pump more. The scare ended thanks to a few things: the 2008 recession (killed some demand for a while), the Bush Iraq troop surge, and fracking. The latter was known about in peak oil circles but at the time the technology was not yet proven, and many dismissed it as delusional "cornucopianism", meaning the belief that the needs of humanity can be solved through technology. Others said it can't work because of a pseudo-economic concept called EROEI (energy returned on energy invested). Of course we know what happened next: the US made fracking work and the US oil industry went from long term decline to the world's top producer of oil again. That broke the back of OPEC and prices fell again, albeit not to the low level they were at pre-2004.
The World3 model came out of social studies and isn't even really science, given that the authors never tried to obtain evidence for their hypothesis before publishing. World3 is a toy with no predictive value, which is why reimplementing it in HTML is possible: it's just a handful of equations. Such a thing never could make accurate predictions, which was extremely obvious at the time and pointed out by many (e.g. Nordhaus, Smil), but this didn't stop its authors being feted as great scientists regardless.
There's a lot of similarities here with COVID modelling in the sense that these sorts of academic modelling exercises all seem to suffer the same kinds of problems and social failure modes:
• Over-simplified to the point that laypeople can easily spot what's missing.
• Guesses pulled out of thin air are treated as having equal value to measured data.
• An implicit assumption that the population is purely static and doesn't react to changing circumstances in any way. When later the predictions turn out to be wrong it's argued that they would have been right if not for the modelling causing behavioral changes, making the entire exercise unfalsifiable pseudo-science by its own premise.
• Either never validated against reality or actually falsified, but academics pretend otherwise.
• Predictions phrased vaguely enough that what most people would consider to be falsification is later argued to be within the bounds of one of the many possible scenarios.
The uselessness of such modelling exercises is evident in the fact that despite at least 50+ years of investment by academia and government into them, there are no academic computerized models widely being used to predict the course of the society or economy.