So to me it feels like as we add random stuff like “yeah there are revolutions but we don’t have any prediction about what they will look like” makes the richer model less compelling. It moves me more towards the ignorant perspective of “sometimes acceleration happens, maybe it will happen soon?”, which is what you get in the limit of adding infinitely many ex ante unknown bells and whistles to your model.
I agree the richer stories, if true, imply a more ignorant perspective. I just think it’s plausible that the more ignorant perspective is the correct perspective.
My general feeling towards the evolution of the economy over the past ten thousand years, reading historical analysis, is something like: “Oh wow, this seems really complex and heterogeneous. It’d be very surprising if we could model these processes well with a single-variable model, a noise term, and a few parameters with stable values.” It seems to me like we may in fact just be very ignorant.
Does a discontinuous change from fossil-fuel use even fit the data? It doesn’t seem to add up at all to me (e.g. doesn’t match the timing of acceleration, there are lots of industries that seemed to accelerate without reliance on fossil fuels, etc.), but would only consider a deep dive if someone actually wanted to stake something on that.
Fossil fuels wouldn’t be the cause of the higher global growth rates, in the 1500-1800 period; coal doesn’t really matter much until the 19th century. The story with fossil fuels is typically that there was a pre-existing economic efflorescence that supported England’s transition out of an ‘organic economy.’ So it’s typically a sort of tipping point story, where other factors play an important role in getting the economy to the tipping point.
Is “intensive agriculture” a well-defined thing? (Not rhetorical.) It didn’t look like “the beginning of intensive agriculture” corresponds to any fixed technological/social/environmental event (e.g. in most cases there was earlier agriculture and no story was given about why this particular moment would be the moment), it just looked like it was drawn based on when output started rising faster.
I’m actually unsure of this. Something that’s not clear to me is to what extent the distinction is being drawn in a post-hoc way (i.e. whether intensive agriculture is being implicitly defined as agriculture that kicks off substantial population growth). I don’t know enough about this.
Doing a regression on yearly growth rates seems like a bad way to approach this.
I don’t think I agree, although I’m not sure I understand your objection. Supposing we had accurate data, it seems like the best approach is running a regression that can accommodate either hyperbolic or exponential growth — plus noise — and then seeing whether we can reject the exponential hypothesis. Just noting that the growth rate must have been substantially higher than average within one particular millennium doesn’t necessarily tell us enough; there’s still the question of whether this is plausibly noise.
Of course, though, we have very bad data here—so I suppose this point doesn’t matter too much either way.
If you just keep listing things, it stops being a plausible source of a discontinuity—you then need to give some story for why your 7 factors all change at the same time. If they don’t, e.g. if they just vary randomly, then you are going to get back to continuous change.
You don’t need a story about why they changed at roughly the same time to believe that they did change at roughly the same time (i.e. over the same few century period). And my impression is that that, empirically, they did change at roughly the same time. At least, this seems to be commonly believed.
I don’t think we can reasonably assume they’re independent. Economic histories do tend to draw casual arrows between several of these differences, sometimes suggesting a sort of chain reaction, although these narrative causal diagrams are admittedly never all that satisfying; there’s still something mysterious here. On the other hand, higher population levels strike me as a fairly unsatisfying underlying cause.
[[EDIT: Just to be clear, I don’t think the phase-transition/inflection-point story is necessarily much more plausible than the noisy hyperbolic story. I don’t have very resilient credences here. But I think that, in the absence of good long-run growth data, they’re at least comparably plausible. I think that economic history narratives, the fairly qualitative differences between modern and pre-modern economies, and evidence from between-country variation in modern times count for at least as much as the simplicity prior.]]
Economic histories do tend to draw casual arrows between several of these differences, sometimes suggesting a sort of chain reaction, although these narrative causal diagrams are admittedly never all that satisfying; there’s still something mysterious here.
Just to make this more concrete:
One example of an IR narrative that links a few of these changes together is Robert Allen’s. To the extent that I understand/remember it, the narrative is roughly: The early modern expansion of trade networks caused an economic boom in England, especially in textile manufacturing. As a result, wages in England became unusually high. These high wages created unusually strong incentives to produce labor-saving technology. (One important effect of the Malthusian conditions is that they make labor dirt cheap.) England, compared to a few other countries that had similarly high wages at other points in history, also had access to really unusually cheap energy; they had huge and accessible coal reserves, which they were already burning as a replacement for wood. The unusually high levels of employment in manufacturing and trade also supported higher levels of literacy and numeracy. These conditions came together to support the development of technologies for harnessing fossil fuels, in the 19th century, and the rise of intensive R&D; these may never have been economically rational before. At this point, there was now a virtuous cycle that allowed England’s growth—which was initially an unsustainable form of growth based on trade, rather than technological innovation—to become both sustained and innovation-driven. The spark then spread to other countries.
This particular tipping point story is mostly a story about why growth rates increased from the 19th century onward, although the growth surge in the previous few centuries, largely caused by the Colombian exchange and expansion of trade networks, still plays an important causal role; the rapid expansion of trade networks drives British wages up and makes it possible for them to profitably employ a large portion of their population in manufacturing.
It feels like you are drawing some distinction between “contingent and complicated” and “noise.” Here are some possible distinctions that seem relevant to me but don’t actually seem like disagreements between us:
If something is contingent and complicated, you can expect to learn about it with more reasoning/evidence, whereas if it’s noise maybe you should just throw up your hands. Evidently I’m in the “learn about it by reasoning” category since I spend a bunch of time thinking about AI forecasting.
If something is contingent and complicated, you shouldn’t count on e.g. the long-run statistics matching the noise distribution—there are unmodeled correlations (both real and subjective). I agree with this and think that e.g. the singularity date distributions (and singularity probability) you get out of Roodman’s model are not trustworthy in light of that (as does Roodman).
So it’s not super clear there’s a non-aesthetic difference here.
If I was saying “Growth models imply a very high probability of takeoff soon” then I can see why your doc would affect my forecasts. But where I’m at from historical extrapolations is more like “maybe, maybe not”; it doesn’t feel like any of this should change that bottom line (and it’s not clear how it would change that bottom line) even if I changed my mind everywhere that we disagree.
“Maybe, maybe not” is still a super important update from the strong “the future will be like the recent past” prior that many people implicitly have and I might otherwise take very seriously. It also leads me to mostly dismiss arguments like “this is obviously not the most important century since most aren’t.” But it mostly means that I’m actually looking at what is happening technologically.
You may be responding to writing like this short post where I say “We have been in a period of slowing growth for the last forty years. That’s a long time, but looking over the broad sweep of history I still think the smart money is on acceleration eventually continuing, and seeing something like [hyperbolic growth]...”. I stand by the claim that this is something like the modal guess—we’ve had enough acceleration that the smart money is on it continuing, and this seems equally true on the revolutions model. I totally agree that any specific thing is not very likely to happen, though I think it’s my subjective mode. I feel fine with that post but totally agree it’s imprecise and this is what you get for being short.
The story with fossil fuels is typically that there was a pre-existing economic efflorescence that supported England’s transition out of an ‘organic economy.’ So it’s typically a sort of tipping point story, where other factors play an important role in getting the economy to the tipping point.
OK, but if those prior conditions led to a great acceleration before the purported tipping point, then I feel like that’s mostly what I want to know about and forecast.
Supposing we had accurate data, it seems like the best approach is running a regression that can accommodate either hyperbolic or exponential growth — plus noise — and then seeing whether we can object the exponential hypothesis. Just noting that the growth rate must have been substantially higher than average within one particular millennium doesn’t necessarily tell us enough; there’s still the question of whether this is plausibly noise.
I don’t think that’s what I want to do. My question is, given a moment in history, what’s the best way to guess whether and in how long there will be significant acceleration? If I’m testing the hypothesis “The amount of time before significant acceleration tends to be a small multiple of the current doubling time” then I want to look a few doublings ahead and see if things have accelerated, averaging over a doubling (etc. etc.), rather than do a regression that would indirectly test that hypothesis by making additional structural assumptions + would add a ton of sensitivity to noise.
You don’t need a story about why they changed at roughly the same time to believe that they did change at roughly the same time (i.e. over the same few century period). And my impression is that that, empirically, they did change at roughly the same time. At least, this seems to be commonly believed.
I don’t think we can reasonably assume they’re independent. Economic histories do tend to draw casual arrows between several of these differences, sometimes suggesting a sort of chain reaction, although these narrative causal diagrams are admittedly never all that satisfying; there’s still something mysterious here. On the other hand, higher population levels strike me as a fairly unsatisfying underlying cause.
It looked like you were listing those things to help explain why you have a high prior in favor of discontinuities between industrial and agricultural societies. “We don’t know why those things change together discontinuously, they just do” seems super reasonable (though whether that’s true is precisely what’s at issue). But it does mean that listing out those factors adds nothing to the a priori argument for discontinuity.
Indeed, if you think that all of those are relevant drivers of growth rates then all else equal I’d think you’d expect more continuous progress, since all you’ve done is rule out one obvious way that you could have had discontinuous progress (namely by having the difference be driven by something that had a good prima facie reason to change discontinuously, as in the case of the agricultural revolution) and now you’ll have to posit something mysterious to get to your discontinuous change.
I agree the richer stories, if true, imply a more ignorant perspective. I just think it’s plausible that the more ignorant perspective is the correct perspective.
My general feeling towards the evolution of the economy over the past ten thousand years, reading historical analysis, is something like: “Oh wow, this seems really complex and heterogeneous. It’d be very surprising if we could model these processes well with a single-variable model, a noise term, and a few parameters with stable values.” It seems to me like we may in fact just be very ignorant.
Fossil fuels wouldn’t be the cause of the higher global growth rates, in the 1500-1800 period; coal doesn’t really matter much until the 19th century. The story with fossil fuels is typically that there was a pre-existing economic efflorescence that supported England’s transition out of an ‘organic economy.’ So it’s typically a sort of tipping point story, where other factors play an important role in getting the economy to the tipping point.
I’m actually unsure of this. Something that’s not clear to me is to what extent the distinction is being drawn in a post-hoc way (i.e. whether intensive agriculture is being implicitly defined as agriculture that kicks off substantial population growth). I don’t know enough about this.
I don’t think I agree, although I’m not sure I understand your objection. Supposing we had accurate data, it seems like the best approach is running a regression that can accommodate either hyperbolic or exponential growth — plus noise — and then seeing whether we can reject the exponential hypothesis. Just noting that the growth rate must have been substantially higher than average within one particular millennium doesn’t necessarily tell us enough; there’s still the question of whether this is plausibly noise.
Of course, though, we have very bad data here—so I suppose this point doesn’t matter too much either way.
You don’t need a story about why they changed at roughly the same time to believe that they did change at roughly the same time (i.e. over the same few century period). And my impression is that that, empirically, they did change at roughly the same time. At least, this seems to be commonly believed.
I don’t think we can reasonably assume they’re independent. Economic histories do tend to draw casual arrows between several of these differences, sometimes suggesting a sort of chain reaction, although these narrative causal diagrams are admittedly never all that satisfying; there’s still something mysterious here. On the other hand, higher population levels strike me as a fairly unsatisfying underlying cause.
[[EDIT: Just to be clear, I don’t think the phase-transition/inflection-point story is necessarily much more plausible than the noisy hyperbolic story. I don’t have very resilient credences here. But I think that, in the absence of good long-run growth data, they’re at least comparably plausible. I think that economic history narratives, the fairly qualitative differences between modern and pre-modern economies, and evidence from between-country variation in modern times count for at least as much as the simplicity prior.]]
Just to make this more concrete:
One example of an IR narrative that links a few of these changes together is Robert Allen’s. To the extent that I understand/remember it, the narrative is roughly: The early modern expansion of trade networks caused an economic boom in England, especially in textile manufacturing. As a result, wages in England became unusually high. These high wages created unusually strong incentives to produce labor-saving technology. (One important effect of the Malthusian conditions is that they make labor dirt cheap.) England, compared to a few other countries that had similarly high wages at other points in history, also had access to really unusually cheap energy; they had huge and accessible coal reserves, which they were already burning as a replacement for wood. The unusually high levels of employment in manufacturing and trade also supported higher levels of literacy and numeracy. These conditions came together to support the development of technologies for harnessing fossil fuels, in the 19th century, and the rise of intensive R&D; these may never have been economically rational before. At this point, there was now a virtuous cycle that allowed England’s growth—which was initially an unsustainable form of growth based on trade, rather than technological innovation—to become both sustained and innovation-driven. The spark then spread to other countries.
This particular tipping point story is mostly a story about why growth rates increased from the 19th century onward, although the growth surge in the previous few centuries, largely caused by the Colombian exchange and expansion of trade networks, still plays an important causal role; the rapid expansion of trade networks drives British wages up and makes it possible for them to profitably employ a large portion of their population in manufacturing.
It feels like you are drawing some distinction between “contingent and complicated” and “noise.” Here are some possible distinctions that seem relevant to me but don’t actually seem like disagreements between us:
If something is contingent and complicated, you can expect to learn about it with more reasoning/evidence, whereas if it’s noise maybe you should just throw up your hands. Evidently I’m in the “learn about it by reasoning” category since I spend a bunch of time thinking about AI forecasting.
If something is contingent and complicated, you shouldn’t count on e.g. the long-run statistics matching the noise distribution—there are unmodeled correlations (both real and subjective). I agree with this and think that e.g. the singularity date distributions (and singularity probability) you get out of Roodman’s model are not trustworthy in light of that (as does Roodman).
So it’s not super clear there’s a non-aesthetic difference here.
If I was saying “Growth models imply a very high probability of takeoff soon” then I can see why your doc would affect my forecasts. But where I’m at from historical extrapolations is more like “maybe, maybe not”; it doesn’t feel like any of this should change that bottom line (and it’s not clear how it would change that bottom line) even if I changed my mind everywhere that we disagree.
“Maybe, maybe not” is still a super important update from the strong “the future will be like the recent past” prior that many people implicitly have and I might otherwise take very seriously. It also leads me to mostly dismiss arguments like “this is obviously not the most important century since most aren’t.” But it mostly means that I’m actually looking at what is happening technologically.
You may be responding to writing like this short post where I say “We have been in a period of slowing growth for the last forty years. That’s a long time, but looking over the broad sweep of history I still think the smart money is on acceleration eventually continuing, and seeing something like [hyperbolic growth]...”. I stand by the claim that this is something like the modal guess—we’ve had enough acceleration that the smart money is on it continuing, and this seems equally true on the revolutions model. I totally agree that any specific thing is not very likely to happen, though I think it’s my subjective mode. I feel fine with that post but totally agree it’s imprecise and this is what you get for being short.
OK, but if those prior conditions led to a great acceleration before the purported tipping point, then I feel like that’s mostly what I want to know about and forecast.
I don’t think that’s what I want to do. My question is, given a moment in history, what’s the best way to guess whether and in how long there will be significant acceleration? If I’m testing the hypothesis “The amount of time before significant acceleration tends to be a small multiple of the current doubling time” then I want to look a few doublings ahead and see if things have accelerated, averaging over a doubling (etc. etc.), rather than do a regression that would indirectly test that hypothesis by making additional structural assumptions + would add a ton of sensitivity to noise.
It looked like you were listing those things to help explain why you have a high prior in favor of discontinuities between industrial and agricultural societies. “We don’t know why those things change together discontinuously, they just do” seems super reasonable (though whether that’s true is precisely what’s at issue). But it does mean that listing out those factors adds nothing to the a priori argument for discontinuity.
Indeed, if you think that all of those are relevant drivers of growth rates then all else equal I’d think you’d expect more continuous progress, since all you’ve done is rule out one obvious way that you could have had discontinuous progress (namely by having the difference be driven by something that had a good prima facie reason to change discontinuously, as in the case of the agricultural revolution) and now you’ll have to posit something mysterious to get to your discontinuous change.