Thanks for this, upvoted! I agree with you that timelines seem like a really important angle that I neglected in the post --- I don't have a fully formed opinion about this yet but will think about it some more.
Thanks for the perspective! I agree in part with your point about trusting the models while the perturbations they predict are small, but even then I'd say that there are two very different possibilities:
we can safely ignore real-world nonlinearities, cascading effects, etc., because the economic models suggest the perturbations are small.
the predicted perturbations are small because the economic models neglect key real-world nonlinearities and cascading effects.
As long as we think the second option is plausible enough, strong skepticism of the models remains justified. I don't claim to know what's actually the case here --- this seems like a pretty important thing to work on understanding better.
Thanks for your comments, for the detailed response, and for upvoting on clarity rather than agreement! I'm looking forward to your upcoming report.
I am not enough of an expert on the economic models at this moment for a debate on the detailed ins and outs of the models to be particularly productive. Nevertheless I do have a lot of experience with mathematical modeling in general, and particularly in modeling systems with nonlinear phenomena (i.e. cascading/systemic effects). From this background, I find the complete absence from these models of phenomena that will almost obviously be key drivers in any global collapse (war, mass migration, etc. --- which are notably missing from the list you give) rather disturbing. As I said in the text, there is no way that models excluding phenomenon X can give you a reasonable estimate for the likelihood of X.
Of course things like war and mass migration are missing from the models because they're really hard to model, and so you can't fault the economic modelers for that. But all models are a crude abstraction of reality anyway; what's important is whether, for the scenario being studied, the models describe the real world in any useful way. I gladly concede that economic models are helpful for predicting, e.g. "small" changes in GDP due to climate change, but see no grounds yet for moderating my skepticism on their ability to say anything meaningful about risks of collapse.
I emphasize that these are not good grounds for thinking that the collapse risk is very high, and this is also not the position I am defending! But they are good grounds for being skeptical of the ability for current models to truly constrain the probability of these extreme scenarios.
Thanks for your comments! Some very quick thoughts on your latter two points:
I agree that one should be careful with letting outlier estimates drive the discussion on what the correct estimate is. Nevertheless, I do think that highlighting Lynas's estimate serves two particularly useful purposes here: it highlights that even an estimate this high is hard to concretely refute (as you noted), and it opens up the discussion of the wide range of intervening values (from the "standard" estimates of 1/10000 or 1/1000 all the way to, say, 1/10). While Lynas's estimate might indeed be an outlier, I suspect that a substantial fraction within EA have estimates scattered across this range; you can still think that Lynas overestimated the risk by two whole orders of magnitude and that the "standard" EA estimates are too low.
I also agree with you that climate scientists might be more sympathetic than most to the idea of climate-change driven collapse at median levels of warming, and so the fact that they don't really mention this in the literature is interesting. However I do suspect that this is primarily due to the difficulty of actually studying this using existing modeling frameworks (as discussed above) as well as the way in which "burden of proof" is typically interpreted in science (and especially in IPCC reports, where it seems to be unusually high). I think this makes it all the more important for EA folks to look at in more detail.
Great to hear, thanks! Appreciate the link to the discussion, and the points you make --- I definitely agree that there's no reason to think that the direct and indirect risks from climate change are anywhere near the same order of magnitude, and that this is one way an unjustified sense of confidence can creep in.
I'm really sorry to hear about this --- I think ALLFED's work fills a really important niche. I donated and would encourage others to do so as well!
Thanks for this, upvoted! I agree with you that timelines seem like a really important angle that I neglected in the post --- I don't have a fully formed opinion about this yet but will think about it some more.
Thanks for these great points! I agree that these are both things that should be looked at further.
Thanks for the perspective! I agree in part with your point about trusting the models while the perturbations they predict are small, but even then I'd say that there are two very different possibilities:
As long as we think the second option is plausible enough, strong skepticism of the models remains justified. I don't claim to know what's actually the case here --- this seems like a pretty important thing to work on understanding better.
Thanks for your comments, for the detailed response, and for upvoting on clarity rather than agreement! I'm looking forward to your upcoming report.
I am not enough of an expert on the economic models at this moment for a debate on the detailed ins and outs of the models to be particularly productive. Nevertheless I do have a lot of experience with mathematical modeling in general, and particularly in modeling systems with nonlinear phenomena (i.e. cascading/systemic effects). From this background, I find the complete absence from these models of phenomena that will almost obviously be key drivers in any global collapse (war, mass migration, etc. --- which are notably missing from the list you give) rather disturbing. As I said in the text, there is no way that models excluding phenomenon X can give you a reasonable estimate for the likelihood of X.
Of course things like war and mass migration are missing from the models because they're really hard to model, and so you can't fault the economic modelers for that. But all models are a crude abstraction of reality anyway; what's important is whether, for the scenario being studied, the models describe the real world in any useful way. I gladly concede that economic models are helpful for predicting, e.g. "small" changes in GDP due to climate change, but see no grounds yet for moderating my skepticism on their ability to say anything meaningful about risks of collapse.
I emphasize that these are not good grounds for thinking that the collapse risk is very high, and this is also not the position I am defending! But they are good grounds for being skeptical of the ability for current models to truly constrain the probability of these extreme scenarios.
Thanks for your comments! Some very quick thoughts on your latter two points:
I agree that one should be careful with letting outlier estimates drive the discussion on what the correct estimate is. Nevertheless, I do think that highlighting Lynas's estimate serves two particularly useful purposes here: it highlights that even an estimate this high is hard to concretely refute (as you noted), and it opens up the discussion of the wide range of intervening values (from the "standard" estimates of 1/10000 or 1/1000 all the way to, say, 1/10). While Lynas's estimate might indeed be an outlier, I suspect that a substantial fraction within EA have estimates scattered across this range; you can still think that Lynas overestimated the risk by two whole orders of magnitude and that the "standard" EA estimates are too low.
I also agree with you that climate scientists might be more sympathetic than most to the idea of climate-change driven collapse at median levels of warming, and so the fact that they don't really mention this in the literature is interesting. However I do suspect that this is primarily due to the difficulty of actually studying this using existing modeling frameworks (as discussed above) as well as the way in which "burden of proof" is typically interpreted in science (and especially in IPCC reports, where it seems to be unusually high). I think this makes it all the more important for EA folks to look at in more detail.
Great to hear, thanks! Appreciate the link to the discussion, and the points you make --- I definitely agree that there's no reason to think that the direct and indirect risks from climate change are anywhere near the same order of magnitude, and that this is one way an unjustified sense of confidence can creep in.