Dr. David Denkenberger co-founded and directs the Alliance to Feed the Earth in Disasters (ALLFED.info) and donates half his income to it. He received his B.S. from Penn State in Engineering Science, his masters from Princeton in Mechanical and Aerospace Engineering, and his Ph.D. from the University of Colorado at Boulder in the Building Systems Program. His dissertation was on an expanded microchannel heat exchanger, which he patented. He is an associate professor at the University of Canterbury in mechanical engineering. He received the National Merit Scholarship, the Barry Goldwater Scholarship, the National Science Foundation Graduate Research Fellowship, is a Penn State distinguished alumnus, and is a registered professional engineer. He has authored or co-authored 134 publications (>4000 citations, >50,000 downloads, h-index = 32, second most prolific author in the existential/global catastrophic risk field), including the book Feeding Everyone no Matter What: Managing Food Security after Global Catastrophe. His food work has been featured in over 25 countries, over 200 articles, including Science, Vox, Business Insider, Wikipedia, Deutchlandfunk (German Public Radio online), Discovery Channel Online News, Gizmodo, Phys.org, and Science Daily. He has given interviews on 80,000 Hours podcast (here and here) and Estonian Public Radio, WGBH Radio, Boston, and WCAI Radio on Cape Cod, USA. He has given over 80 external presentations, including ones on food at Harvard University, MIT, Princeton University, University of Cambridge, University of Oxford, Cornell University, University of California Los Angeles, Lawrence Berkeley National Lab, Sandia National Labs, Los Alamos National Lab, Imperial College, and University College London.
Denkenberger
This was very helpful! I found the diagrams particularly useful. Visible lighting design for rooms has a similar problem of uniform illumination, but it is mitigated by the fact that there is significant reflection of the light, which I presume does not apply for far UVC.
Has there been any work on planning to relocate existing UV systems to the most critical tasks, if an extreme pandemic hit soon, of making more super PPE/UV systems?One unpublished study by a Russian academic and a CDC researcher allegedly estimated that the cost of 1 ACH by ventilation is about $135.91 USD and by GUV is about $14.44 USD.[131] 1DaySooner and Rethink Priorities have estimates that “The price of current systems is currently too high for at-scale deployment, though there are reasons to think the price can be lowered significantly;” they estimate that the cost of upgrading all U.S. buildings for improved indoor air quality would be about $120 billion - $420 billion.[132]
The units do not appear to be complete—cost of 1 ACH for how big of space? Footnote 131 requires a password. Footnote 132 says “all public buildings in the US” not “all US buildings.” If public building is defined as this, I would guess that would control less than 10% of transmission in the US.
Why is flesh weaker than diamond?
I don’t think this is a fair comparison. If nature wanted skin to be harder, it can do that, for instance with scales (particularly hard in the case of turtle shells). Of course your logic explains why diamond is harder than bone. But if you want a small thing that could penetrate flesh, we already have it in the form of parasites.
One of the points in the book Strangers Drowning was that very dedicated altruists (some EAs included) live like it is war time all the time. Basically, the urgency of people dying from poverty, animals suffering, and humanity’s future at risk demand the sacrifices that are typically reserved for war time. Another example is if existential risk were high, some argue that we should be on “extreme war footing” and dedicate a large portion of society’s resources to reducing the risk. I’m interested in your perspective on these thoughts.
Thanks for the correction! I have fixed it and added a link (the link was in the main document, but it’s good to have it in the executive summary as well).
This is a decent summary, but there are a couple corrections:
ALLFED increased paid team members, but much less than doubled (we have capacity to expand more quickly with additional funding).
We do have 17 advisory board members, but they represent 4 countries, not 9 (the 9 countries were represented by the 17 team members at the retreat).
ALLFED’s 2023 Highlights
Nice post!
The model does not predict much differences between the different scenarios until 2020-2030. Therefore, we only know that the model has not been falsified so far, but it is still unclear what the path is we are currently on.
I think it would be helpful to see an overlay of our actual trajectory. Though the absolute values of the models are not that different for the period 2000 to 2020, the slopes are quite different. I think there was a paper analyzing the fits including the slopes. The increase of production of food since the year 2000 has been much larger than any of the models predicted. Also, I think the increase in industrial capacity is higher than any of these models. Interestingly, some people interpret this as us overshooting farther, so then we will fall more dramatically. But because we generally have not seen reduction in slopes, I don’t really see evidence for this, so I think that the optimistic interpretation is more likely to be right, basically that we have innovated around limits to growth so far.
Thanks for all you have done!
Finally, EAs have treated EtG as increasingly more weird, especially offline, defeating the original argument for engaging.
This is very disappointing, especially because, if you disregard “still deciding”, EtG was the second most popular route to impact among EAs in the 2022 survey.
(leading a—dare I say—successful effective nonprofit)
Sure—go ahead and dare. :)
My day job is associate professor of mechanical engineering at University of Canterbury in New Zealand, and I volunteer for ALLFED. Nearly 100% of my donations are to ALLFED. I think that ALLFED is the most cost-effective way of improving the long run future at the margin (see here and here, though I’m not quite as bullish as the mean survey/poll results in those papers), but there are orders of magnitude of uncertainty, and I think more total money should be put into AGI safety.
As one who donates 50%, it doesn’t seem like it should be that uncommon. One way I think about it is earning like upper-middle-class, living like middle-class, and donating like upper-class. Tens of percent of people work for tens of percent less money in sectors like nonprofits and governments. And I’ve heard of quite a few non-EAs who have taken jobs for half the money. And yet most people think about donating that large of a percent very differently than taking a job that pays less. I’m still not sure why—other than that it is uncommon or “weird.”
I agree that most academic research is a bad ROI but I find that a lot of this sort of ‘nobody reads research’ commentary is equating reads with citations which seems completely wrong. By that metric most forum posts would also not be read by anyone.
I agree-for one, the studies I’ve seen saying that the median publication is not cited are including conference papers, so if one is talking about the peer-reviewed literature, citations are significantly greater. I’ve estimated the average number of citations per paper is around 30 for the peer-reviewed literature. Furthermore, from what I’ve seen, the number of reads on places like ResearchGate and Academia.edu tend to be one to two orders of magnitude greater than the number of citations. So I think a reasonable expectation for a peer-reviewed paper is hundreds or thousands of reads.
The government could internalize this positive externality by providing incentives, like this.
I was assuming 50 % reduction in international trade, and 50 % of that reduction being caused by climatic effects, so only 25 % (= 0.5^2) caused by climatic effects. I have changed “50 % of it” to “50 % of this loss” in my original reply to clarify.
That makes sense. Thanks for putting the figure in!
I guess famine deaths due to the climatic effects are described by a logistic function, which is a strictly increasing function, so I agree with the above. However, I guess the increase will be pretty small for low levels of soot.
If it were linear starting at 10.5 Tg and going to 22.1 Tg, versus linear starting at 0 Tg and going to 22 Tg, then I think the integral (impact) would be about four times as much. But I agree if you are going linear from 10.4 Tg versus logistic from 0 Tg, the difference would not be as large. But it still could be a factor of two or three, so I think it’s good to run a sensitivity case.
Very interesting!
Space colonies. Fertility is low in wealthy countries with large unsettled territories (Canada, Australia), even though they are far more hospitable than other planets. There is no reason to think that space colonies alone will reverse the fertility decline.
I think the incentive for fertility depends on the level of connection with the Earth. If it were fully independent from Earth, it would have a strong incentive to increase population because there are large economies of scale in terms of increasing the standard of living, including being able to create more living space per person, more advanced electronics, media, etc.
Both sides targeted civilians in WWII. Hopefully that is not the case now, but I’m not sure.
>Half of the impact of the total loss of international food trade would cause 2.6% to >die according to Xia 2022. So why is it not 4.43%+2.6% = 7.0% mortality?
In my BOTEC with “arguably more reasonable assumptions”, I am assuming just a 50 % reduction in international food trade, not 100 %.
That’s why I only attributed half of the impact of total loss of international food trade. If I attributed all the impact, it would have been 4.43%+5.2% = 9.6% mortality. I don’t see how you are getting 5.67% mortality.
My famine deaths due to the climatic effects are a piecewise linear function which is null up to a soot injection into the stratosphere of 11.3 Tg. So, if one inputs 5 Tg into the function, the output is 0 famine deaths due to the climatic effects, not negative deaths.
My understanding is that you chose this piecewise linear function to be null at 11.3 Tg because that’s where the blue and gray dotted lines crossed, meaning that it appeared that the climate impacts did not kill anyone below 11.3 Tg. But what I’m arguing is that those two lines had different assumptions about feeding food to animals and waste, so the conclusion is not correct that there was no climate mortality below 11.3 Tg. And this is supported by the fact that there are currently under nutrition deaths, and any nonzero Tg is likely to increase those deaths.
I am happy to describe what happens in a very worst case scenario, involving no adaptations, and no international food trade.
There are many ways that things could go worse than that scenario. As I have mentioned, there could be reductions in nonfood trade, such as fertilizers, pesticides, agricultural equipment, energy, etc. There could be further international conflict. There could be civil unrest in countries and a breakdown of the rule of law. If there is loss of cooperation outside of people known personally, it could mean a return to foraging, or ~99.9% mortality if we returned to the last time we were all hunter-gatherers. But it could be worse than this given the people initially would not be very good foragers, the climate would be worse, and we could cause a lot of extinctions during the collapse. The very worst case scenario is if there is insufficient food, if it were divided equally, everyone would starve to death.
I must note that, under the above assumptions, activities related to resilient food solutions would have cost-effectiveness 0, as one would be assuming no adaptations.
I certainly agree that there would be some reduction in human edible food fed to animals and food waste before there will be large-scale deployment of resilient foods. But what I’m arguing is that the baseline expected mortality without significant preparation on resilient foods could be 25% because of a combination of factors listed above. Furthermore, I think that preparation involving planning and piloting of resilient foods would make it less likely that we fall into some of the terrible situations above.
In general, I do not think it is obvious whether the cost-effectiveness of decreasing famine deaths due to the climatic effects at the margin increases/decreases with mortality. The cost-effectiveness of saving lives is negligible for negligible mortality and sufficiently high mortality, and my model assumes cost-effectiveness increases linearly with mortality, but I wonder what is the death rate for which cost-effectiveness is maximum.
As above, even if the baseline expectation were extinction, there could be high cost effectiveness of saving lives from resilient foods by shifting us away from that scenario, so I disagree with “The cost-effectiveness of saving lives is negligible for … sufficiently high mortality.”
For arguably more reasonable assumptions of 50 % loss of international food trade, and 50 % of it being caused by the climatic effects, linearly interpolating, the increase in the death rate would be 25 % (= 0.5^2). So the new death rate would be 5.67 % (= 0.0443 + (0.0940 − 0.0443)*0.25), i.e. 1.28 (= 0.0567/0.0443) times my value.
Half of the impact of the total loss of international food trade would cause 2.6% to die according to Xia 2022. So why is it not 4.43%+2.6% = 7.0% mortality?
It is still the case that I would get a negative death rate inputting 5 Tg into my formula. However, I am linearly interpolating, and the formula is only supposed to work for a mean stratospheric soot until the end of year 2 between 14.6 and 24.6 Tg, which excludes 5 Tg. I am approximating the logistic function describing the famine deaths due to the climatic effects as being null up to an injection of soot into the stratosphere of 11.3 Tg.
I see how you avoid the negative death rate by not considering 5 Tg. However, this does not address the issue that your comparison is not fair, which is exposed by the fact that if you did put in 5 Tg, you would get negative death rate.
So my interpretation is that the blue line corresponds to no livestock grain fed to humans and current food waste (in 2010), but without international food trade.
I think that is a reasonable assumption, as then the mortality due to 5 Tg alone (no trade in both cases) is ~2% (not a reduction in mortality).
Ideally, instead of adjusting the top line of Figure 5b to include international food trade, I would rely on scenarios accounting for both climatic effects and no loss of international food trade, but Xia 2022 does not present results for that.
One logically consistent way of doing it would be taking the difference between the blue and dark red lines, because they are comparable scenarios. I agree that no reduction in waste or food fed to animals is too pessimistic, but maybe you could do sensitivity on the scenario? Because even though I think that particular scenario is unlikely, I do think that cascading risks including loss of much of nonfood trade could very well increase mortality to these levels.
I am very open to different views about the famine death rate due to the climatic effects of a large nuclear war. My 95th percentile is 702 times my 5th percentile.
That is true, but if you had significant probability mass on the scenarios where people react very suboptimally, then your mean mortality would be a lot higher.
In that case, I would only be overestimating the amount of soot by 10 %, which is a small factor in the context of the large uncertainty involved (my 95th percentile famine deaths due to the climatic effects is 62.3 times my best guess).
Do you mean underestimating? I agree that it’s not that large of an effect.
For reference, maintaining my famine deaths due to climatic effects negligible up to an injection of soot into the stratosphere of 11.3 Tg, if I had assumed a total loss of international food trade fully caused by the climatic effects, I would have obtained a famine death rate due to the climatic effects of a large nuclear war of 5.78 % (= 1 - (0.993 + (0.902 − 0.993)/(24.6 − 14.6)*(14.5 − 14.6))*0.948), i.e. 1.30 (= 0.0578/0.0443) times my value of 4.43 %. For arguably more reasonable assumptions of 50 % loss of international food trade, and 50 % of it being caused by the climatic effects, linearly interpolating, the increase in the death rate would be 25 % (= 0.5^2). So the new death rate would be 4.77 % (= 0.0443 + (0.0578 − 0.0443)*0.25), i.e. 1.08 (= 0.0477/0.0443) times my value.
The total loss of international food trade would cause 5.2% of all die in Xia 2022. So it seems like attributing this all to the climactic effects would increase your death rate by 5.2 percentage points. But digging in deeper, since you are using the gray dotted line in figure 5B corresponding to no human edible food fed to animals and zero waste, if you plugged in a value of 5 Tg, you would say that that amount of soot would actually decrease mortality relative to no food trade and 0 Tg. So clearly that no trade case is not the scenario of no human edible food fed to animals and zero waste (I couldn’t find quickly what exactly their assumptions were for that case). I understand that you are picking the no human edible food fed animals and zero waste scenario because you think other factors would compensate for this optimism. But I think it is particularly inappropriate for the relatively small amounts of Tg.
I thought this was comprehensive, and it was clever how you avoided doing a Monte Carlo simulation for most of the variables. The expected amount of soot to the stratosphere was similar to my and Luisa’s numbers for a large-scale nuclear war. So the main discrepancies are the expected number of fatalities and the impact on the long-term future.
From Figure 4 of Wagman 2020, the soot injected into the stratosphere for an available fuel per area of 5 g/cm^2 is negligible[14].
At 5 g/cm^2, Still most of soot makes it into the upper troposphere, so I think much of that would eventually go to the stratosphere. Furthermore, forest fires are typically less than 5 g/cm^2, and they are moving front fires rather than firestorms, and yet still some of the soot makes it into the stratosphere. In addition, some counter value targets would be in cities with higher g/cm^2. Since you found the counterforce detonations were ~4x as numerous, 1⁄7 the fuel loading, and if the soot to stratosphere percent was 1/3x, that would be ~20% as much soot to stratosphere as the countervalue.
From Fig. 5b of Xia 2022, for the case in which there is no international food trade, all livestock grain is fed to humans, and there is no food waste (top line), adjusted to include international food trade dividing by 94.8 % food support for no international food trade nor climatic effects, there are no deaths for 10.5 Tg[39]. I guess the societal response will have an effect equivalent to assuming international food trade, all livestock grain being fed to humans, and no food waste (see next section), so I supposed the famine deaths due to the climatic effects are negligible up to the climate change induced by 10.5 Tg of soot being injected into the stratosphere in Xia 2022.…
Nevertheless, I am not trying to estimate all famine deaths. I am only attempting to arrive at the famine deaths due to the climatic effects, not those resulting directly or indirectly from infrastructure destruction. I expect this will cause substantial disruptions to international food trade.
I do think there will be significant disruptions in trade due to the infrastructure destruction. But I also think perhaps the majority of the disruption to food trade in particular would be due to the climate impacts on the nontarget countries, which is the majority of the food production. Furthermore, the climate impacts make the overall catastrophe significantly worse, so I think they will increase the chances significantly of the loss of nearly all trade (not just food). This is a major reason why I expect significantly higher mortality due to climate impacts.
This is because Toon 2008:
Assumed regions were targeted in decreasing order of population [and therefore soot injected into the stratosphere] within 5.25 km of ground zero
I do not endorse this assumption.
Why do you not endorse this for countervalue targeting?
Mitigating starvation after a population loss of 50 % does not seem that different from saving a life now, and I estimate a probability of 3.29*10^-6 of such a loss due to the climatic effects of nuclear war before 2050[58].
Your model of the long-term future impact does not incorporate potential cascading impacts associated with catastrophes, which is why you find the marginal value of saving a life in a catastrophe not very different than saving a single life with mosquito bed nets. This is probably the largest crux. With the potential for collapse of nearly all trade (not just food), I think there is potential for collapse of civilization, from which we may not recover. But even if there is not collapse of civilization, I think there’s a significant chance that worse values end up in AGI.
Nonetheless, I believe it would be a surprising and suspicious convergence if broadly decreasing starvation due to the climatic effects of nuclear war was among the most cost-effective interventions to increase democracy levels, or positively shape the development of transformative artificial intelligence (TAI).I think there is a high correlation between saving lives in a catastrophe and improving the long run future. This is probably clearest in the case of reducing the probability of collapse of civilization. Though resilient foods have a longer causal chain to democracy than working directly on democracy, resilient foods are many orders of magnitude more neglected, so it seems at least plausible to me. As for TAI, resilient foods are still orders of magnitude more neglected, which is why my paper indicates they likely have higher long-term cost effectiveness compared to direct work on TAI (or competitive even if one reduced the cost effectiveness of resilient foods by 3 orders of magnitude).
Another factor is that if countries are aware of the potential of scaling up resilient foods, they would be less likely to restrict trade. Therefore, I’m thinking the outcomes might be fairly bimodal, with one scenario of resilient food production and continued trade, and another scenario of not having resilient food production and loss of trade, potentially more than just food trade, perhaps with loss of industrial civilization or worse.
I think this assumes a scenario where, after the asteroid that causes human extinction, the next billion years are large asteroid/comet free, which is not a good assumption.