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Kuhlemann argues that human overpopulation is the best example of an “unsexy” global catastrophic risk, but this is not taken seriously by the vast majority of global catastrophic risk scholars.
I think the reason overpopulation is generally not taken seriously by the GCR community is that they don't believe it would be catastrophic. Some believe that there would be a small reduction in per capita income, but greater total utility. Others argue that having more population would actually raise per capita income and could be key to maintaining long-term innovation.
This is a tricky thing to define, because by some definitions we are already in the 5 year count-down on a slow takeoff.
Some people advocate for using GDP, so the beginning is if you can see the AI signal in the noise (which we can't yet).
Nuclear triad aside, there's the fact that the Arctic is more than 1000 miles away from the nearest US land (about 1700 miles away from Montana, 3000 miles away from Texas), that Siberia is already roughly as close.
Well, there’s Alaska, but yes, part of Russia is only ~55 miles away from Alaska, so the overall point stands that Russia having a greater presence in the Arctic doesn't change things very much.
And of course, the fact the Arctic is made of, well, ice, that melts more and more as the climate warms, and thus not the best place to build a missile base on.
That’s not what is being proposed - it is building more bases in ports on the land where the water doesn’t freeze as much because of climate change.
If negative effects are worse than expected, it can't be reversed.
I agree that MCB can be reversed faster, but still being able to reverse in a few years is pretty responsive. There are strong interactions with other GCRs. For instance, here's a paper that argues that if we have a catastrophe like an extreme pandemic that disrupts our ability to do solar radiation management (SRM), then we could have a double catastrophe of rapid warming and the pandemic. So this would push towards more long-term SRM, such as space systems. However, there are also interactions with abrupt sunlight reduction scenarios such as nuclear winter. In this case, we would want to be able to turn off the cooling quickly. And having SRM that can be turned off quickly in the case of nuclear winter could make us more resilient to nuclear winter than just reducing CO2 emissions.
What about Wait But Why?
Nice summary! My subjective experience participating as an expert was that I was able to convince quite a few people to update towards greater risk by giving them some considerations that they had not thought of (and also by clearing up misinterpretations of the questions). But I guess in the scheme of things, it was not that much overall change.
What I wanted was a way to quantify what fraction of human cognition has been superseded by the most general-purpose AI at any given time. My impression is that that has risen from under 1% a decade ago, to somewhere around 10% in 2022, with a growth rate that looks faster than linear. I've failed so far at translating those impressions into solid evidence.
This is similar to my question of what percent of tasks AI is superhuman at. Then I was thinking if we have some idea what percent of tasks AI will become superhuman at in the next generation (e.g. GPT5), and how many tasks the AI would need to be superhuman at in order to take over the world, we might be able to get some estimate of the risk of the next generation.
I agree that indoor combustion producing small particles that go deep into the lungs is a major problem, and there should be prevention/mitigation. But on the dust specifically, I was hoping to see a cost-benefit analysis. Since most household dust is composed of relatively large particles, they typically do not penetrate beyond the nose and throat, and so are more of an annoyance than something that threatens your life. So I am skeptical if one doesn’t have particular risk factors such as peeling lead paint or allergies, measures such as regular dusting (how frequently are you recommending?), not wearing shoes in the house, having hardwood floors if you like the benefits of carpet such as sound absorption, etc would be cost-effective when you value people’s time.
Recall that GPT2030 could do 1.8 million years of work[8] across parallel copies, where each copy is run at 5x human speed. This means we could simulate 1.8 million agents working for a year each in 2.4 months.
You point out that human intervention might be required every few hours, but with different time zones, we could at least have the GPT working twice as many hours a week as humans, so that would imply ~1 month above. As for the speed now, you say about the same to three times as fast for thinking. You point out that it also does writing, but it is verbose. However, for solving problems like that coding interview, it does appear to be an order of magnitude faster already (and this is my experience solving physical engineering problems).
AI having scope-sensitive preferences for which not killing humans is a meaningful cost
Could you say more what you mean? If the AI has no discount rate, leaving Earth to the humans may require within a few orders of magnitude 1/trillion kindness. However, if the AI does have a significant discount rate, then delays could be costly to it. Still, the AI could make much more progress in building a Dyson swarm from the moon/Mercury/asteroids with their lower gravity and no atmosphere, allowing the AI to launch material very quickly. My very rough estimate indicates sparing Earth might only delay the AI a month from taking over the universe. That could require a lot of kindness if they have very high discount rates. So maybe training should emphasize the superiority of low discount rates?
I think "50% you die" is more motivating to people than "90% you die" because in the former, people are likely to be able to increase the absolute chance of survival more, because at 90%, extinction is overdetermined.
When asked on Lex’s podcast to give advice to high school students, Elezier’s response was “don’t expect to live long.”
Not to belittle the perceived risk if one believes in 90% chance of doom in the next decade, but even if one has a 1% chance of an indefinite lifespan, the expected lifespan of teenagers now is much higher than previous generations.
Right, both ChatGPT and Bing chat recognize it as a riddle/joke. So I don't think this is correct:
If you ask GPT- "what's brown and sticky?", then it will reply "a stick", even though a stick isn't actually sticky.
Very useful post and discussion! Let's ignore the issue that someone in capabilities research might be underestimating the risk and assume they have appropriately assessed the risk. Let's also simplify to two outcomes of bliss expanding in our lightcone and extinction (no value). Let's also assume that very low values of risk are possible but we have to wait a long time. It would be very interesting to me to hear how different people (maybe with a poll) would want the probability of extinction to be below before activating the AGI. Below are my super rough guesses:
1x10^-10: strong longtermist
1x10^-5: weak longtermist
1x10^-2 = 1%: average person (values a few centuries?)
1x10^-1 = 10%: person affecting: currently alive people will get to live indefinitely if successful
30%: selfish researcher
90%: fame/power loving older selfish researcher
I was surprised that my estimate was not more different for a selfish person. With climate change, if an altruistic person affecting individual thinks the carbon tax should be $100 per ton carbon, a selfish person should act as if the carbon tax is about 10 billion times lower, so ten orders of magnitude different versus ~one order for AGI. So it appears that AGI is a different case in that the risk is more internalized to the actors. Most of the variance for AGI appears to be from how longtermist one is vs whether one is selfish or altruistic.
Denkenberger posted two papers he wrote in regards to a 150Tg nuclear exchange scenario (worst case scenario, total targeting of cities). As far as I can tell, although the developed world doesn't come close to famine and there is theoretically enough food to feed everyone on Earth
To clarify, the world would have enough food if trade continues and if we massively scale up resilient foods. Trade continuing is very uncertain, and making it likely that we scale up resilient foods would require significantly more planning and piloting.
For the one paper, it is too early to tell. For the other, there just has not been very much engagement. Mainly the public debate has been between the Robock team, which is highly confident that full-scale nuclear war would cause nuclear winter, and the Los Alamos team, which is highly confident that full-scale nuclear war would not cause nuclear winter. We find the truth is likely somewhere in between. I talked about this in one of my 80k podcasts. Our analysis is quite similar to Luisa Rodriguez' analysis that cubefox links to below.
Thanks, Peter. That draft assumes global cooperation, which is likely too optimistic, so we have submitted another draft that also analyzes the case of breakdown of trade (hopefully public soon). We also have this paper that looks at the US specifically and takes into account food storage (and uncertainty of whether nuclear war would result in nuclear winter).
Great post! I've been mentioning for years that volunteering can be an effective way of making a contribution. Though many people think of volunteering as for a specific organization, I don't think it has to be, so a hobby could be an example. I think there are not enough volunteer opportunities in EA, and we've worked hard at ALLFED on our volunteer program. Not only have we had dozens of volunteers skill up, but they have also made significant contributions, often co-authoring journal articles and becoming full time staff. Thanks for the shout out! I'm actually still volunteering for ALLFED (and donating).
I'm probably a bit more concerned about monkeypox than you are, mainly because it has an alarmingly long incubation period (up to 14 days) and then a punishingly long infectious period (3-4 weeks).
So with doubling every 10.5 days, that would seem to mean a high R0 - what's your estimate? And really because some people are still being cautious about COVID, the true R0 (with normal behavior) would be even higher than what is measured now.
I would say that is basically right. AC exhaust is about as humid as indoor air. The fraction of the heating load in the summer due to infiltration really does depend on how tight your building construction is. With the numbers Jeff was assuming for a very old house, infiltration would be a much larger percentage. There are some other sources of heat in a house that come with humidity, such as people and showers, but overall it is much less humidity than bringing in outdoor air (there is heat conduction through the walls, electricity use of lighting and appliances, etc.). So that might mean that it would take you from a 25% efficiency loss (ignoring humidity) up to a 35% efficiency loss, which is still a big deal. But I'm not sure if 85°F in California typically corresponds to 50% relative humidity.
If you want to geek out on this you can use a psychrometric chart. For instance, if outdoor air is 85F and 50% relative humidity (RH), that's an enthalpy of about 35 BTU/lb of dry air. Typical exit air conditions on the cool side of an air conditioner are ~50F and 100% RH, so ~20 BTU/lb of dry air. The dehumidification portion would be going to 85F and ~30% RH or ~29 BTU/lb of dry air, so ~40% of the heat removed is in the form of condensing water (latent). This means you would take the sensible part and multiply by about 1.7 to get the total load on the air conditioner. If you were not drawing in outdoor air, the latent load would be much lower. So overall I think you're right that in CA the humidity correction is not as big as the other factors.
The thermal time constant of a building is around a day, so you should really be running each of these tests for more than a day (and correcting for differences in ambient conditions). Basically, the control should exceed the average ambient temp because of solar and internal (e.g. electricity consumption) gains. And see my other comment about doing something about humidity removal. Then we might actually have something rigorous (based on doing an experiment with fairly expensive equipment, I still had error bars around +/-1°C, so I don't think you have very much confidence at this point).
I must admit I was surprised by the statistics here. It is true if you only use the air conditioner few days a year, the energy efficiency is not important. However, the cooling capacity is important. I think many people are using efficiency to mean cooling capacity above. Anyway, let's say the incremental cost of going from one hose to two hoses is $30. From working on Department of Energy energy efficiency rules, typically the marginal markup of an efficient product is less than the markup on the product overall (meaning that the incremental cost of just adding a hose is less than the $20 of buying it separately). It is true that with a smaller area for the air to come into the device with a hose, the velocity has to be higher, so the fan blades need to be made bigger (it typically is one motor powering two different fan blades on two sides, at least for window units). But then you could save money on the housing because the port is smaller. The incremental cost of motors is low. Then if the air conditioner cost $200 to start with, that would be 15% incremental cost. Then let's say the cooling capacity increased by 25% (I would say it actually does matter that a T-shirt was used, which would allow room area and instead of just outdoor air, so it probably would be higher than this). What this means is that the two hose actually has greater cooling capacity per dollar, so you should choose a small two hose even if you don't care about energy use at all. Strictly this is only true with no economies of scale, which is not a great assumption. But I think overall it will hold. Another case this would break down is if a person were plugging and unplugging many times, but I don't think that's the typical person. So I suspect what is going on is that people don't realize that the cooling capacity of the one hose is actually reduced more than the cost, so they should just be getting a smaller capacity two hose unit (at lower initial cost and energy cost).
There is a broader question here of whether there should be energy efficiency regulations. If people were perfectly rational and had perfect information, we would not need them. But not only are the incremental costs of energy efficiency regulations found to be economically beneficial by the US Department of Energy (basically a good return on investment), but a retrospective study found that the actual incremental cost of meeting the efficiency regulations was about an order of magnitude lower than predicted by the Department of Energy! So I think there's a very strong case for energy efficiency regulations.
I overlooked a crucial consideration raised by denkenberger here that reduces the efficiency loss ~2x.
Thanks-it looks like you are referring to the net infiltration flow rate impact on the building. But there was also the consideration of humidity, and I did not see any humidity measurements in the data, so we are not able to resolve that one. Humidity sensors are fairly cheap, but notoriously unreliable. But one could actually measure the amount of water condensed pretty accurately to get an idea how much of the cooling of the air conditioner is going to condensing water versus cooling air (sensibly).
What is your estimate of the Metaculus question "Will there be a positive transition to a world with radically smarter-than-human artificial intelligence?" It sounds like it is much lower than the community prediction of 55%. Do you think this is because the community has significant probability mass on CAIS, ems, or Paul-like scenarios? What probability mass do you put on those (and are there others)?
Yes, 0.35 ACH is for the whole house. Most houses do not have active ventilation systems, so that's all you would get for the bedroom. But that is true that if you are worried about CO2, you should have higher ACH in bedrooms. But this recommendation is not just about CO2, but also things like formaldehyde. Also it is roughly the amount that houses get on average. I have seen studies showing that the cost of sick building syndrome is well worth having higher ventilation rates. So probably more houses should have active ventilation. But if you don't have active ventilation in a house, I think 0.35 ACH is a reasonable average. Apartment buildings will have active ventilation and higher occupant density, so the ACH will generally be higher, as you point out.
Yes - it is quite leaky - the rule of thumb the American Society of Heating, Refrigerating and Air Conditioning Engineers for low rise residential is more like 0.3 ACH. This would make your filtration look a lot better.
The infiltration factor of a well-functioning woodstove is far less than a one hose air conditioner, because the air is heated to much higher temperatures. However, it can be significant for fireplaces.
I studied the impact of infiltration because of clothes dryers when I was doing energy efficiency consulting. The nonobvious thing that is missing from this discussion is that the infiltration flow rate does not equal the flow rate of the hot air out the window. Basically absent the exhaust flow, there is an equilibrium of infiltration through the cracks in the building equaling the exfiltration through the cracks in the building. When you have a depressurization, this increases the infiltration but also decreases the exfiltration. If the exhaust flow is a small fraction of the initial infiltration, the net impact on infiltration is approximately half as much as the exhaust flow. The rule of thumb for infiltration is it produces about 0.3 air changes per hour, but it depends on the temperature difference to the outside and the wind (and the leakiness of the building). I would guess that if you did this in a house, the exhaust flow would be relatively small compared to the natural infiltration. So roughly the impact due to the infiltration is about half as much as the calculations indicate. But if you were in a tiny tight house, then the exhaust flow would overwhelm the natural infiltration and the increase in infiltration would be close to the exhaust flow.
Another factor is the dehumidification load on the air conditioner. This is a really big deal in the southeastern US, though it would be less of a deal in the Bay Area. Basically, if it is very humid outside, the additional infiltration air has to be de-humidified, and that can double how much heat the air conditioner needs to remove from the infiltration air. So this could counteract the benefit of the net infiltration being smaller than the exhaust flow.
The exhaust temperature of 130°F sounds high to me for regular air conditioner, but heat pumps designed to heat hot water and dry clothing to go even higher than that. So it is possible they increase it more than a regular air conditioner to increase the overall efficiency (because the fan energy is significantly larger with the hose as compared to a typical window unit). Still, I am confident that the reduction in efficiency of one hose versus two hose is less than 50% unless it is very hot and humid outside.
Portable units have to meet a much weaker standard. I actually pushed for a more stringent standard on these products when I was consulting for the Appliance Standards Awareness Project.
Zvi has now put a postscript in the ALLFED section above. We have updated the inadvertent nuclear war fault tree model result based on no nuclear war since the data stopped coming in, and also reduced the annual probability of nuclear war further going forward. And then, so as to not over claim on cost effectiveness, we did not include a correction for non-inadvertent US/Russia nuclear war nor conflict with China. Resilient foods are still highly competitive with AGI safety according to the revised model.
Remember the things that ALL have to be true for a "nuclear winter" to happen at all. I'm not gonna say it's a completely debunked myth, but to me the probability is clearly low enough that I mostly ignore it in my planning.
It is conjunctive, but I've run probability distributions in a Monte Carlo model in a journal article and got about 20% chance of agricultural collapse given full scale nuclear war. So I think it is important for planning, as the consequences are far larger than the direct effects.
Sure looks like we’re past the peak [in South Africa], and the peak was remarkably low there, so low that it doesn’t make sense. Why would behaviors adjust this much this fast for so few cases, which were on average much milder?
You can see Google mobility data here at the bottom, and indeed, the response to this wave is much smaller than other waves.
I believe your calculation was 70% chance of not having it given a negative test, so if you have two independent negative tests, that would be 91% chance of not having it (1 - 0.09), or 9% chance of having it. But in reality, false negatives are very common. And you need to start with a prior probability to update from. From the paper I referenced, if you have some symptoms and were exposed, the prior probability of having COVID might be 91%, but after one negative result, you are still at 77-80% probability of having COVID. However, if your symptoms don’t match the common ones for COVID or if you don’t know you were exposed, then the prior probability of having COVID is much lower to start with. Then a negative test result would update downward slightly from that prior.
*If the rapid test had some probability of success, like 70%, then if you took two test you might figure 1-(1-.7)^2 = 1-.3^2 = 1-.09 = 01% you have covid. But are the rapid tests independent?**
You need to start with a prior for this calculation. This paper also discusses independence of tests. And I think you meant to write 91%.
The substantive complaint was that they [ALLFED] did an invalid calculation when calculating the annual probability of nuclear war. They did a survey to establish a range of probabilities, then they averaged them. One could argue about what kinds of ‘average them’ moves work for the first year, but over time the lack of a nuclear war is Bayesian evidence in favor of lower probabilities and against higher probabilities. It’s incorrect to not adjust for this, and the complaint was not merely the error, but that the error was pointed out and not corrected.
Tl; dr: ALLFED appreciates the feedback. We disagree that it was a mistake - there were smart people on both sides of this issue. Good epistemics are very important to ALLFED.
Full version:
Zvi is investigating the issue. I won’t name names, but suffice it to say, there were smart people disagreeing on this issue. We have been citing the fault tree analysis of the probability of nuclear war, which we think is the most rigorous study because it uses actual data. Someone did suggest that we should update the probability estimate based on the fact that nuclear war has not yet occurred (excluding World War II). Taking a look at the paper itself (see the top of page 9 and equation (5) on that page), for conditional probabilities of occurrence for which effectively zero historical occurrences have been observed out of n total cases when it could have occurred, the probability in the model was updated according to a Bayesian posterior distribution with a uniform prior and binomial likelihood function. Historical occurrences updated in this way were A) the conditional probability that Threat Assessment Conference (TAC)-level attack indicators will be promoted to a Missile Attack Conference (MAC), and (B) the conditional probability of leaders’ decision to launch in response to mistaken MAC-level indicators of being under attack. Based on this methodology, it would be double-counting to update their final distribution further based on the historical absence of accidental nuclear launches over the last 76 years.
But what we do agree on is that if one starts with a high prior, one should update. And that's what was done by one of our coauthors for his model of the probability of nuclear war, and he got similar results to the fault tree analysis. Furthermore, the fault tree analysis was only for inadvertent nuclear war (one side thinking they are being attacked, and then "retaliating"). However, there are other mechanisms for nuclear war, including intentional attack, and accidental detonation of a nuclear weapon and escalation from there. Furthermore, though many people consider nuclear winter only possible for a US-Russia nuclear war, now that China has a greater purchasing power parity than the US, we think there is comparable combustible material there. So the possibility of US-China nuclear war or Russia-China nuclear war further increases probabilities. So even if there should be some updating downward on the inadvertent US-Russia nuclear war, I think the fault tree analysis still provides a reasonable estimate. I also explained this on my first 80k podcast.
Also, we say in the paper, "Considering uncertainty represented within our models, our result is robust: reverting the conclusion required simultaneously changing the 3-5 most important parameters to the pessimistic ends." So as Zvi has recognized, even if one thinks the probability of nuclear war should be significantly lower, the overall conclusion doesn't change. We have encouraged people to put their own estimates in.
Again, we really appreciate the feedback. Good epistemics are very important to us. We are trying to reach the truth. We want to have maximum positive impact on the world, so that's why we spend a significant amount of time on prioritization.
It's hard to pin down a threshold of a specific time of exposure because it depends on the minimum infectious dose, which varies widely among people, at least for lots of diseases. Also, the rate of shedding varies widely based on the progression of the disease, whether the person is talking, how far away the person is, etc. Furthermore, the HVAC system causes additional variation. So I think when you add all these uncertainties, a 16 times reduction in emission/inhalation would correspond to very roughly a 16 times reduction in infection, but I would be very interested to see if someone has run the math on this.
Great work!
The thing is, at some point it does mean the virus is unstoppable, in the sense that no reasonable or worthwhile attempt to stop it has any chance of success, outside of at most protecting particular vulnerable groups and doing mitigation. If the baseline transmission is higher than Delta and it’s mostly ignoring vaccinations, what is your plan exactly? Lock down much harder than we did in 2020? Close the grocery stores?
Naïve calculation: if surgical masks blocked 75% of aerosols going out and coming in, and if all transmission were through aerosols, and if people wore these masks all the time, this could theoretically overcome R0 = 16 (because the transmission would be reduced a factor of four in both steps). Of course it will not work out this well, but then there are many other measures that can be taken in addition. So I think it would be technically feasible to keep it under control. But if it were to run quickly through most of the population, omicron would have to be much less severe than other variants to not overwhelm hospitals (without massive scale up of treatments).
this gives a paltry annual return on investment of 0.075%
which seems large until we note that it implies an annualized rate of return of 0.08%; far more than our estimate above, but a tiny rate of return.
Am I comparing the right numbers? It doesn’t seem like far more to me.
That was an exciting graph! However, the labeling would be more consistent if it were steam engines, piston engines, and turbine engines OR stationary, ship/barge, train, automobile, and aircraft (I assume you mean airplanes and helicopters and you excluded rockets).
Comparing to other vaccines is helpful. But what about a more outside view of new medical treatments? I'm not sure what the reference class should be, but I think the fact that the mRNA vaccine has never been used before should give us pause.
Maybe the most effective thing would be if there were a vitamin D futures market, to bid up the price to incite more production of it. But at the individual level, I think it makes sense to stock up to increase the price a little bit. If you don't end up needing it, you could always give/sell it to those who do later. The one I bought is good for 1.5 years.
Another interesting piece of evidence is a study on homeless people in Boston (who would likely not be vitamin D deficient because more outdoor time):
"100% of 147 COVID-19 positive subjects were asymptomatic."
Source, which doesn't really make the connection:
Baggett, T. P., Keyes, H., Sporn, N. & Gaeta, J. M. COVID-19 outbreak at a large homeless shelter in
Boston: Implications for universal testing. medRxiv 2020.04.12.20059618 (2020)
doi:10.1101/2020.04.12.20059618.
I have estimated global vitamin D3 production to be a few tons per year, so at US RDA of 600 UI, we could only provide about 3% of the global population. At your suggestion of 5000 UI/day, it would only be about 0.3% of people. This is why I looked into quickly scaling up vitamin D production. The most promising appeared to be seaweed, but we could not get anyone excited about doing it before there was a shortage. Fortunately, just mega dosing of those testing positive appears to be within our global D3 production capability at current infection rate. However, if we let it run through the population, I don't think we would have sufficient supplies at current production.
Note that that statistic is how long people have been in their current job, not how long they will stay in their current job total. If everyone stayed in their jobs for 40 years, and you did a survey of how long people have been in their job, the median will come out to 20 years. I have not found hard data for the number we actually want, but this indicates that the median time that people stay in their jobs is about eight years, though it would be slightly shorter for younger people.
I like your succinct way of restating the case for spending some money on catastrophes other than AI.
It is possible that a loss of industry could be beneficial in the long term. One can adjust the moral hazard parameter to take into account this possibility. However, it does subject us to more natural risk like supervolcanic eruptions and asteroid/comet impacts. And if we actually lost anthropological civilization, we would not be doing any AI safety work. Even just losing industry for a long time I think would make most AI safety work not feasible, but I am interested in your thoughts. Without industry, we would not be able to afford nearly as many researchers. And they would just be doing math on paper.
This could potentially help many decades in the future. But it would need to be an order of magnitude or more reduction in energy costs for this to produce a lot of food. And I am particularly concerned about one of these catastrophes happening in the next decade.
Grains are all from the same family-grass. It is conceivable that a malicious actor could design a pathogen(s) that kills all grains. Or maybe it would become an endemic disease that would decrease the vigor of the plants permanently. I'm not arguing that any of these non-recovery scenarios are too likely. However, if together they represent 10% probability, and if there is a 10% probability of the sun being blocked this century, and a 10% probability of civilization collapsing if the sun is blocked, this would be a one in 1000 chance of an existential catastrophe from agricultural catastrophes this century. This is worth some effort to reduce.
Sorry for my voice recognition software error-I now have fixed it. It turns out that if you want to store enough food to feed 7 billion people for five years, it would cost tens of trillions of dollars. What I am proposing is spending tens of millions of dollars for targeted research and development and planning. The idea is that we would not have to spend a lot of money on emergency use only machinery. I use the example of the United States before World War II-it hardly produced any airplanes. But once it entered World War II, it retrofitted the car manufacturing plants to produce airplanes very quickly. I am targeting food sources that could be ramped up very quickly with not very much preparation (in months, see graph here. The easiest killed leaves (for human food) to collect would be agricultural residues with existing farm equipment. For leaves shed naturally (leaf litter), we could release cows into forests. I also analyze logistics in the book, and it would be technically feasible. Note that these catastrophes would only destroy regional infrastructure. However, the big assumption is that there would still be international cooperation. Without these alternative food sources, most people would die, so it would likely be in the best interest of many countries to initiate conflicts. However, if countries knew that they could actually benefit by cooperating and trading and ideally feed everyone, cooperation is more likely (though of course not guaranteed). So you could think of this as a peace project.
In the case of the sun being blocked by comet impact, super volcanic eruption, or full-scale nuclear war with the burning of cities, there would be local devastation, but the majority of global industry would function. Most of our energy is not dependent on the sun. So it turns out the biggest problem is food, and arable land would not be valuable. Extracting human edible calories from leaves would only work for those leaves that were green when the catastrophe happened. They could provide about half a year of food for everyone, or more realistically 10% of food for five years.
I also work on the catastrophes that could disrupt electricity globally, such as an extreme solar storm, multiple high-altitude detonations of nuclear weapons around the world creating electromagnetic pulses (EMPs), and a super computer virus. Since nearly everything is dependent on electricity, this means we lose fossil fuel production and industry. In this case, energy is critical, but there are ways of dealing with it. So the food problem still turns out to be quite important (the sun is still shining, but we don't have fossil fuel based tractors, fertilizers and pesticides), though there are solutions for that.