Vasco Grilo

• Vasco Grilo 30 Apr 2024 18:44 UTC

  2 points

  0 ∶ 0

  on: doebem: Cause Pri­ori­ti­za­tion Re­search in Brazil (2023/​2024)

  Thanks for sharing, Gisele!

  1. Construction of the Cause Map

  As a starting point, we built a map of potential causes, identifying social problems that are either causes or consequences of poverty and low development in Brazil. This work was done in partnership with the community members of Effective Altruism Brazil. The conclusion of this stage was a map of 59 causes grouped into seven areas: education, employment and income, food insecurity, obesity and malnutrition, housing, basic sanitation, health, and violence and crime.

  Have you considered analysing animal welfare interventions? It is not related to poverty and low development, but Brazil’s rising incomes are resulting in more factory-farming.

  From 2002 to 2022, the number of poultry birds in Brazil increased 78.2 % (= 1.60*10^9/​(898*10^6) − 1).

  From 2000 to 2020, aquaculture production in Brazil increased 266 % (= 630*10^3/​(172*10^3) − 1).

Imi­ta­tion Learn­ing is Prob­a­bly Ex­is­ten­tially Safe

• Vasco Grilo 30 Apr 2024 9:05 UTC

  2 points

  0 ∶ 0

  on: GWWC’s eval­u­a­tions of evaluators

  Hi,

  I appreciate THL has room for more funding. You say in the report on animal charity evaluators that:

  A direct referral from Open Philanthropy’s Farm Animal Welfare team — the largest funder in the impact-focused animal welfare space — on THL indeed currently being funding-constrained, i.e. that it has ample room to cost-effectively use marginal funds on corporate campaigns and that there aren’t strong diminishing returns to providing THL with extra funding.

  However, Open Philanthropy (OP), which granted 8.3 M$ to THL in 2023, presumably wants to fund THL up to a certain point. Other donors donating to THL could simply mean OP has to donate less. So I wonder whether donating to THL has the same effect as donating to OP. If this is so, donating to THL would be equivalent to mostly supporting human welfare interventions. Based on OP’s grants data on 17 February 2024, only 9.98 % of the money granted by OP has gone to animal welfare interventions.

  I believe donations to AWF do not suffer as much from the above. Their grants are much smaller than OP’s.

An­i­mals in Cost-Benefit Analysis

• Vasco Grilo 24 Apr 2024 16:08 UTC

  4 points

  1 ∶ 0

  on: Mo­ti­va­tion gaps: Why so much EA crit­i­cism is hos­tile and lazy

  Great post, titotal!

  Why should a person focus on your issue in particular, rather than

  It looks like you meant to write something after this.

  One way to react to this would be to try and compensate for motivation gaps when evaluating the strengths of different arguments. Like, if you are evaluating a claim, and side A has a hundred full time advocates but side B doesn’t, don’t just weigh up current arguments, weigh up how strong side B’s position would be if they also had a hundred full time advocates. A tough mental exercise!

  Relatedly, there is this post from Nuño Sempere.

  Summary

  This document seeks to outline why I feel uneasy about high existential risk estimates from AGI (e.g., 80% doom by 2070). When I try to verbalize this, I view considerations like

  • selection effects at the level of which arguments are discovered and distributed

  • community epistemic problems, and

  • increased uncertainty due to chains of reasoning with imperfect concepts

  as real and important.

  I still think that existential risk from AGI is important. But I don’t view it as certain or close to certain, and I think that something is going wrong when people see it as all but assured.

• Vasco Grilo 24 Apr 2024 14:14 UTC

  11 points

  1 ∶ 0

  in reply to: Matthew Rendall’s comment on: If You’re Go­ing To Eat An­i­mals, Eat Beef and Dairy

  Thanks for the follow up, Matthew! Strongly upvoted.

  My best guess is also that additional GHG emissions are bad for wild animals, but it has very low resilience, so I do not want to advocate for conservationism. My views on the badness of the factory-farming of birds are much more resilient, so I am happy with people switching from poultry to beef, although I would rather have them switch to plant-based alternatives. Personally, I have been eating plant-based for 5 years.

  Moreover, as Clare Palmer argues

  Just flagging this link seems broken.

  I think you have misinterpreted what my article about discounting is recommending.

  Sorry! It sounded so much like you were referring to Weitzman 1998 that I actually did not open the link. My bad! I have now changed “That paper says one should discount” to “One should discount”.

  a traditional justification for discounting is that if we didn’t, we’d be obliged to invest nearly all our income, since the number of future people could be so great.

  I do not think this is a good argument for discounting. If it turns out we should invest nearly all our income to maximise welfare, then I would support it. In reality, I think the possibility of the number of future people being so great is more than offset by the rapid decay of how much we could affect such people, such that investing nearly all our income is not advisable.

  I argue for discounting damages to those who would be much better off than we are at conventional rates, but giving sizable—even if not equal—weight to damages that would be suffered by everyone else, regardless of how far into the future they exist.

  This rejects (perfect) impartiality, right? I strongly endorse expected total hedonistic utilitarianism, so I would rather maintain impartiality. At the same time, the above seems like a good heuristic for better outcomes even under fully impartial views.

• Vasco Grilo 24 Apr 2024 7:38 UTC

  5 points

  0 ∶ 0

  in reply to: Matthew Rendall’s comment on: If You’re Go­ing To Eat An­i­mals, Eat Beef and Dairy

  Nice points, Matthew!

  (a) It wasn’t clear to me that the estimate of global heating damages was counting global heating damages to non-humans.

  I have now clarified my estimate of the harms of GHG emissions only accounts for humans. I have also added:

  I estimated the scale of the welfare of wild animals is 4.21 M times that of farmed animals. Nonetheless, I have neglected the impact of GHG emissions on wild animals due to their high uncertainty. According to Brian Tomasik:

  “On balance, I’m extremely uncertain about the net impact of climate change on wild-animal suffering; my probabilities are basically 50% net good vs. 50% net bad when just considering animal suffering on Earth in the next few centuries (ignoring side effects on humanity’s very long-term future).”

  In particular, it is unclear whether wild animals have positive/​negative welfare.

  I have added your name to the Acknowledgements. Let me know if you would rather remain anonymous.

  (b) It appears you were working with a study that employed a discount rate of 2%. That’s going to discount damages in 100 years to 13% of their present value, and damages in 200 years to 1.9% of their present value—and it goes downhill from there. But that seems very hard to justify. Discounting is often defended on the ground that our descendants will be richer than we are.

  Carleton 2022 presents results for various discount rates, but I used the ones for their preferred value of 2 %. I have a footnote saying:

  “Our preferred estimates use a discount rate of $\delta$ = 2 %”. This is 1.08 (= 0.02/​0.0185) times the 1.85 % (= (17.5/​9.72)^(1/​(2022 − 1990)) − 1) annual growth rate of the global real GDP per capita from 1990 to 2022. The adequate growth rate may be higher due to transformative AI, or lower owing to stagnation. I did not want to go into these considerations, so I just used Carleton 2022’s mainstream value.

  ---

  But that rationale doesn’t apply to damages in worst-case scenarios. Because they could be so enduring, these damages are huge in expectation.

  I used to think this was relevant, but mostly no longer do:

  • One should discount the future at the lowest possible rate, but it might still be the case that this is not much lower than 2 % (for reasons besides pure time discounting, which I agree should be 0).

  • I believe human extinction due to climate change is astronomically unlikely. I have a footnote with the following. “For donors interested in interventions explicitly targeting existential risk mitigation, I recommend donating to LTFF, which mainly supports AI safety. I guess existential risk from climate change is smaller than that from nuclear war (relatedly), and estimated the nearterm annual risk of human extinction from nuclear war is 5.93*10^-12, whereas I guess that from AI is 10^-6”.

  • I guess human extinction is very unlikely to be an existential catastrophe. “For example, I think there would only be a 0.0513 % (= e^(-10^9/​(132*10^6))) chance of a repetition of the last mass extinction 66 M years ago, the Cretaceous–Paleogene extinction event, to be existential”. You can check the details of the Fermi estimate in the post.

  • If your worldview is such that very unlikely outcomes of climate chance still have meaningful expected value, the same will tend to apply to our treatment of animals. For example, I assume you would have to consider effects on digital minds.

  • I am open to indirect longterm effects dominating the expected value, but I suppose maximising more empirically quantifiable less uncertain effects on welfare is still a great heuristic.

• Vasco Grilo 23 Apr 2024 18:50 UTC

  2 points

  0 ∶ 0

  in reply to: Stan Pinsent’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  Can you give an example of what might count as “spending to save lives in wars 1k times as deadly” in this context?

  For example, if one was comparing wars involding 10 k or 10 M deaths, the latter would be more likely to involve multiple great power, in which case it would make more sense to improve relationships between NATO, China and Russia.

  Thinking about the amounts we might be willing to spend on interventions that save lives in 100-death wars vs 100k-death wars, it intuitively feels like 251x is a way better multiplier than 63,000. So where am I going wrong?

  You may be right! Interventions to decrease war deaths may be better conceptualised as preventing deaths within a given severity range, in which case I should not have interpreted lirerally the example in Founders Pledge’s report Philanthropy to the Right of Boom. In general, I think one has to rely on cost-effectiveness analyses to decide what to prioritise.

  When you are thinking about the PDF of $\frac{P_i}{P_f}$, are you forgetting that ∇$\frac{P_i}{P_f}$ is not proportional to ∇$P_f$?

  I am not sure I got the question. In my discussion of Founders Pledge’s example about war deaths, I assumed the value of saving one life to be the same regardless of population size, because this is what they were doing). So I did not use the ratio between the initial and population.

• Vasco Grilo 23 Apr 2024 13:26 UTC

  26 points

  3 ∶ 2

  in reply to: jackva’s comment on: If You’re Go­ing To Eat An­i­mals, Eat Beef and Dairy

  Thanks for tagging me, Johannes! I have not read the post, but in my mind one should overwhelmingly focus on minimising animal suffering in the context of food consumption. I estimate the harm caused to farmed animals by the annual food consumption of a random person is 159 times that caused to humans by their annual GHG emissions.

  Fig. 4 of Kuruc 2023 is relevant to the question. A welfare weight of 0.05 means that one values 0.05 units of welfare in humans as much as 1 unit of welfare in animals, and it would still require a social cost of carbon of over 7 k$/​t for prioritising beef reductions over poultry reductions, whereas United States Environmental Protection Agency (EPA) proposes one of 190 $/​t. If one values 1 unit of welfare the same regardless of species (i.e. if one rejects speciesism), there is basically no way it makes sense to go from beef to poultry (ignoring effects on wild animals; see discussion below).

  

• Vasco Grilo 22 Apr 2024 17:20 UTC

  3 points

  0 ∶ 0

  in reply to: Stan Pinsent’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  Thanks for the comment, Stan!

  Using PDF rather than CDF to compare the cost-effectiveness of preventing events of different magnitudes here seems off.

  Technically speaking, the way I modelled the cost-effectiveness:

  • I am not comparing the cost-effectiveness of preventing events of different magnitudes.

  • Instead, I am comparing the cost-effectiveness of saving lives in periods of different population losses.

  Using the CDF makes sense for the former, but the PDF is adequate for the latter.

  You show that preventing (say) all potential wars next year with a death toll of 100 is 1000^1.6 = 63,000 times better in expectation than preventing all potential wars with a death toll of 100k.

  I agree the above follows from using my tail index of 1.6. It is just worth noting that the wars have to involve exactly, not at least, 100 and 100 k deaths for the above to be correct.

  More realistically, intervention A might decrease the probability of wars of magnitude 10-100 deaths and intervention B might decrease the probability of wars of magnitude 100,000 to 1,000,000 deaths. Suppose they decrease the probability of such wars over the next n years by the same amount. Which intervention is more valuable? We would use the same methodology as you did except we would use the CDF instead of the PDF. Intervention A would be only 1000^0.6 = 63 times as valuable.

  This is not quite correct. The expected deaths from wars with $d_1$ to $d_2$ deaths is ${\int }_{d_1}^{d_2}x\frac{\alpha {d_m}^{\alpha }}{x^{\alpha +1}}dx=\frac{\alpha {d_m}^{\alpha }({m_1}^{1-\alpha }-{m_2}^{1-\alpha })}{\alpha -1}$, where ${d_m}^{\alpha }$ are the minimum war deaths. So, for a tail index of $\alpha =1.6$, intervention A would be 251 (= (10^-0.6 − 100^-0.6)/​((10^5)^-0.6 - (10^6)^-0.6)) times as cost-effective as B. As the upper bounds of the severity ranges of A and B get increasingly close to their lower bounds, the cost-effectiveness of A tends to 63 k times that of B. In any case, the qualitative conclusion is the same. Preventing smaller wars averts more deaths in expectation assuming war deaths follow a power law.

  As an intuition pump we might look at the distribution of military deaths in the 20th century. Should the League of Nations/​UN have spent more effort preventing small wars and less effort preventing large ones?

  I do not know. Instead of relying on past deaths alone, I would rather use cost-effectiveness analyses to figure out what is more cost-effective, as the Centre for Exploratory Altruism Research (CEARCH) does. I just think it is misleading to directly compare the scale of different events without accounting for their likelihood, as in the example from Founders Pledge’s report Philanthropy to the Right of Boom I mention in the post.

  When it comes to things that could be even deadlier than WWII, like nuclear war or a pandemic, it’s obvious to me that the uncertainty about the death toll of such events increases at least linearly with the expected toll, and hence the “100-1000 vs 100k-1M” framing is superior to the PDF approach.

  I am also quite uncertain about the death toll of catastrophic events! I used the PDF to remain consistent which Founders Pledge’s example, which compared discrete death tolls (not ranges).

• Vasco Grilo 21 Apr 2024 10:32 UTC

  2 points

  0 ∶ 0

  in reply to: Owen Cotton-Barratt’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  By “pre- and post-catastrophe population”, I meant the population at the start and end of a period of 1 year, which I now also refer to as the initial and final population.

  I guess you are thinking that the period of 1 year I mention above is one over which there is a catastrophe, i.e. a large reduction in population. However, I meant a random unconditioned year. I have now updated “period of 1 year” to “any period of 1 year (e.g. a calendar year)”. Population has been growing, so my ratio between the initial and final population will have a high chance of being lower than 1.

• Vasco Grilo 21 Apr 2024 9:41 UTC

  2 points

  0 ∶ 0

  in reply to: MichaelStJules’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  Oh, I didn’t mean for you to define the period explicitly as a fixed interval period. I assume this can vary by catastrophe. Like maybe population declines over 5 years with massive crop failures. Or, an engineered pathogen causes massive population decline in a few months.

  Hi @MichaelStJules, I am tagging you because I have updated the following sentence. If there is a period longer than 1 year over which population decreases, the power laws describing the ratio between the initial and final population of each of the years following the 1st could have different tail indices, with lower tail indices for years in which there is a larger population loss. I do not think the duration of the period is too relevant for my overall point. For short and long catastrophes, I expect the PDF of the ratio between the initial and final population to decay faster than the benefits of saving a life, such that the expected value density of the cost-effectiveness decreases with the severity of the catastrophe (at least for my assumption that the cost to save a life does not depend on the severity of the catastrophe).

  I just wasn’t sure what exactly you meant. Another intepretation would be that P_f is the total post-catastrophe population, summing over all future generations, and I just wanted to check that you meant the population at a given time, not aggregating over time.

  I see! Yes, both $P_i$ and $P_f$ are population sizes at a given point in time.

• Vasco Grilo 21 Apr 2024 9:29 UTC

  2 points

  0 ∶ 0

  in reply to: Owen Cotton-Barratt’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  I think that the risk of human extinction over 1 year is almost all driven by some powerful new technology (with residues for the wilder astrophysical disasters, and the rise of some powerful ideology which somehow leads there). But this is an important class! In general dragon kings operate via something which is mechanically different than the more tame parts of the distribution, and “new technology” could totally facilitate that.

  To clarify, my estimates are supposed to account for unknown unknowns. Otherwise, they would be any orders of magnitude lower.

  Unfortunately, for the relevant part of the curve (catastrophes large enough to wipe out large fractions of the population) we have no data, so we’ll be relying on theory.

  I found the “Unfortunately” funny!

  My understanding (based significantly just on the “mechanisms” section of that wikipedia page) is that dragon kings tend to arise in cases where there’s a qualitatively different mechanism which causes the very large events but doesn’t show up in the distribution of smaller events. In some cases we might not have such a mechanism, and in others we might.

  Makes sense. We may even have both cases in the same tail distribution. The tail distribution of the annual war deaths as a fraction of the global population is characteristic of a power law from 0.001 % to 0.01 %, then it seems to have a dragon king from around 0.01 % to 0.1 %, and then it decreases much faster than predicted by a power law. Since the tail distribution can decay slower and faster than a power law, I feel like this is still a decent assumption.

  It certainly seems plausible to me when considering catastrophes (and this is enough to drive significant concern, because if we can’t rule it out it’s prudent to be concerned, and risk having wasted some resources if we turn out to be in a world where the total risk is extremely small), via the kind of mechanisms I allude to in the first half of this comment.

  I agree we cannot rule out dragon kings (flatter sections of the tail distribution), but this is not enough for saving lives in catastrophes to be more valuable than in normal times. At least for the annual war deaths as a fraction of the global population, the tail distribution still ends up decaying faster than a power law despite the presence of a dragon king, so the expected value density of the cost-effectiveness of saving lives is still lower for larger wars (at least given my assumption that the cost to save a life does not vary with the severity of the catastrophe). I concluded the same holds for the famine deaths caused by the climatic effects of nuclear war.

  One could argue we should not only put decent weight on the existence of dragon kings, but also on the possibility that they will make the expected value density of saving lives higher than in normal times. However, this would be assuming the conclusion.

• Vasco Grilo 21 Apr 2024 8:30 UTC

  2 points

  0 ∶ 0

  in reply to: Denkenberger’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  Thanks for the comment, David! I agree all those effects could be relevant. Accordingly, I assume that saving a life in catastrophes (periods over which there is a large reduction in population) is more valuable than saving a life in normal times (periods over which there is a minor increase in population). However, it looks like the probability of large population losses is sufficiently low to offset this, such that saving lives in normal time is more valuable in expectation.

• Vasco Grilo 20 Apr 2024 22:37 UTC

  2 points

  0 ∶ 0

  in reply to: Owen Cotton-Barratt’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  Thanks for clarifying! I agree B) makes sense, and I am supposed to be doing B) in my post. I calculated the expected value density of the cost-effectiveness of saving a life from the product between:

  • A factor describing the value of saving a life ($B=k_B{\left(P_i/P_f\right)}^{{ϵ}_B}$).

  • The PDF of the ratio between the initial and final population ($f=\alpha (P_i/P_f{)}^{-(\alpha +1)}$), which is meant to reflect the probability of a catastrophe.

• Vasco Grilo 20 Apr 2024 22:23 UTC

  2 points

  0 ∶ 0

  in reply to: Owen Cotton-Barratt’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  if you’re primarily trying to model effects on extinction risk

  I am not necessarily trying to do this. I intended to model the overall effect of saving lives, and I have the intuition that saving a life in a catastrophe (period over which there is a large reduction in population) conditional on it happening is more valuable than saving a life in normal times, so I assumed the value of saving a life increases with the severity of the catastrophe. One can assume preventing extinction is specially important by selecting a higher value for ${ϵ}_B$ (“the elasticity of the benefits [of saving a life] with respect to the ratio between the initial and final population”).

• Vasco Grilo 20 Apr 2024 22:03 UTC

  2 points

  0 ∶ 0

  in reply to: Owen Cotton-Barratt’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  Thanks for the critique, Owen! I strongly upvoted it.

  I’m worried that modelling the tail risk here as a power law is doing a lot of work, since it’s an assumption which makes the risk of very large events quite small (especially since you’re taking a power law in the ratio

  Assuming the PDF of the ratio between the initial and final population follows a loguniform distribution (instead of a power law), the expected value density of the cost-effectiveness of saving a life would be constant, i.e. it would not depend on the severity of the catastrophe. However, I think assuming a loguniform distribution for the ratio between the initial and final population majorly overestimates tail risk. For example, I think a population loss (over my period length of 1 year^[1]) of 90 % to 99 % (ratio between the initial and final population of 10 to 100) is more likely than a population loss of 99.99 % to 99.999 % (ratio between the initial and final population of 10 k to 100 k), whereas a loguniform distribution would predict both of these to be equally likely.

  aside from the threshold from requiring a certain number of humans to have a viable population

  My reduction in population is supposed to refer to a period of 1 year, but the above only decreases population over longer horizons.

  the structure of the assumption essentially gives that extinction is impossible

  I think human extinction over 1 year is extremely unlikely. I estimated 5.93*10^-12 for nuclear wars, 2.20*10^-14 for asteroids and comets, 3.38*10^-14 for supervolcanoes, a prior of 6.36*10^-14 for wars, and a prior of 4.35*10^-15 for terrorist attacks.

  But we know from (the fancifully named) dragon king theory that the very largest events are often substantially larger than would be predicted by power law extrapolation.

  Interesting! I did not know about that theory. On the other hand, there are counterexamples. David Roodman has argued the tail risk of solar storms decreases faster than predicted by a power law:

  

  I have also found the tail risk of wars decreases faster than predicted by a power law:

  

  Do you have a sense of the extent to which the dragon king theory applies in the context of deaths in catastrophes?

  1. ^

     I have now clarified this in the post.

• Vasco Grilo 20 Apr 2024 20:59 UTC

  2 points

  0 ∶ 0

  in reply to: Owen Cotton-Barratt’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  I’m confused by some of the set-up here. When considering catastrophes, your “cost to save a life” represents the cost to save that life conditional on the catastrophe being due to occur? (I’m not saying “conditional on occurring” because presumably you’re allowed interventions which try to avert the catastrophe.)

  My language was confusing. By “pre- and post-catastrophe population”, I meant the population at the start and end of a period of 1 year, which I now also refer to as the initial and final population. I have now clarified this in the post.

  I assume the cost to save a life in a given period is a function of the ratio between the initial and final population of the period.

  Or is the point that you’re only talking about saving lives via resilience mechanisms in catastrophes, rather than trying to make the catastrophes not happen or be small? But in that case the conclusions about existential risk mitigation would seem unwarranted.

  I meant to refer to all mechanisms (e.g. prevention, response and resilience) which affect the variation in population over a period.

• Vasco Grilo 20 Apr 2024 20:54 UTC

  2 points

  0 ∶ 0

  in reply to: Owen Cotton-Barratt’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  Thanks for all your comments, Owen!

  That paper was explicitly considering strategies for reducing the risk of human extinction.

  My expected value density of the cost-effectiveness of saving a life, which decreases as catastrophe severity increases, is supposed to account for longterm effects like decreasing the risk of human extinction.

• Vasco Grilo 20 Apr 2024 20:46 UTC

  2 points

  0 ∶ 0

  in reply to: MichaelStJules’s comment on: Sav­ing lives in nor­mal times is bet­ter to im­prove the longterm fu­ture than do­ing so in catas­tro­phes?

  Thanks for the comment, Michael!

  Also, to be clear, this is supposed to be ~immediately pre-catastrophe and ~immediately post-catastrophe, right? (Catastrophes can probably take time, but presumably we can still define pre- and post-catastrophe periods.)

  I have updated the post changing “pre- and post-catastrophe population” to “population at the start and end of a period of 1 year”, which I now also refer to as the initial and final population.

  You’re modelling the cost-effectiveness of saving a life conditional on catastrophe here, right?

  No. It is supposed to be the cost-effectiveness as a function of the ratio between the initial and final population.

  Typically x-risk interventions aim at reducing the risk of catastrophe, not the benefits conditional on catastrophe.

  Yes, interpreting catastrophe as a large population loss. In my framework, xrisk interventions aim to save lives over periods whose initial population is significantly higher than the final one.