Vasco Grilo🔸

• Vasco Grilo🔸 25 Jul 2024 19:52 UTC

  2 points

  0 ∶ 0

  in reply to: jackva’s comment on: Fu­ture deaths from non-op­ti­mal tem­per­a­ture and cost-effec­tive­ness of strato­spheric aerosol injection

  Thanks for this, Vasco, thought-provoking as always!

  Likewise! Thanks for the thoughtful comment.

  Insofar as is this a correct representation of your argument

  It seems like a fair representation.

  a. Dying from heat stress is a very extreme outcome and people will act in response to climate change much earlier than dying. For example, before people die from heat stress, they might abandon their livelihoods and migrate, maybe in large numbers.
  
  b. More abstractly, the fact that an extreme impact outcome (heat death) is relatively rare is not evidence for low impact in general. Climate change pressures are not like a disease that kills you within days of exposure and otherwise has no consequence.

  Agreed. However:

  • I think migration will tend to decrease deaths because people will only want to migrate if they think their lives will improve (relative to the counterfactual of not migrating).

  • The deaths from non-optimal temperature I mentioned are supposed to account for all causes of death, not just extreme heat and cold. According to GBD, in 2021, deaths from environmental heat and cold exposure were 36.0 k (I guess this is what you are referring to by heat stress), which was just 1.88 % (= 36.0*10^3/​(1.91*10^6)) of the 1.91 M deaths from non-optimal temperature. My post is about how these 1.91 M deaths would change.

  a. You seem to suggest we are very uncertain about many of the effect signs. I think the basic argument why people concerned about climate change would argue that changes will be negative and that there be compounding risks is because natural and human systems are adapted to specific climate conditions. That doesn’t mean they cannot adapt at all, but that does mean that we should expect it is more likely that effects are negative, at least as short-term shocks, than positive for welfare.

  This makes sense. On the other hand, one could counter global warming will be good because:

  • There are more deaths from low temperature than from high temperature.

  • The disease burden per capita from non-optimal temperature has so far been decreasing (see 2nd to last graph).

  b. I think a lot of the other arguments on the side of “indirect risks are low” you cite are ultimately of the form (i) “indirect effects in other causes are also large” or (ii) “pointing to indirect effects make things inscrutable and unverifiable”. (i) might be true but doesn’t matter, I think, for the question of whether warming is net-bad and (ii) is also true, but does nothing by itself on whether those indirect effects are real—we can live in a world where indirect effects are rhetorically abused and still exist and indeed dominate in certain situations!

  Agreed. I would just note that i) can affect prioritisation across causes.

• Vasco Grilo🔸 25 Jul 2024 19:06 UTC

  3 points

  0 ∶ 2

  in reply to: Stephen Clare’s comment on: The Precipice Revisited

  Thanks for the comment, Stephen.

  Vasco, how do your estimates account for model uncertainty?

  I tried to account for model uncertainty assuming 10^-6 probability of human extinction given insufficient calorie production.

  I don’t understand how you can put some probability on something being possible (i.e. p(extinction|nuclear war) > 0), but end up with a number like 5.93e-14 (i.e. 1 in ~16 trillion). That implies an extremely, extremely high level of confidence.

  Note there are infinitely many orders of magnitude between 0 and any astronomically low number like 5.93*10^-14. At least in theory, I can be quite uncertain while having a low best guess. I understand greater uncertainty (e.g. higher ratio between the 95th and 5th percentile) holding the median constant tends to increase the mean of heavy-tailed distributions (like lognormals), but it is unclear to which extent this applies. I have also accounted for that by using heavy-tailed distributions whenever I thought appropriate (e.g. I modelled the soot injected into the stratosphere per equivalent yield as a lognormal).

  As a side note, 10 of 161 (6.21 %) forecasters of the Existential Risk Persuasion Tournament (XPT), 4 experts and 6 superforecasters, predicted a nuclear extinction risk until 2100 of exactly 0. I guess these participants know the risk is higher than 0, but consider it astronomically low too.

  Putting ~any weight on models that give higher probabilities would lead to much higher estimates.

  I used to be persuaded by this type of argument, which is made in many contexts by the global catastrophic risk community. I think it often misses that the weight a model should receive is not independent of its predictions. I would say high extinction risk goes against the low prior established by historical conflicts.

  I am also not aware of any detailed empirical quantitative models estimating the probability of extinction due to nuclear war.

Fu­ture deaths from non-op­ti­mal tem­per­a­ture and cost-effec­tive­ness of strato­spheric aerosol injection

• Vasco Grilo🔸 24 Jul 2024 21:36 UTC

  7 points

  2 ∶ 3

  on: The Precipice Revisited

  Thanks for the update, Toby. I used to defer to you a lot. I no longer do. After investigating the risks myself in decent depth, I consistently arrived to estimates of the risk of human extinction orders of magnitude lower than your existential risk estimates. For example, I understand you assumed in The Precipice an annual existential risk for:

  • Nuclear war of around 5*10^-6 (= 0.5*10^-3/​100), which is 843 k (= 5*10^-6/​(5.93*10^-12)) times mine.

  • Volcanoes of around 5*10^-7 (= 0.5*10^-4/​100), which is 14.8 M (= 5*10^-7/​(3.38*10^-14)) times mine.

  In addition, I think the existential risk linked to the above is lower than their extinction risk. The worst nuclear winter of Xia et. al 2022 involves an injection of soot into the stratosphere of 150 Tg, which is just 1 % of the 15 Pg of the Cretaceous–Paleogene extinction event. Moreover, I think this would only be existential with a chance of 0.0513 % (= e^(-10^9/​(132*10^6))), assuming:

  • An exponential distribution with a mean of 132 M years (= 66*10^6*2) represents the time between i) human extinction in such catastrophe and ii) the evolution of an intelligent sentient species after such a catastrophe. I supposed this on the basis that:

    • An exponential distribution with a mean of 66 M years describes the time between:

      • 2 consecutive such catastrophes.

      • i) and ii) if there are no such catastrophes.

    • Given the above, i) and ii) are equally likely. So the probability of an intelligent sentient species evolving after human extinction in such a catastrophe is 50 % (= ¹⁄₂).

    • Consequently, one should expect the time between i) and ii) to be 2 times (= ¹⁄₀.50) as long as that if there were no such catastrophes.

  • An intelligent sentient species has 1 billion years to evolve before the Earth becomes habitable.

• Vasco Grilo🔸 24 Jul 2024 15:48 UTC

  2 points

  0 ∶ 0

  in reply to: Denkenberger’s comment on: New 80k prob­lem pro­file: Nu­clear weapons

  Hi David,

  Existential catastrophe, annual	0.30%	20.04%	David Denkenberger, 2018
  Existential catastrophe, annual	0.10%	3.85%	Anders Sandberg, 2018

  Based on my adjustments to CEARCH’s analysis of nuclear and volcanic winter, the expected annual mortality of nuclear winter as a fraction of the global population is 7.32*10^-6. I estimated the deaths from the climatic effects would be 1.16 times as large as the ones from direct effects. In this case, the expected annual mortality of nuclear war as a fraction of the global population would be 1.86 (= 1 + ¹⁄₁.16) times the expected annual mortality of nuclear winter as a fraction of the global population, i.e. 0.00136 %(= 1.86*7.32*10^-6). So the annual losses in future potential mentioned in the table above are 221 (= 0.0030/​(1.36*10^-5)) and 73.5 (= 0.0010/​(1.36*10^-5)) times my expected annual death toll, whereas I would have expected the annual loss in future potential to be much lower than the expected annual death toll.

• Vasco Grilo🔸 24 Jul 2024 15:22 UTC

  3 points

  0 ∶ 0

  in reply to: Matt Boyd’s comment on: New 80k prob­lem pro­file: Nu­clear weapons

  Great points, Matt.

  I think essentially all (not just many) pathways from AI risk will have to flow through other more concrete pathways. AI is a general purpose technology, so I feel like directly comparing AI risk with other lower level pathways of risk, as 80 k seems to be doing somewhat when they describe the scale of their problems, is a little confusing. To be fair, 80 k tries to account for this talking about the indirect risk of specific risks, which they often set to 10 times the direct risk, but these adjustments seem very arbitrary to me.

  In general, one can get higher risk estimates by describing risk at a higher level. So the existential risk from LLMs is smaller than the risk from AI, which is smaller than the risk from computers, which is smaller than the risk from e.g. subatomic particles. However, this should only update one towards e.g. prioritise “computer risk” over “LLM risk” to the extent the ratio between the cost-effectiveness of “computer risk interventions” and “LLM risk interventions” is proportional to the ratio between the scale of “computer risk” and “LLM risk”, which is quite unclear given the ambiguity and vagueness of the 4 terms involved^[1].

  To get more clarity, I believe it is be better to prioritise at a lower level, assessing the cost-effectiveness of specific classes of interventions, as Ambitious Impact (AIM), Animal Charity Evaluators (ACE), the Centre for Exploratory Altruism Research (CEARCH), and GiveWell do.

  1. ^

     “Computer risk”, “LLM risk”, “computer risk interventions” and “LLM risk interventions”.

• Vasco Grilo🔸 23 Jul 2024 14:33 UTC

  2 points

  0 ∶ 0

  in reply to: Will Howard🔹’s comment on: EA Fo­rum fea­ture sug­ges­tion thread

  Here is an example with text in a table aligned to the left (select all text → cell properties → table cell text alignement).

  Statistic	Annual epidemic/​pandemic deaths as a fraction of the global population
  Mean	0.236 %
  Minimum	0
  5th percentile	1.19*10^-6
  10th percentile	3.60*10^-6
  Median	0.0276 %
  90th percentile	0.414 %
  95th percentile	0.684 %
  Maximum	10.3 %

• Vasco Grilo🔸 22 Jul 2024 8:23 UTC

  6 points

  1 ∶ 1

  on: Should Open Philan­thropy have ex­ter­nal grant re­view­ers?

  Thanks for the post! I wonder whether it would also be good to have public versions of the applications (sensible information could be redacted), as Manifund does, which would be even less costly than having external reviewers.

• Vasco Grilo🔸 20 Jul 2024 9:16 UTC

  2 points

  0 ∶ 0

  in reply to: Will Howard🔹’s comment on: EA Fo­rum fea­ture sug­ges­tion thread

  Thanks, Will! Relatedly, I noted the importation makes the text in tables go from aligned to the centre in docs to aligned to the left/​right on the EA Forum editor.

Democ­racy in­dices weighted by nom­i­nal and real gross do­mes­tic product

• Vasco Grilo🔸 17 Jul 2024 18:58 UTC

  8 points

  0 ∶ 0

  in reply to: Michael Hinge’s comment on: A non-alarmist model of nu­clear winter

  Hi Mike.

  Los Alamos find that firestorms are highly unlikely to form under nuclear detonations, even at very high fuel loads, and so lofting is negligible. They only look at fission scale weaponry.

  I think this may well misrepresent Los Alamos’ view, as Reisner 2019 does not find significantly more lofting, and they did model firestorms. I estimated 6.21 % of emitted soot being injected into the stratosphere in the 1st 40 min from the rubble case of Reisner 2018, which did not produce a firestorm. Robock 2019 criticised this study, as you did, for not producing a firestorm. In response, Reisner 2019 run:

  Two simulations at higher fuel loading that are in the firestorm regime (Glasstone & Dolan, 1977): the first simulation (4X No-Rubble) uses a fuel load around the firestorm criterion (4 g/​cm2) and the second simulation (Constant Fuel) is well above the limit (72 g/​cm2).

  Crucially, they say (emphasis mine):

  Of note is that the Constant Fuel case is clearly in the firestorm regime with strong inward and upward motions of nearly 180 m/​s during the fine-fuel burning phase. This simulation included no rubble, and since no greenery (trees do not produce rubble) is present, the inclusion of a rubble zone would significantly reduce BC production and the overall atmospheric response within the circular ring of fire.

  These simulations led to a soot injected into the stratosphere in the 1st 40 min per emitted soot of 5.45 % (= 0.461/​8.454) and 6.44 % (= 1.53/​23.77), which are quite similar to the 6.21 % of Reisner 2018 for no firestorm I mentioned above. This suggests a firestorm is not a sufficient condition for a high soot injected into the stratosphere per emitted soot under Reisner’s view?

  In my analysis, I multiplied the 6.21 % emitted soot that is injected into the stratosphere in the 1st 40 min from Reisner 2018 by 3.39 in order to account for soot injected afterwards, but this factor is based on estimates which do not involve firestorms. Are you implying the corrective factor should be higher for firestorms? I think Reisner 2019 implicitly argues against this. Otherwise, they would have been dishonest by replying to Robock 2019 with an incomplete simulation whose results differ from that of the full simulation. In my analysis, I only adjusted the results from Reisner’s and Toon’s views in case there was explicit information to do so^[1], i.e. I did not assume they concealed key results.

  As a result, blending together the Los Alamos model with that of Rutgers doesn’t really work as a baseline, they’re based on a very different binary concerning firestorms and lofting and you exclude other relevant analysis, like that of Lawrence Livermore.

  In my analysis, I also did not integrate evidence from Wagman 2020 (whose main author is affiliated with Lawrence Livermore National Laboratory) to estimate the soot injected into the stratosphere per countervalue yield. As far as I can tell, they do not offer independent evidence from Toon’s view. Rather than estimating the emitted soot as Reisner 2018 and Reisner 2019 did, they set it to the soot injected into the stratosphere in Toon 2007:

  Finally, we choose to release 5 Tg (5·10^12 g) BC into the climate model per 100 fires, for consistency with the studies of Mills et al. (2008, 2014), Robock et al. (2007), Stenke et al. (2013), Toon et al. (2007), and Pausata et al. (2016). Those studies use an emission of 6.25 Tg BC and assume 20% is removed by rainout during the plume rise, resulting in 5 Tg BC remaining in the atmosphere.

  1. ^

     For example, I adjusted downwards the soot injected into the stratosphere from Reisner 2019 (based on data from Denkenberger 2018), as it says (emphasis mine):

     Table 1. Estimated BC Using an Idealized Diagnostic Relationship (BC Estimates Need to be Reduced by a Factor of 10–100) and Fuel Loadings From the Simulations Shown in Reisner et al. and Two New Simulations for 100 15-kt Detonations

• Vasco Grilo🔸 17 Jul 2024 18:18 UTC

  8 points

  0 ∶ 0

  in reply to: Stan Pinsent’s comment on: A non-alarmist model of nu­clear winter

  Great points, Stan!

  Yes, I agree that the crux is whether firestorms will form.

  I am not confident this is the crux.

  The main thing I would like people to take away is that we remain uncertain what would be more damaging about a nuclear conflict: the direct destruction, or its climate-cooling effects.

  I arrived at the same conclusion in my analysis, where I estimated the famine deaths due to the climatic effects of a large nuclear war would be 1.16 times the direct deaths.

• Vasco Grilo🔸 17 Jul 2024 17:19 UTC

  3 points

  0 ∶ 5

  on: Re­think Pri­ori­ties’ Mo­ral Par­li­a­ment Tool

  Thanks for making this! I think it is valuable, although I should basically just donate to whatever I think is the most cost-effective given my strong endorsement of expected total hedonistic utilitarianism and maximising expected choice-worthiness.

• Vasco Grilo🔸 16 Jul 2024 18:19 UTC

  2 points

  0 ∶ 0

  in reply to: JoshuaBlake’s comment on: Can a pan­demic cause hu­man ex­tinc­tion? Pos­si­bly, at least on priors

  Thanks for the relevant points, Joshua. I strongly upvoted your comment.

  Could you please expand on why you think a Pareto distribution is appropriate here?

  I did not mean to suggest a Pareto distribution is appropriate, just that it is worth considering.

  Tail probabilities are often quite sensitive to the assumptions here, and it can be tricky to determine if something is truly power-law distributed.

  Agreed. In my analysis of conflict deaths, for the method where I used fitter:

  The 5th and 95th percentile annual probability of a conflict causing human extinction are 0 and 5.02 % [depending on the distribution]

  ---

  When I looked at the same dataset, albeit processing the data quite differently, I found that a truncated or cutoff power-law appeared to be a good fit. This gives a much lower value for extreme probabilities using the best-fit parameters. In particular, there were too few of the most severe pandemics in the dataset (COVID-19 and 1918 influenza) otherwise; this issue is visible in fig 1 of Marani et al. Could you please add the data to your tail distribution plot to assess how good a fit it is?

  I did not get what you would like me to add to my tail distribution plot. However, I added here the coefficients of determination (R^2) of the regressions I did.

  A final note, I think you’re calculating the probability of extinction in a single year but the worst pandemics historically have lasted multiple years. The total death toll from the pandemic is perhaps the quantity most of interest.

  Focussing on the annual deaths as a fraction of the global population is useful because it being 1 is equivalent to human extinction. In contrast, total epidemic/​pandemic deaths as a fraction of the global population in the year in which the epidemic/​pandemic started being equal to 1 does not imply human extinction. For example, a pandemic could kill 1 % of the population each year for 100 years, but population remain constant due to births being equal to the pandemic deaths plus other deaths.

  However, I agree interventions should be assessed based on standard cost-effectiveness analyses. So I believe the quantity of most interest which could be inferred from my analysis is the expected annual epidemic/​pandemic deaths. These would be 2.28 M (= 2.87*10^-4*7.95*10^9) multiplying:

  • My annual epidemic/​pandemic deaths as a fraction of the global population based on data from 1900 to 2023. Earlier years are arguably not that informative.

  • The population in 2021.

  The above expected death toll would rank as 6th in 2021.

  For reference, based on my analysis of conflicts, I get an expected death toll of conflicts based on historical data from 1900 to 2000 (also adjusted for underreporting), and the population in 2021 of 3.83 M (= 2.87*10^-4*7.95*10^9), which would rank above as 5th.

  Here is a graph with the top 10 actual causes of death and expected conflict and epidemic/​pandemic deaths:

  

• Vasco Grilo🔸 16 Jul 2024 7:12 UTC

  2 points

  0 ∶ 0

  in reply to: Jeff Kaufman’s comment on: Can a pan­demic cause hu­man ex­tinc­tion? Pos­si­bly, at least on priors

  Thanks for the comment, Jeff.

  Are the high numbers of deaths in the 1500s old world diseases spreading in the new world?

  Yes, and deaths are especially high in the 1500s given my assumption of high underreporting then.

  If so, that seems to overestimate natural risk: the world’s current population isn’t separated from a larger population that has lots of highly human-adapted diseases.

  Agreed. Personally, I guess the annual probability of a natural pandemic causing human extinction is lower than 10^-10.

  In the other direction, this kind of analysis doesn’t capture what I personally see as a larger worry: human-created pandemics. I know you’re extrapolating from the past, and it’s only very recently that these would even have been possible, but this seems at least worth noting.

  I think it is interesting that:

  There has been a downward trend in the logarithm of the annual epidemic/​pandemic deaths as a fraction of the global population, with the R^2 of the linear regression of it on the year being 38.5 %. I guess the sign of the slope is resilient against changes to my modelling of the underreporting. One may argue the aforementioned logarithm will increase in the next few decades based on inside view factors such as technology becoming cheaper and more powerful. Nevertheless, technology also became cheaper and more powerful during the period of 1500 to 2023 covered by my data, but these still suggest a decreasing logarithm of the annual epidemic/​pandemic deaths as a fraction of the global population.

Can a pan­demic cause hu­man ex­tinc­tion? Pos­si­bly, at least on priors

• Vasco Grilo🔸 15 Jul 2024 14:27 UTC

  2 points

  0 ∶ 0

  in reply to: tobycrisford’s comment on: On the Value of Ad­vanc­ing Progress

  Are you saying:

  1. There are no concievable interventions someone could make with p non-tiny.

  2. U, expected utility in a year in 500 years time, is approximately 0.

  3. Something else… my setup of the situation is wrong, or unrealistic..?

  1, in the sense I think the change in the immediate risk of human extinction per cost is astronomically low for any conceivable intervention. Relatedly, you may want to check my discussion with Larks in the post I linked to.

• Vasco Grilo🔸 15 Jul 2024 12:48 UTC

  2 points

  0 ∶ 0

  in reply to: Stan Pinsent’s comment on: Re­silience to Nu­clear & Vol­canic Winter

  Thanks for the feedback on the votes and animal welfare comparison!

• Vasco Grilo🔸 14 Jul 2024 21:10 UTC

  4 points

  0 ∶ 0

  in reply to: tobycrisford’s comment on: On the Value of Ad­vanc­ing Progress

  Thanks for the kind words. I was actually unsure whether I should have followed up given my comments in this thread had been downvoted (all else equal, I do not want to annoy readers!), so it is good to get some information.

  Thanks for the detailed reply on that! You’ve clearly thought about this a lot, and I’m very happy to believe you’re right on the impact of nuclear war, but It sounds like you are more or less opting for what I called option 1? In which case, just substitute nuclear war for a threat that would literally cause extinction with high probability (say release of a carefully engineered pathogen with high fatality rate, long incubation period, and high infectiousness). Wouldn’t that meaningfully affect utility for more than a few centuries? Because there would be literally no one left, and that effect is guaranteed to be persistent! Even if it “just” reduced the population by 99%, that seems like it would very plausibly have effects for thousands of years into the future.

  I think the effect of the intervention will still decrease to practically 0 in at most a few centuries in that case, such that reducing the nearterm risk of human extinction is not astronomically cost-effective. I guess you are imagining that humans either go extinct or have a long future where they go on to realise lots of value. However, this is overly binary in my view. I elaborate on this in the post I linked to at the start of this paragraph, and its comments.

  It seems to me that to avoid this, you have to either say that causing extinction (or near extinction level catastrophe) is virtually impossible, through any means, (what I was describing as option 1) or go the other extreme and say that it is virtually guaranteed in the short term anyway, so that counterfactual impact disappears quickly (what I was describing as option 2). Just so I understand what you’re saying, are you claiming one of these two things? Or is there another way out that I’m missing?

  I guess the probability of human extinction in the next 10 years is around 10^-7, i.e. very unlikely, but far from impossible.

• Vasco Grilo🔸 14 Jul 2024 12:24 UTC

  2 points

  0 ∶ 0

  in reply to: Vasco Grilo🔸’s comment on: Famine deaths due to the cli­matic effects of nu­clear war

  Actually, I think I did well using the geometric mean. So I no longer endorse the comment above, and may have overall underestimated deaths in my post.