JoshuaBlake

• JoshuaBlakeFeb 26, 2025, 8:42 AM

  2 points

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  on: An easy win for hard de­ci­sions.

  Thanks for this post @alex lawsen, I continually revisit this as inspiration and to remember the usefulness of this process when I am making hard decisions, especially for my career.

  From your blog, I know you are a big user of LLMs. I was wondering if you had successfully used them to replace, or complement, this process? When I feed one my Google Doc, I find the output is too scattergun or vague to be useful, compared to sharing the same Doc with friends.

  If you’ve succeeded using LLMs, would you please share what prompts and models that have worked well for you?

• JoshuaBlakeFeb 12, 2025, 8:03 PM

  3 points

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  in reply to: Matthew_Barnett’s comment on: The am­bigu­ous effect of full au­toma­tion on wages

  Thanks, that’s very useful for me trying to follow but not that deep in the models!

• JoshuaBlakeFeb 12, 2025, 7:43 PM

  2 points

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  in reply to: Matthew_Barnett’s comment on: The am­bigu­ous effect of full au­toma­tion on wages

  I feel like the overall takeaway is very different though. I’ve not fully understood the details in either argument so this is a little vibes based. You seemed to be arguing that below subsistence wages were fairly likely while here it seems to be that even falling wages require a weird combination of conditions.

  What have I misunderstood?

• JoshuaBlakeJan 30, 2025, 1:54 PM

  6 points

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  on: Char­ity Cost-Effec­tive­ness Really Does Fol­low a Power Law

  This seems like good work but the headline and opening paragraph aren’t supported when you’ve shown it might be log-normal. Log-normal and power distributions often have quite different consequences for how important it is to move to very extreme percentiles, and hence this difference can matter for lots of decisions relevant to EA.

• JoshuaBlakeJan 2, 2025, 8:50 PM

  9 points

  3 ∶ 1

  on: #211 – Why hous­ing still isn’t fixed and what would ac­tu­ally work (Sam Bow­man on The 80,000 Hours Pod­cast)

  While Sam Bowman is interesting and seems ideologically similar to many EAs, it’s not obvious to me why he’s on the 80k podcast. Does 80k think UK housebuilding is one of the world’s most pressing problems? Is there something else I’m missing?

• JoshuaBlakeDec 13, 2024, 2:11 PM

  3 points

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  on: State of EA Poland and fund­ing opportunity

  Naively, this seems like a great fit for EAIF, which is looking to fund more projects. Is there something I’m missing?

• JoshuaBlakeOct 24, 2024, 6:28 PM

  2 points

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  on: How Likely Are Var­i­ous Pre­cur­sors of Ex­is­ten­tial Risk?

  Type of estimate: Geometric mean of forecaster probabilities

  This is a bit odd. Should probabilities be odds?

• JoshuaBlakeJul 16, 2024, 4:17 PM

  12 points

  2 ∶ 0

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

  Could you please expand on why you think a Pareto distribution is appropriate here? Tail probabilities are often quite sensitive to the assumptions here, and it can be tricky to determine if something is truly power-law distributed.

  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?

  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.

• JoshuaBlakeJul 15, 2024, 8:40 AM

  6 points

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  in reply to: Will Bradshaw’s comment on: The Precipice Revisited

  If you were willing to start putting doses in arms without any safety or efficacy testing at all, that’s when you could start.

  I think even this is pretty optimistic because there was very little manufacturing capacity at that point.

• JoshuaBlakeJul 11, 2024, 2:34 PM

  4 points

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  in reply to: Jeff Kaufman 🔸’s comment on: Sam­ple Prevalence vs Global Prevalence

  I don’t see how you can say both that it will “almost never” be the case that NYC will “hit 1% cumulative incidence after global 1% cumulative incidence” but also that it would surprise you if you can get to where your monitored cities lead global prevalence?

  Sorry, this is poorly phrased by me. I meant that it would surprise me if there’s much benefit from adding a few additional cities.

• JoshuaBlakeJul 10, 2024, 10:19 PM

  2 points

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  in reply to: calebp’s comment on: Sam­ple Prevalence vs Global Prevalence

  The best stuff looking at global-scale analysis of epidemics is probably by GLEAM. I doubt full agent-based modelling at small-scales is giving you much but massively complicating the model.

• JoshuaBlakeJul 10, 2024, 10:15 PM

  4 points

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  in reply to: Jeff Kaufman 🔸’s comment on: Sam­ple Prevalence vs Global Prevalence

  Sorry, I answered the wrong question, and am slightly confused what this post is trying to get out. I think your question is: will NYC hit 1% cumulative incidence after global 1% cumulative incidence?

  I think this is almost never going to be the case for fairly indiscriminately-spreading respiratory pathogens, such as flu or COVID.

  The answer is yes only if NYC’s cumulative incidence is lower than the global mean region (weighted by population). Due to connectedness, I expect NYC to always be hit pretty early, as you point out, definitely before most rural communities. I think the key point here is that NYC doesn’t need to be ahead of the epicentre of the disease, only the global mean.

  One way of looking at this is how early on does NYC get hit compared to other cities/​regions. This analysis (pdf) orders cities by connectedness to Wuhan to answer this question for COVID. It looks like they’ve released an online tool that lets you specify different origin locations and epidemiological parameters. So you could rank how early NYC gets hit for a range of different scenarios.

  by carefully choosing a few cities to monitor around the world you can probably get to where it leads global prevalence

  This would surprise me. It’s hard to imagine a scenario where the arrival time at different major travel hubs is very desynchronized as these locations are highly connected to each other. So you’d probably then end up looking at a long tail of locations which are poorly connected to the main travel hubs.

• JoshuaBlakeJul 10, 2024, 5:31 PM

  2 points

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  on: Sam­ple Prevalence vs Global Prevalence

  This effect should diminish as the pandemic progresses, but at least in the <1% cumulative incidence situations I’m most interested in it should remain a significant factor.

  1% cumulative incidence is quite high, so I think this is probably far along you’re fine. E.g. we’ve estimated London hit this point for COVID around 22 Mar 2020 when it was pretty much everywhere.

• JoshuaBlakeJul 9, 2024, 3:07 PM

  4 points

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  in reply to: OscarD🔸’s comment on: Num­ber of EAs per cap­ita by country

  Hmm.… That cutoff is really making it hard to assess what’s going on here IMO. Everything is kinda clustered close to the line making me suspect the selection effect is important.

• JoshuaBlakeJul 1, 2024, 7:18 AM

  4 points

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  in reply to: Seth Ariel Green 🔸’s comment on: Farmed an­i­mals are neglected

  This seems unnecessarily precise and long. I don’t think many people would get a different takeaway from the current title but it has over twice as many words.

• JoshuaBlakeJul 1, 2024, 7:10 AM

  2 points

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  in reply to: OscarD🔸’s comment on: Num­ber of EAs per cap­ita by country

  You could do a funnel type plot where your y-axis is EAs/​capita and your x-axis is 1/​sqrt(population), which is sort of what you’d expect the standard deviation to look like.

• JoshuaBlakeJul 1, 2024, 7:07 AM

  7 points

  1 ∶ 0

  on: Cost-effec­tive­ness of epi­demic/​pan­demic preparedness

  This seems intuitively in the right ballpark (within an order of magnitude of GiveWell), but I’d caution that, as far as I can tell, the World Bank and Bernstein et al. numbers are basically made up.

  I’ve previously written about how to identify higher impact opportunities. In particular, we need to be careful about the counterfactuals here because a lot of the money on pandemic preparedness comes from governments who would otherwise spend on even less cost effective things.

• JoshuaBlakeJul 1, 2024, 6:58 AM

  3 points

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  in reply to: Dhruvin Patel’s comment on: Ad­vice for cur­rent /​ po­ten­tial PhD students

  Glad you found it useful.

  Your question is hard to answer because tackling GCBRs is extremely inter-disciplinary and I’m not familiar enough with the field at large to identify clear gaps. Personal fit probably matters more than the specific field. Most work on pandemic preparedness is applicable to GCBRs, and there’s lots of research groups on that.

  Sorry that’s not a very helpful answer. If you’ve got more specific questions I’m happy to try and help.

Ad­vice for cur­rent /​ po­ten­tial PhD students

• JoshuaBlakeMay 25, 2024, 12:14 PM

  8 points

  3 ∶ 1

  in reply to: Ramiro’s comment on: The Char­ity Com­mis­sion has con­cluded its in­quiry into Effec­tive Ven­tures Foun­da­tion UK

  My top line summary is: in several areas, EV were operating below the standard the commission would expect, but have rectified the issues to the commission’s satisfaction.