Linch

• Linch 21 Apr 2021 2:58 UTC

  4 points

  on: What are your ques­tions for World Malaria Day with Rob Mather (AMF), Maddy Maras­ciulo (Malaria Con­sor­tium), and Alekos Si­moni (Tar­get Malaria)?

  Questions for both:
  
  How has the covid-19 pandemic affected your ability to raise money for malaria prevention from both EA and non-EA sources?
  
  How has it affected your ability to deliver {bednets, chemoprevention} to beneficiaries?

• Linch 9 Apr 2021 16:07 UTC

  2 points

  on: Non-phar­ma­ceu­ti­cal in­ter­ven­tions in pan­demic pre­pared­ness and response

  Do you have thoughts on pandemic prevention NPIs (eg vector control)? Many of these things are technically non-pharmaceutical interventions, though of course looks very different from mask mandates or social distancing orders!

• Linch 8 Apr 2021 21:00 UTC

  7 points

  on: Non-phar­ma­ceu­ti­cal in­ter­ven­tions in pan­demic pre­pared­ness and response

  Thanks a lot for this! Like willbradshaw I agree that this post is “well-written, thoughtful, well-linked and thorough!”

  What are some objections to anything I’ve written here?

  If I were to nitpick, I think my biggest objection is that your approach to tackling the problem of NPIs for pandemic preparedness and response appears extremely atheoretical. I think this is fine for a scoping study that tries to estimate the scale of the problem, and fine (perhaps even highly underrated!) for clinical studies. But I think we can get decent results at lower cost with a bit of simple theory.
  
  I believe this because I think the human body in general, and the immune system in particular, is woefully complicated, so it makes sense that we cannot have much faith in biologically plausible mechanisms for treatments, which leads us to necessitate correspondingly greater faith in end-to-end RCTs(and be in a state of radical cluelessness otherwise). But there are other parts of epidemiology that’s simpler and more well-understood, such that for transmission we can be reasonably confident in our ability to dice the problem and isolate it into specific confusing subcomponents.
  
  For example, suppose we are worried about a potential respiratory disease pandemic, and we want to figure out whether intervention X (say installing MERV filters for offices) has a sufficiently large impact on an (un)desired endpoint (eg symptomatic disease, hospitalizations). One approach might just be:

  Sounds plausible, but we can’t know much with confidence in the absence of end-to-end empirical results. What we can do is run an RCT where we install MERV filters in the treatment group and don’t install it in the control group with a sufficiently large sample size to power for differences that are big enough for us to care about, and compare results after the study’s natural endpoint.

  I think this is good, but potentially quite expensive/​time-consuming (which is really bad in a fast-moving pandemic!). One way we can potentially do better:
  

  Well, disease transmission isn’t magic, and we’re reasonably confident in the very high-level theory of respiratory diseases. So we can at least decompose the problem into two parts:

  1. Treat human bodies as a black box function that takes in some combination of scary microbe-laden particles and outputs some probability of undesired endpoints.

  2. Model the world as something that sends scary microbe-laden particles and figure out which interventions reduce such particles to a level that the modeled function in (1) should consider too low to notice.

  My decomposition isn’t particularly interesting, but I think it’s reasonably clean. With it, we can
  
  Tackle 1) with human challenge trials where microbe dose/​frequency/​timing is variable, to understand what are plausible ranges of parameters for how many droplets are needed to be bad.
  
  Tackle 2) with some combination of

  1. computational fluid dynamics simulations

  2. lab experiments on how much people breathe each other’s air, and how fast air need to cycle to reduce that.

  3. field experiments on effect of MERV filters on closely analogous particles (on the physical level)

  4. prior knowledge of the transmission patterns of other similar diseases

  5. ???

  Now my decomposition is still quite high level, and I’m not sure that my suggested instrumentalizations here aren’t dumb. But hopefully what I’m gesturing at makes sense?

• Linch 8 Apr 2021 10:45 UTC

  3 points

  on: A Biose­cu­rity and Biorisk Read­ing+ List

  Thanks a lot for this list!
  
  This is a bit of a tangent, but one implicit assumption I find interesting in your list and when other EA biosecurity-focused people talk about existential biosecurity (eg, this talk by Kevin Esvelt ) is that there’s relatively little focus on what I consider “classical epidemiology.”

  This seems in contrast to the implicit beliefs of both a) serious EAs who haven’t thought as much about biosecurity (weak evidence here: the problem/​speaker selection 80000 hours podcasts) and b) public health people who are less aware of EA (weak evidence here: undergrad or grad students in public health who I sometimes talk to or are in an advisory position for).

  Putting numbers to this vague intuition, I would guess that your reading list here would suggest an optimal biosecurity-focused portfolio will have a focus of ~5-20% in classical epidemiology, whereas many EA students would think the weighting of epidemiology should be closer to ~30-60%.
  
  I’m interested in whether you agree with my distinction here and consider it a fair characterization? If so, do you think it’s worthwhile to have a writeup explaining why (or why not!) many EA-aligned students overweight epidemiology in their portfolio of considerations for important ways to reduce existential biorisk?
  
  EDIT: Relevant Twitter poll.

• Linch 5 Apr 2021 3:08 UTC

  4 points

  on: Sys­temic change, global poverty erad­i­ca­tion, and a ca­reer plan re­think: am I right?

  You may be interested in Max Roser’s perspective on his interactions with Hickel.

• Linch 1 Apr 2021 15:37 UTC

  4 points

  in reply to: Ben_West’s comment on: How much does perfor­mance differ be­tween peo­ple?

  This is really cool, thank you!

• Linch 1 Apr 2021 1:50 UTC

  7 points

  in reply to: Aaron Gertler’s comment on: What are your main reser­va­tions about iden­ti­fy­ing as an effec­tive al­tru­ist?

  If I recall correctly, this was not your position several years ago , when we talked about this more(circa 2015, 2016 or so). Which is not too surprising—I mean I sure hope I changed a lot in the intervening years!
  
  But assuming my memory of this is correct, do you recall when you made this shift, and the core reasons for it? Interested if there’s a short/​fast way to retrace your intellectual journey so that other people might make the relevant updates.

• Linch 29 Mar 2021 20:35 UTC

  4 points

  in reply to: Max_Daniel’s comment on: How much does perfor­mance differ be­tween peo­ple?

  I have indeed made that comment somewhere. It was one of the more insightful/​memorable comments she made when I interviewed her, but tragically I didn’t end up writing down that question in the final document (maybe due to my own lack of researcher taste? :P)
  
  That said, human memory is fallible etc so maybe it’d be worthwhile to circle back to Liv and ask if she still endorses this, and/​or ask other poker players how much they agree with it.

• Linch 26 Mar 2021 0:52 UTC

  31 points

  on: How much does perfor­mance differ be­tween peo­ple?

  Thanks for this. I do think there’s a bit of sloppiness in EA discussions about heavy-tailed distributions in general, and the specific question of differences in ex ante predictable job performance in particular. So it’s really good to see clearer work/​thinking about this.

  I have two high-level operationalization concerns here:

  1. Whether performance is ex ante predictable seems to be a larger function of our predictive ability than of the world. As an extreme example of what I mean, if you take our world on November 7, 2016 and run high-fidelity simulations 1,000,000 times , I expect 1 million/​1 million of those simulations to end up with Donald Trump winning the 2016 US presidential election. Similarly, with perfect predictive ability, I think the correlation between ex ante predicted work performance and ex post actual performance approach 1 (up to quantum) . This may seem like a minor technical point, but I think it’s important to be careful of the reasoning here when we ask whether claims are expected to generalize from domains with large and obvious track records and proxies (eg past paper citations to future paper citations) or even domains where the ex ante proxy may well have been defined ex post (Math Olympiad records to research mathematics) to domains of effective altruism where we’re interested in something like counterfactual/​Shapley impact*.

  2. There’s counterfactual credit assignment issues for pretty much everything EA is concerned with, whereas if you’re just interested in individual salaries or job performance in academia, a simple proxy like $s or citations is fine. Suppose Usain Bolt is 0.2 seconds slower at running 100 meters. Does anybody actually think this will result in huge differences in the popularity of sports, or percentage of economic output attributable to the “run really fast” fraction of the economy, never mind our probability of spreading utopia throughout the stars? But nonetheless Usain Bolt likely makes a lot more money, has a lot more prestige, etc than the 2nd/​3rd fastest runners. Similarly, academics seem to worry constantly about getting “scooped” whereas they rarely worry about scooping others, so a small edge in intelligence or connections or whatever can be leveraged to a huge difference in potential citations, while being basically irrelevant to counterfactual impact. Whereas in EA research it matters a lot whether being “first” means you’re 5 years ahead of the next-best candidate or 5 days.

  Griping aside, I think this is a great piece and I look forward to perusing it and giving more careful comments in the coming weeks!
  
  *ETA: In contrast, if it’s the same variable(s) that we can use to ex ante predict a variety of good outcomes of work performance across domains, then we can be relatively more confident that this will generalize to EA notions. Eg, fundamental general mental ability, integrity, etc.

• Linch 25 Mar 2021 21:50 UTC

  4 points

  in reply to: Pablo’s comment on: Some global catas­trophic risk estimates

  I think somewhat higher chance of users being alive than that, because of the big correlated stuff that EAs care about.

• Linch 23 Mar 2021 23:11 UTC

  39 points

  in reply to: Peter_Hurford’s comment on: EA Funds has ap­pointed new fund managers

  I disagree. I think there’s something wrong with that, inasmuch as global health donors can already directly defer to Givewell, and Elie’s public views on within-cause prioritization do not appear to be obviously different from Givewell’s.

• Linch 23 Mar 2021 21:47 UTC

  19 points

  in reply to: AslanP’s comment on: I scraped all pub­lic “Effec­tive Altru­ists” Goodreads read­ing lists

  Some of the books are also mandatory reading in American public school education, or mandatory reading in other countries/​institutions.

• Linch 23 Mar 2021 9:43 UTC

  15 points

  in reply to: Dale’s comment on: Please stand with the Asian diaspora

  The university’s defence was that on average asians had inferior personalities

  Can you mention the page number(s) and/​or quote the relevant sections somewhere? I tried skimming the 130 page pdf and didn’t see anything that alluded to this directly.

• Linch 23 Mar 2021 9:31 UTC

  4 points

  in reply to: evelynciara’s comment on: Progress Open Thread: March 2021

  Congrats!

• Linch 23 Mar 2021 9:18 UTC

  2 points

  in reply to: Robert Charles’s comment on: What are some his­tor­i­cal ex­am­ples of peo­ple and or­ga­ni­za­tions who’ve in­fluenced peo­ple to do more good?

  Sorry I don’t think I fully understand you. Can you rephrase?

• Linch 23 Mar 2021 3:12 UTC

  43 points

  in reply to: jsteinhardt’s comment on: Please stand with the Asian diaspora

  Though I wouldn’t be surprised if I found a better organization with more searching.. I’m definitely in the market for that if you have ideas.

  I don’t have direct ideas for the stated goal, but some brainstorming on the purpose of why you are interested in Asian advocacy might be fruitful? If you are interested in things that help Asian diaspora have better lives, have a wildly flourishing future, etc, I’d bet that the same general (human-focused) cause areas that EAs are interested in (scientific advancement, reducing existential synthetic biology and AI risk, etc) are in expectation better for Asian people than Laaunch or the other orgs above.
  
  If you want things that disproportionately benefit Asians (eg, because “I want to show support for Asians so I want to do basically the same thing I was planning to do anyway” is a bad look/​ serve poorly as an honest loyalty signal), I’d probably look into ways to improve health and other outcomes in countries with a lot of Asians, or affect a lot of Asians. Plausible targets include outdoor pollution, smoking cessation, deworming, and lead poisoning. I’d also more speculatively suggest donating to reduce nuclear risk, since I think the majority of potential flashpoints for nuclear risk is in Asia.
  
  If you want to donate to organizations that disproportionately benefit upper-middle class Asians in Western countries (because this is the relevant group of friends/​collaborators/​students/​etc that you wish to express solidarity to), I’m pretty stuck on ideas, yeah. I think there’s a fairly high difficulty in finding charities in this space with any nontrivial and positive tangible outcome (even more so than normal for charity selection).

  Speaking personally, I do think the impact of racism on me is nonzero and negative. But almost all of the experiences of racism in my adult life looks less like explicit and obvious racial prejudice and more like statistical disparate impact, in both my corporate and social life. In no individual case would there be obvious racism, but collectively a (murky) picture is painted. Eg, I have to apply to X jobs to get offers I’m happy with, whereas I suspect my demographic twin of a different race only have to apply to ~Y jobs to get the same number of offers, visa approvals to the US are harder for Asians than for Europeans*, stuff like that. This is only my own anecdotal experience, but I suspect it generalizes well to East Asian people** who are likely to be your friends/​coworkers/​etc.
  
  It seems pretty hard to meaningfully improve on the disparate impact stuff without a clear theory of change, and I don’t think there are obvious quick fixes (eg, I sure don’t want to work in any job that I have to sue to get!). I’d maybe weakly endorse a variation of Dale’s comment here and suggest organizations that lobby for greater standardization/​legibility in admittance to universities and prestige jobs (under the assumption that illegibility and informal systems almost always disproportionately benefit people with power, and harms minorities). But I’m hesitant to recommend donating to any specific group that works on this without at least doing some due diligence on their theory of change.
  
  Thinking farther afield, I’d also be interested in great power stuff and other things that mitigate potential future ethnic tensions.
  
  * And people who are ethnically Asian are more likely to be from Asian countries than from European countries.
  
  ** I’m less sure about generalization to eg, West Asians because I can imagine a lot of anti-Arab and Anti-Israeli sentiment that’s more direct.

• Linch 20 Mar 2021 19:58 UTC

  10 points

  in reply to: jsteinhardt’s comment on: Please stand with the Asian diaspora

  They (EDIT: Laaunch) seem to be doing a lot of different things and I’m confused as to what their theory of change is.
  
  (Tbc I only had a cursory look at their website so it’s possible I missed it).

• Linch 18 Mar 2021 17:17 UTC

  9 points

  in reply to: Meadowlark’s comment on: What Makes Outreach to Pro­gres­sives Hard

  Speaking descriptively, are most active leftists members of the working class rather than the PMC? My impression is that while many working-class people have implicitly leftist views on economics, the demographics that leftists predominantly draw from for activism is the highly educated PMC class, similar to EA.
  
  This impression can of course be wrong due to selection bias of who I end up talking with, so I’d personally find it valuable to correct for this bias!

• Linch 16 Mar 2021 18:53 UTC

  4 points

  in reply to: Linch’s comment on: The Im­por­tance of Ar­tifi­cial Sentience

  Relevant EA Forum question and thread.

• Linch 14 Mar 2021 5:14 UTC

  12 points

  on: Strong Ev­i­dence is Common

  I think in the real world there are many situations where (if we were to put explicit Bayesian probabilities on such beliefs, which we almost never do), beliefs with ex ante ~0 credence quickly get extraordinary updates. My favorite example is sense perception. If I woke up after sleeping on a bus and were to put explicit Bayesian probabilities on anticipating what I will see next time I open my eyes, then my belief I’d assign in the true outcome (ignoring practical constraints like computation and my near inability to have any visual imagery) has ~0 credence. Yet it’s easy to get strong Bayesian updates: I just open my eyes. In most cases, this should be a large enough update, and I go on my merry way.
  
  But suppose I open my eyes and instead see people who are approximate lookalikes of dead US presidents sitting around the bus. Then at that point (even though the ex ante probability of this outcome and that of a specific other thing I saw isn’t much different), I will correctly be surprised, and have some reasons to doubt my sense perception.
  
  Likewise, if instead of saying your name is Mark Xu, you instead said “Lee Kuan Yew”, I at least would be pretty suspicious that your actual name is Lee Kuan Yew.

  I think a lot of this confusion in intuitions can be resolved by looking at what MacAskill calls the difference between unlikelihood and fishiness:

  Lots of things are a priori extremely unlikely yet we should have high credence in them: for example, the chance that you just dealt this particular (random-seeming) sequence of cards from a well-shuffled deck of 52 cards is 1 in 52! ≈ 1 in 10^68, yet you should often have high credence in claims of that form. But the claim that we’re at an extremely special time is also fishy. That is, it’s more like the claim that you just dealt a deck of cards in perfect order (2 to Ace of clubs, then 2 to Ace of diamonds, etc) from a well-shuffled deck of cards.

  Being fishy is different than just being unlikely. The difference between unlikelihood and fishiness is the availability of alternative, not wildly improbable, alternative hypotheses, on which the outcome or evidence is reasonably likely. If I deal the random-seeming sequence of cards, I don’t have reason to question my assumption that the deck was shuffled, because there’s no alternative background assumption on which the random-seeming sequence is a likely occurrence. If, however, I deal the deck of cards in perfect order, I do have reason to significantly update that the deck was not in fact shuffled, because the probability of getting cards in perfect order if the cards were not shuffled is reasonably high. That is: P(cards not shuffled)P(cards in perfect order | cards not shuffled) >> P(cards shuffled)P(cards in perfect order | cards shuffled), even if my prior credence was that P(cards shuffled) > P(cards not shuffled), so I should update towards the cards having not been shuffled.

  Put another way, we can dissolve this by looking explicitly at Bayes’ theorem. $P\left(Hypothesis|Evidence\right)=\frac{P\left(Evidence|Hypothesis\right)\ast P\left(Hypothesis\right)}{P\left(Evidence\right)}$

  and in turn, $P\left(Evidence\right)=P\left(Evidence|Hypothesis\right)\ast P\left(Hypothesis\right)+P\left(Evidence|OtherHypotheses\right)\ast P\left(OtherHypotheses\right)$
  
  $P\left(Evidence|Hypothesis\right)$ is high in both the “fishy” and “non-fishy” regimes. However,$P\left(Evidence|OtherHypotheses\right)$ is much higher for fishy hypotheses than for non-fishy hypotheses, even if the surface-level evidence looks similar!