Bio

Participation
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CE/AIM Research Training Program graduate and research contractor at ARMoR under the Global Impact Placements program, working on research & quantitative modeling to support policy advocacy for market-shaping tools to help combat AMR, and also exploring similar "decision guidance" roles e.g. applied prioritization research. Previously supported by a FTX Future Fund regrant and later Open Philanthropy's affected grantees program. Before that I spent 6 years doing data analytics, business intelligence and knowledge + project management in various industries (airlines, e-commerce) and departments (commercial, marketing), after majoring in physics at UCLA. I've also initiated some local priorities research efforts, e.g. a charity evaluation initiative with the moonshot aim of reorienting Malaysia's giving landscape towards effectiveness, albeit with mixed results. 

I first learned about effective altruism circa 2014 via A Modest Proposal, a polemic on using dead children as units of currency to force readers to grapple with the opportunity costs of subpar resource allocation under triage. I have never stopped thinking about it since, although my relationship to it has changed quite a bit; I related to Tyler's personal story (which unsurprisingly also references A Modest Proposal as a life-changing polemic):

I thought my own story might be more relatable for friends with a history of devotion – unusual people who’ve found themselves dedicating their lives to a particular moral vision, whether it was (or is) Buddhism, Christianity, social justice, or climate activism. When these visions gobble up all other meaning in the life of their devotees, well, that sucks. I go through my own history of devotion to effective altruism. It’s the story of [wanting to help] turning into [needing to help] turning into [living to help] turning into [wanting to die] turning into [wanting to help again, because helping is part of a rich life].

Comments
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Topic contributions
3

Nicolaj correct me if I'm wrong – I think it's derived here in the OP:

(Quantitatively it would be captured by  when combined with the improving circumstances component. That comes from solving the last equation in Rethink Priorities’ 2023 report for  given  and —i.e., assuming that the compounding non-monetary benefits factor also reflects diminishing marginal utility from income doublings. As a result I'm assuming the discount rate reflects  for the remainder of the post.)

That last equation on pg 48 is 𝑟_𝐺𝑖𝑣𝑒𝑊𝑒𝑙𝑙 = (1 + δ)(1 + 𝑔)^(η−1) − 1. δ is the pure time preference rate, for which GiveWell's choice is δ = 0%; pg 30 in the RP report above summarizes the reasoning behind this choice. 

Maybe 

Other scattered remarks

Perhaps the virtue ethicist part of you may feel partly assuaged by GiveDirectly's blog post about the project? I'm thinking in particular of these sections (warning - long quotes):

 GiveDirectly confirmed recipients and communities want to be featured, as always:  

  • For all media projects, we first consult with village leadership to confirm their interest and consent for participating. For this video, we also met with local and national government officials to confirm if they were supportive of such a large spotlight. 
  • Journalists and content creators always follow this guidance when visiting GiveDirectly programs. Profiled recipients first give informed consent before sharing their story. You can read our consent forms here→

Beast Philanthropy centered the local culture:

  • They regularly solicited input from our local staff about whether approaches and portrayals would be received well by the community and had us give notes on the video edit. 
  • They focused on English-speakers so recipients could share more of their story in their own voice. 
  • They worked to capture the cultural specificity of the community, forgoing stock music for natural sounds→

After filming, GiveDirectly’s safeguarding team interviewed 9 of the filmed recipients. You can read their feedback here – some highlights:

Recipients enjoyed being on camera.

  • “The way they came and interacted with me and my family, that’s what I liked most. I felt in place and free with them.”
  • “I was very happy and I welcomed them. I showed them my land agreement together with the land, iron sheets (for my new roof) and some household materials.” 

Their motivations for participating varied.

  • “I did accept to participate because of the challenges and poverty that my community members are facing. I needed to represent their views.”
  • “I needed to tell how happy I felt and also to show the rest of the community members that when given something small or large you can always use it in a way that can help raise your standard of living.”

Two gave us actionable feedback for how we can improve next time.

  • “I was relaxed and very happy, though my husband got anxious about the number of GiveDirectly staff who visited us.”
  • “I felt good about it, though I feel I should also be shown the photos and videos to watch.”

Later this month, we’ll screen the video for the featured community dubbed into Nga’Karimojong (their language), followed by a focus group discussion, then update this blog with their thoughts on the final video.

This was to me a surprising amount of beneficiary thoughtfulness for a MrBeast video (admittedly I don't watch his content often), albeit in line with my expectations for GiveDirectly.

Upvoted :) 

I agree with Ben Millwood's comment that I don't think this would change many decisions in practice. 

To add another point, input parameter uncertainty is larger than you probably think, even for direct-delivery GHD charities (let alone policy or meta orgs). The post Quantifying Uncertainty in GiveWell Cost-Effectiveness Analyses visualises this point particularly vividly; you can see how a 10% change doesn't really change prioritisation much:

InterventionGiveWellOur Mean95% CIDiference
Against Malaria Foundation0.03750.03840.0234 - 0.0616+2.4%
GiveDirectly0.003350.003590.00167 - 0.00682+7%
Helen Keller International0.05410.06110.0465 - 0.0819+12.8%
Malaria Consortium0.0310.03180.0196 - 0.0452+2.52%
New Incentives0.04580.05210.0139 - 0.11713.8%

(Look at how large those 95% CIs are vs a 10% change.)

I think a useful way to go about this is to ask, what would have to change to alter the decisions (e.g. top-recommended charities, intervention ideas turned into incubated charities, etc)? This gets you into uncertainty analysis, to which I'd point you to froolow's Methods for improving uncertainty analysis in EA cost-effectiveness models.

The ARC Prize website takes this definitional stance on AGI:

Consensus but wrong:

AGI is a system that can automate the majority of economically valuable work.

Correct:

AGI is a system that can efficiently acquire new skills and solve open-ended problems.

Something like the former definition, central to reports like Tom Davidson's CCF-based takeoff speeds for Open Phil, basically drops out of (the first half of the reasoning behind) the big-picture view summarized in Holden Karnofsky's most important century series: to quote him, the long-run future would be radically unfamiliar and could come much faster than we think, simply because standard economic growth models imply that any technology that could fully automate innovation would cause an "economic singularity"; one such technology could be what Holden calls PASTA ("Process for Automating Scientific and Technological Advancement"). In What kind of AI? he elaborates (emphasis mine)

I mean PASTA to refer to either a single system or a collection of systems that can collectively do this sort of automation. ...

By talking about PASTA, I'm partly trying to get rid of some unnecessary baggage in the debate over "artificial general intelligence." I don't think we need artificial general intelligence in order for this century to be the most important in history. Something narrower - as PASTA might be - would be plenty for that. ...

I don't particularly expect all of this to happen as part of a single, deliberate development process. Over time, I expect different AI systems to be used for different and increasingly broad tasks, including and especially tasks that help complement human activities on scientific and technological advancement. There could be many different types of AI systems, each with its own revenue model and feedback loop, and their collective abilities could grow to the point where at some point, some set of them is able to do everything (with respect to scientific and technological advancement) that formerly required a human.

This is why I think it's basically justified to care about economy-growing automation of innovation as "the right working definition" from the x-risk reduction perspective for a funder like Open Phil in particular, which isn't what an AI researcher like Francois Chollet cares about. Which is fine, different folks care about different things. But calling the first definition "wrong" feels like the sort of mistake you make when you haven't at least good-faith effort tried to do what Scott suggested here with the first definition: 

... if you're looking into something controversial, you might have to just read the biased sources on both sides, then try to reconcile them.

Success often feels like realizing that a topic you thought would have one clear answer actually has a million different answers depending on how you ask the question. You start with something like "did the economy do better or worse this year?", you find that it's actually a thousand different questions like "did unemployment get better or worse this year?" vs. "did the stock market get better or worse this year?" and end up with things even more complicated like "did employment as measured in percentage of job-seekers finding a job within six months get better" vs. "did employment as measured in total percent of workforce working get better?". Then finally once you've disentangled all that and realized that the people saying "employment is getting better" or "employment is getting worse" are using statistics about subtly different things and talking past each other, you use all of the specific things you've discovered to reconstruct a picture of whether, in the ways important to you, the economy really is getting better or worse.

Note also that PASTA is a lot looser definitionally than the AGI defined in Metaculus' When will the first general AI system be devised, tested, and publicly announced? (2031 as of time of writing), which requires the sort of properties Chollet would probably approve (single unified software system, not a cobbled-together set of task-specialized subsystems), yet if the PASTA collective functionally completes the innovation -> resources -> PASTA -> innovation -> ... economic growth loop, that would already be x-risk relevant. The argument would then need to be "something like the Chollet's / Metaculus' definition is necessary to complete the growth loop", which would be a testable hypothesis.

AMF does. Quoting Rob Mathers' (AMF CEO) recent post, emphasis mine:

Many recognise the impact of AMF’s work, yet we still have significant immediate funding gaps that are over US$300m. ...

There is already a significant shortfall in funding for malaria control activities, including for net distribution programmes so miraculous things will have to happen in the coming year if we are to get anywhere close, globally and across all funding partners, to where we need to be to be able to drive malaria impact numbers down. Counterfactually of course, if the funding that is being brought to bear was not there, the number of people affected by malaria would be horrifically higher. Currently there are ~620,000 deaths a year from malaria and 250 million people fall sick. 

The Global Fund is the world’s largest funder of malaria control activities and has a funding replenishment round every three years, with funding provided by global governments, that determines the funds it has available across three disease areas: HIV/Aids, malaria and TB. The target for the 2024 to 2026 period was raising US$18 billion, largely to stand still. The funding achieved was US$15.7 billion. The shortfall will have major ramifications and we are already seeing the impact in planning in the Democratic Republic of Congo, one of the two countries in the world worst affected by malaria, for the 2024 to 2026 programme. Currently only 65% of the nets desperately needed will be able to be funded. We have never had this low a percentage of funding at this stage, with limited additional funding forecast.

The latest actual publicly-available RFMF figure I can find for AMF, and the other top GW charities, is here from Q3 2020, which is probably what you're referring to in the OP by "It's hard to find up-to-date data"; back then it was just $37.8M, nearly an order of mag lower, although I'm not sure whether Rob's and GiveWell's RFMF figures are like for like.

The justifications for these grants tend to use some simple expected value calculation of a singular rosy hypothetical casual chain. The problem is it's possible to construct a hypothetical value chain to justify any sort of grant. So you have to do more than just make a rosy casual chain and multiply numbers through.

Worth noting that even GiveWell doesn't rely on a single EV calculation either (however complex). Quoting Holden's 10 year old writeup Sequence thinking vs. cluster thinking:

Our approach to making such comparisons strikes some as highly counterintuitive, and noticeably different from that of other “prioritization” projects such as Copenhagen Consensus. Rather than focusing on a single metric that all “good accomplished” can be converted into (an approach that has obvious advantages when one’s goal is to maximize), we tend to rate options based on a variety of criteria using something somewhat closer to (while distinct from) a “1=poor, 5=excellent” scale, and prioritize options that score well on multiple criteria.

We often take approaches that effectively limit the weight carried by any one criterion, even though, in theory, strong enough performance on an important enough dimension ought to be able to offset any amount of weakness on other dimensions. 

... I think the cost-effectiveness analysis we’ve done of top charities has probably added more value in terms of “causing us to reflect on our views, clarify our views and debate our views, thereby highlighting new key questions” than in terms of “marking some top charities as more cost-effective than others.”

I'd be interested to see explanations from the disagree-voters (even short ones would be useful). Was it the proposed renaming? The description draft? Something else? 

Yeah I roll to disbelieve too. One of my quantitative takeaways from Andrew Gelman's modelling of the 2020 elections was that very few states (4/50, in particular New Hampshire, Pennsylvania, Wisconsin, and Michigan) were modelled as close enough that p(one vote changes outcome) > 1 in 10 million; New Hampshire tops the list at 1 in 8M. Optimistically assuming $100 per voter that's still nearly a billion dollars at the very low end; a more realistic estimate would probably be ~1 OOM higher. Probably some sort of nonlinearity kicks in at this scale, or the most cost-effective tactics to sway voters cap out at relatively low levels for whatever reason?

On the flip side, I'm reminded of Scott's essay Too much dark money in almonds? which provides an intuition pump for why it might be the case that it's not as expensive as you may expect to swing the election:

Everyone always talks about how much money there is in politics. This is the wrong framing. The right framing is Ansolabehere et al’s: why is there so little money in politics? But Ansolabehere focuses on elections, and the mystery is wider than that. ... 

(in case you’re keeping track: all donations to all candidates, all lobbying, all think tanks, all advocacy organizations, the Washington Post, Vox, Mic, Mashable, Gawker, and Tumblr, combined, are still worth a little bit less than the almond industry. And Musk could buy them all.) ... 

In this model, the difference between politics and almonds is that if you spend $2 on almonds, you get $2 worth of almonds. In politics, if you spend $2 on Bernie Sanders, you get nothing, unless millions of other people also spend their $2 on him. People are great at spending money on direct consumption goods, and terrible at spending money on coordination problems.

(I don't really have an opinion either way on whether more or less money should be spent on this)

[Caveat that I don't know anything else about this] 

I recall Rob Wiblin's 80K article on voting referencing this summary table from the 2015 edition of Get Out The Vote claiming "$30-100 or a few hours of work as a volunteer" to "persuade one stranger to vote for your preferred candidate", a lot lower than the OP's claimed figures, and that even adjusting upwards for various factors doesn't worsen this by more than an OOM. 

Is it important to get others to vote? Here is a table of cost-effectiveness estimates of  various interventions to get out the vote.

(That said, I regard these figures basically the same way GW treats the best cost-effectiveness estimates in the DCP2/3) 

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