Bio

Participation
4

I currently work with CE/AIM-incubated charity ARMoR on research distillation, quantitative modelling and general org-boosting to support policy advocacy for market-shaping tools to incentivise innovation and ensure access to antibiotics to help combat AMR

I previously did AIM's Research Training Program, was supported by a FTX Future Fund regrant and later Open Philanthropy's affected grantees program, and 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 and changing my mind about becoming a physicist. I've also initiated some local priorities research efforts, e.g. a charity evaluation initiative with the moonshot aim of reorienting my home country Malaysia's giving landscape towards effectiveness, albeit with mixed results. 

I first learned about effective altruism circa 2014 via A Modest Proposal, Scott Alexander's 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].

How others can help me

I'm looking for "decision guidance"-type roles e.g. applied prioritization research.

How I can help others

Do reach out if you think any of the above piques your interest :)

Comments
173

Topic contributions
3

I thought it'd be helpful to improve comparability with LChamberlain's answer on Sinergia Animal by being a bit more object-level: 

  • Saulius previously estimated that a dollar donated to support corporate cage-free commitments historically helped free somewhere between 9 to 120 hens from cages, with a mean of 42 hens; since their average lifespan is 1.1–1.5 years that's 54 years of improved life per dollar. Vasco then discounts this by -80% going forward to incorporate Open Phil's thinking that "we’ve covered many of the strongest opportunities in this space, and we think that current marginal opportunities are considerably weaker" to get ~8.4 hens freed per dollar donated
  • From LChamberlain's answer, ACE estimates Sinergia's work to free 53.5 hens per dollar, which is >6x Vasco's estimate for (presumably) cage-free campaigns in general and slightly above Saulius' average

I'd be curious to understand how much of this ~6x difference is a 'best charity implementing an intervention' thing (which I'm guessing is what Omnizoid asked for? Akin to how cost per person treated is the most important consideration for MH treatment interventions), and how much is due to differences in methodology, considerations included/excluded (e.g. OP's "current marginal opportunities are considerably weaker"), inputs, etc.

I appreciated these parts of Jason's article, and am curious if others have a different take:

What to do?

The article recommends that humanity avoid creating mirror bacteria, even as a scientific experiment, no matter how tight the biosecurity around it (which can never be perfect). Funders should not fund such research; governments should even ban it.

This is a simple cost-benefit calculation. On the cost side, the threat is plausible, and the potential damage incalculable. Thus, the risk is immense. On the benefit side, there is no crucial goal for humanity that is known to be enabled by mirror life. Restricting this research would not fundamentally impede progress in biology or bioengineering generally.

Not all forms of mirror biology would even need to be restricted. For instance, there are potential uses for mirror proteins, and those can be safely engineered in the lab. The only dangerous technologies are the creation of full mirror cells, and certain enabling technologies which could easily lead to that (such as the creation of a full mirror genome or key components of a proteome).

In short, by pruning off a relatively small branch of the tech tree, we can avoid a true existential risk.

The article also recommends research to develop surveillance and countermeasures, in case humanity ever does encounter mirror bacteria. This research can be advanced significantly without creating full mirror cells.

We have time to react

Given that the threat is relatively distant, no immediate action is needed. We have time to discuss it thoroughly, among a wider set of participants. The article released today is meant to be the beginning of that wider conversation, not a call to urgent action.

I like Austin Vernon's idea for scaling CO2 direct air capture to 40 billion tons per year, i.e. matching our current annual CO2 emissions, using (extreme versions of) well-understood industrial processes.  

The proposed solution may not be the cheapest out there. Other ideas like ocean seeding or olivine weathering might be less expensive. But most of the science is understood, and it can scale quickly. I'd guess 100,000 workers could build enough sites to capture our 40 billion tons goal in a decade. The capital expenditure rate would be between $1 trillion and $5 trillion yearly, or 1% to 5% of global GDP. That cost and deployment speed take doomer scenarios off the table. Say something scary like melting permafrost threatens runaway warming. You can target the area with a few years of sulfur cooling while a tiny portion of the global economy builds carbon capture devices. It is nothing like a wartime mobilization.

The most disruptive aspect would be energy usage. We'd need to ramp output up at double-digit rates because each ton of CO2 requires 2-3 MWh of energy for removal. Thankfully low-grade heat is easy to come by. There is enough energy near coal mines in Wyoming or natural gas fields in SW Pennsylvania at less than $5/MWh. Other places might use solar, hydro, or geothermal steam if they lack fossil fuel reserves. The key is to put the facilities at the energy sources instead of trying to move the energy. Cheap energy makes the operating costs <1% of global GDP. Many clean energy proponents have fretted about how to keep fossil fuel reserves in the ground. Burning them to run carbon capture equipment kills two birds with one stone!

The takeaway is that we could completely turn around the carbon dioxide problem within a few years with a similar spending rate as rich world COVID relief. There won't be a scenario where we've waited too long to act.

I am admittedly perhaps biased to want moonshots like Vernon's idea to work, and for society at large to be able to coordinate and act on the required scale, after seeing these depressing charts from Assessing the costs of historical inaction on climate change:

figure 2
figure 5

I'd add Maternal Health Initiative is Shutting Down by Ben Williamson and Sarah Eustis-Guthrie. Their Asterisk article Why we shut down is great too.

On an individual level I appreciate things like Scott Alexander's Mistakes list, pinned at the top of his blog, on "times I was fundamentally wrong about a major part of a post and someone was able to convince me of it". I'd appreciate it if more public intellectuals did this.

Pretty funny CGD blog post by Victoria Fan and Rachel Bonnifield: If the Global Health Donors Were Your Parents: A (Whimsical) Comparative Perspective. Quoting at length (with some reformatting):

Navigating the global health funding landscape can be confusing even for global health veterans; there are scores of donors and multilateral funding mechanisms, each with its own particular structure, personality, and philosophy. For the uninitiated, PEPFAR, GAVI, PMI, WHO, the Global Fund, UNITAID, and the Gates Foundation can all appear obscure and intimidating. But if your head is spinning from acronym-induced vertigo, fear not! We are here to help you make sense of it all. How, you ask? With a clear method for donor identification: comparing the donors to your parents. So what would happen if the donors were your parents and you asked them for a new car? 

  • PEPFAR: Ok, we’ll buy you a new car, but we’re going with you to the dealership and it must be American-made. At least one seat must be devoted to abstinence and the delay of sexual debut. Before you drive the car, you must promise not to support prostitution. Each quarter, you must report how many miles you’ve driven with how many passengers, with a target of 1000 passenger-miles per month.
  • President’s Malaria Initiative: We’ve made it very clear that we only support four proven, cost-effective interventions for child rearing: food, clothing, health care, and education. What, do you think money in the Malaria family just grows on trees? Just because HIV/AIDS has a shiny new car doesn’t mean we can afford it.
  • UNITAID: We’ve identified pediatric vehicles as a niche market which is currently underserved by the major transport providers. By buying cars for you and all our other children, we are helping to create a pediatric automotive market with new and superior transportation commodities. Prior to our innovative entry into the pediatric vehicle market, most of our potential beneficiaries were getting around using lower-quality forms of transportation, such as bicycles, buses, and walking.
  • GAVI: We will purchase and a deliver a car for you from a particular GAVI-approved dealership. However, you must co-finance the purchase with wages from your part-time job. Gas and insurance will require separate applications.
  • WHO: Sorry, we haven’t had a car budget in ten years. But we DO have a new set of guidelines on best practices for safe car driving, and a box full of old carfax vehicle reports that you’re welcome to look at any time. Please let us know right away if you experience any engine trouble; regular and reliable reporting allows us to maintain an up-to-date transmission failure surveillance system. And don’t forget to celebrate Vehicle Safety Day on May 11!
  • Gates Foundation: Of course, darling, we gave your boarding school plenty of money to buy a car. And since we’re on the Board, we’ll make sure they buy the right car. And you can drive it any time you want…as long as one of us is in the passenger seat to make sure you’re going the right way.
  • Global Fund: We’ve reviewed your proposal for a Range Rover and according to Consumer Reports it is a technically capable car for city driving. Here is a $70,000 check for you to go and buy the Range Rover, as discussed in your proposal.

Oliver Kim's How Much Should We Trust Developing Country GDP?, a review of Morten Jerven's 2013 book Poor Numbers: How We Are Misled by African Development Statistics and What to Do About It, makes the same point about GDP as well. Improving data collection in underresourced areas in general seems like a cross-cutting 'cause X'.

Some quotes: 

Hollowed out by years of state neglect, African statistical agencies are now often unable to conduct basic survey and sampling work. Jerven writes:

In 2010, I returned to Zambia and found that the national accounts now were prepared by one man alone… Until very recently he had had one colleague, but that man was removed from the National Accounts Division to work on the 2010 population census. To make matters worse, lack of personnel in the section for industrial statistics and public finances meant that the only statistician left in the National Accounts Division was responsible for these data as well. (pg. x)

Without the staff to collect and analyze survey data, statistical agencies are usually forced to improvise, guessing the size of the economy from population figures, which are themselves extrapolated from censuses that are decades-old.

... I’m haunted by the words of the lone Zambian statistician, sitting in his empty office, who asks Jerven plaintively: “What happens if I disappear?”


Many African states are failing at the basic task of knowing how many people live in their borders—let alone accurately measuring their economic activity. The vast, unobserved informal sector (which includes subsistence farming, and something like 60% of working people) is usually estimated just as a direct function of population.5 Lacking direct harvest yields, estimates of agricultural output are often produced using FAO models based on planting-season rainfall data.6 Even the minimal task of measuring the goods traveling across borders—in theory, the easiest thing for a sovereign state to accomplish—is occasionally beyond the reach of statistical agencies. Until 2008, landlocked Uganda only collected trade data on goods that eventually passed through the Kenyan port of Mombasa, ignoring the four other countries on its borders.7

In the absence of good underlying data, the prevailing approach for GDP in developing Africa can be summarized as:

Income estimates… derived by multiplying up per capita averages of doubtful accuracy by population estimates equally subject to error. (p. 39)


Poor Numbers came out in 2013, attracting a wave of scholarly and policy attention (including by Bill Gates). Once you’ve heard its arguments, it’s virtually impossible to look at a GDP statistic the same way again.

But what actual progress has been made in the statistical capacity of nations?

Seemingly, not much. In late 2014, perhaps in response to Jerven’s book, the World Bank relaunched its website for its Statistical Capacity Indicator—a metric on a 0-100 scale which scores countries based on the strength of their “Methodology”, “Source Data”, and “Periodicity and Timeliness”. But even by this clunky internal metric, progress has been glacially slow: in 2004, the average score for African countries was 58.2; in 2019, it was just 61.4.

Moreover, over six years of an economics PhD, I have never heard of any economist using this statistic. Poor Numbers is well-cited and well-read (at least by Africa specialists), but its lessons about the fundamental unreliability of statistics have largely not been absorbed in how we actually do economics.

It's in Table 3. I do agree this should be visualised as well.

Makes sense. Their analysis treats large donors differently, although they don't mention anything about retention differences vs other pledgers. Given that GWWC say they "often already have individual relationships with them", my guess is it's probably slightly higher.

There's this chart from the What trends do we see in GWWC Pledgers’ giving? subsection of GWWC's 2020-22 cost-eff self-evaluation:

Joel's comment on this is worth reading too.

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