BOTEC of the day -- some charts on the energy use of agentic AI by Zeke Hausfather:

If you're a heavy agentic AI user and this prompts you to offset CO2eq emitted via donations and you're wondering how much to donate, here's yet another BOTEC-ed table for reference, courtesy of Scott Alexander. 

For instance, I think my token consumption is (ballpark) an order of mag lower than Zeke. That's 7 cheeseburgers, so I'd offset a year of my AI usage by donating optimistically ~$0.60 to Native Energy (to pay people in 3rd world countries to not cut down trees, with all the ways that ToC can fail) or if I wanted more confidence, ~$40 to Climeworks (to suck CO2 out of the air and stick it into the ground). $40 is $3.30 per month, which seems like a small premium to pay on top of my AI subscription.

A key uncertainty is true per-token energy consumption, which nobody knows. If I were pessimistic and really wanted to cover my bases with the offset donations, the 25% cache reads assumption implies doubling my energy consumption estimate, so 14 cheeseburgers or $80 to Climeworks.

(To be clear I don't really eat cheeseburgers, so eating 14 fewer of them in a year isn't really an option available to me. Also the Climeworks estimate is from 2021, I wouldn't be surprised if they've gotten more cost-effective since at capturing and storing CO2, so the pessimistic donation amount may be much lower)

You might be interested in Quantifying Uncertainty in GiveWell Cost-Effectiveness Analyses (2022)

 

Intervention	GiveWell	Our Mean	95% CI	Diference
Against Malaria Foundation	0.0375	0.0384	0.0234 - 0.0616	+2.4%
GiveDirectly	0.00335	0.00359	0.00167 - 0.00682	+7%
Helen Keller International	0.0541	0.0611	0.0465 - 0.0819	+12.8%
Malaria Consortium	0.031	0.0318	0.0196 - 0.0452	+2.52%
New Incentives	0.0458	0.0521	0.0139 - 0.117	13.8%

as well as Methods for improving uncertainty analysis in EA cost-effectiveness models (also 2022, it was a good year for this stuff)

The graph below shows the costs and benefits of each GiveWell top charity on a log scale (except New Incentives, which I ran out of time to add). The dotted black line shows the cost-effectiveness of the most cost-effective charity; anything above this line is less effective than this charity and anything below this line is more effective than this charity. On the left is effectively GiveWell’s published conclusions – the base case analysis of my refactoring of their model. You can see it recommends Sightsavers, HKI and AMF as the most cost-effective charities, with very little to pick between them. On the right is the same graph with full uncertainty analysis added (see Section 4 for details on the ‘PSA’ technique). This shows that any charity except GiveDirectly could potentially be the most cost-effective, with varying degrees of plausibility (and GiveDirectly could easily be most cost-effective if we add in some risk adjustment, as we do in Section 4).

This is important because it shows that without uncertainty analysis, we are at a material risk of picking the wrong charities to fund. 

... By means of a broad conclusion, uncertainty analysis is supportive of GiveWell’s current approach of not simply donating to the charity with the highest cost-effectiveness ratio in the base case model. There is a high degree of uncertainty surrounding this model and any charity in GiveWell’s top recommendations has the potential to be cost-effective within the bounds of some very reasonable sensitivity analysis. The table below suggests some plausible scenarios by which each charity might be the most – or at least significantly more – cost-effective

Thanks for the corrections. Agree there's plenty of room for debate re: objective function, I generally think people don't take this seriously enough (Nuno Sempere's estimating value series is what I have in mind by "take it seriously"). 

Richard Ngo's Towards a Formal Scientific Epistemology sketches out the beginnings of an alternative to Bayesian epistemology in case you're interested. I am not the right person to field questions about it unfortunately, I'll just quote his intro:

In my post “Why I’m not a Bayesian”, I argued that the Bayesian approach of assigning credences to propositions with binary truth values only works in simple and restricted domains. Instead, I claimed, a better approach to epistemology is to assign degrees of truth to models of the world.

This approach is broadly inspired by science, which is the domain from which we have the most evidence about which epistemological practices allow us to solve very hard problems. We don’t currently have a complete theory of scientific epistemology, but we can identify some important differences between scientific epistemology and Bayesian epistemology. Central examples of Bayesian epistemology (such as Solomonoff induction) assume that the truth lies within the class of hypotheses being considered. Conversely, in central examples of scientific research, the truth is definitely not already under consideration: the main problem is to come up with any hypothesis that explains existing data.

Another way of putting this point: Bayesian epistemology is entirely about empirical updates, whereas science is mostly about the process of constructing new theories. In some cases, once you’ve constructed a theory, you can be confident that it’s close to the truth merely from how well it fits existing data. But scientific theories are only fully accepted after they make successful advance predictions. That’s another difference compared with Bayesian epistemology, which treats retrodictions as equivalent to predictions.

In general I think scientific epistemology is far superior as a guide for thinking about difficult problems (like AI alignment) than Bayesian epistemology. However, scientific epistemology has mostly been described informally—e.g. by Popper, Kuhn, Feyerabend, etc. Popper did attempt to formally define a metric for degrees of truth, but it wasn’t very successful. I’d like to be able to describe scientific epistemology as formally as we can describe Bayesian epistemology (and ideally to unify them in a single framework).

I interpret his follow-up essay Agents as webs of beliefs: Unifying beliefs, goals, and actions as sketching out the bigger picture.

Thanks Anthony. Would it be fair to interpret your unawareness series as your steelman of OP's post title, or as being relevant?

I couldn't find on a quick look what decision-making approach you would (at least provisionally) endorse instead, bracketing maybe? For my own reference later:

Bracketing says to base our decisions on those consequences we are – in a precise sense – not clueless about, “bracketing out” the others. The idea is that the effects we’re clueless about should not override the obligations given to us by the benefits we aren’t clueless about, like the immediate benefits of malaria nets to the global poor. Thus bracketing could provide action-guidance in the face of cluelessness and in particular support neartermism.

... Take Mogensen's (2020) example of deciding whether to donate to the Against Malaria Foundation (AMF) or the Make-a-Wish Foundation (MAWF). As Mogensen argues, you’re clueless about the overall effects of donating to AMF vs. MAWF, due to their highly ambiguous effects on population dynamics, economic growth, resource usage, etc.  You can come up with lots of asymmetrical effects each intervention has on total value, and you don’t have any principled way of weighing them up, but they still may swamp the immediate effects. Thus it seems that each of the available actions – Donate to AMF, Donate to MAWF, or Do Nothing – is permissible.

And yet, if you’re like me, you suspect that even an impartial consequentialist ought to choose AMF. For we aren’t clueless about the effects of our actions on the immediate beneficiaries! Restricting attention to those who would be prevented from contracting malaria by an AMF donation and the child who would be granted a wish by an MAWF donation, we can rank our actions by their expected total value: AMF  MAWF  Do Nothing. So my thought is, “Those immediately affected, who I’m not clueless about, give me a reason to Donate to AMF. It is true that, once I start accounting for more moral patients, I’ll become clueless about total value. But my cluelessness about this enlarged set of patients does not override the reasons given to me by the immediate beneficiaries. So, still, I’m required to Donate to AMF.” And that's the essence of bracketing.

I would be particularly interested in how you think meta- and/or longtermist-oriented grantmaking could be improved by your work. I have not been very impressed by the reasoning behind some of these (sometimes quite large) grants, at least on the rare occasions they've been shared publicly. 

The post title somewhat confuses me since reasons 2, 4, and 5 (miscalibration, anchoring cascades, and granularity failure at the tails) are in-paradigm critiques.

I'm also confused by the link to the XPT tournament as if it illustrates the assertion that EAs take Bayesian reasoning too far, given it did in fact include domain experts and given its headline finding that the forecasts were discrepant between the groups and failed to converge after structured persuasion. Same confusion re: linked GPI paper. 

I do think many of your points are correct. The strongest argument to your post title I can think of is sparse evidence + multiple models and you're uncertain between them -> precise priors are unwarranted -> use an interval instead, possibly quite wide like [10⁻⁶, 0.5] (just to make something up) -> updating may not collapse this wide interval -> so EV-maxxing becomes undefined -> so switch to other decision criteria, e.g. maybe robustness to harms, which is less Bayesian as per your post title -> choose robustly good actions, maintain option value, build capacity etc. Which is basically what most meta interventions are about, no?

As someone who spent years building performance tracking dashboards for executive teams (albeit in the pre-ChatGPT dark ages) may I just say that https://firstembrace.org.uk/impact is a banger dashboard

I hear you on the mis-aimed backlash. I also interpret what she's doing as fundamentally capabilitarian, which is underappreciated in these parts. 

Thoughts on how the calculator might more fairly represent what MacKenzie Scott's giving aims to achieve? I can ask Claude to create a mockup based on your bullets :) 

Good review on the ACX blog of Masooda Bano's 2012 book Breakdown in Pakistan, based on ethnographic case studies, interviews, and surveys of her home country (she's a professor at Oxford). Very worth reading if it hasn't yet come across your Substack feed. Here are some long quotes to whet your appetite.

Vividly shocking opener:

A couple of decades ago in Kabul, a fellow aid worker on his third beer came up with a vivid simile for what American money was doing to the Afghan organizations implementing USAID projects. “It’s like gluing a rabbit mouth-first to a fire hose. You blast its guts right out of its ass…but as long as the water keeps running, it looks like you’ve taught the rabbit to dance.”

Oxford politics professor Masooda Bano uses less lurid language, but Breakdown in Pakistan addresses the same basic phenomenon: how Western aid donors and INGOs unintentionally eviscerate so many of the local institutions they try to “capacity-build.” The problem goes beyond the obvious mismatch of huge funds with small-scale organizations. Even low-to-moderate external funding can leave an organization hollowed out, bereft of local participants and the power it once had to motivate change on the ground.  

... Overall, I came to recognize a global machine that systematically:

• sought out organizations with high capacity to mobilize local collective action;
• gutted them of that capacity, in exchange for a new role as professional project implementers and grant managers;
• tried to build new project-specific village committees as an artificial (and desperately inadequate) substitute; and finally
• withdrew funding, leading to the collapse of both the previous institutions and the ersatz ones we’d built up in their place.

We were late-stage German foresters, belatedly trying to import an ant colony or fungi after our monoculture had already devastated the soil ecology.

Why would even low-to-moderate external funding hollow out local organizations? 

Voluntary membership organizations, like these voluntary organizations (VOs) had been, are the ideal type of what we call civil society: institutions funded by donations (rather than sales or taxes), operating based on people freely coming together for some shared cause (rather than to make money or obey a government mandate). The foreign aid industry uses “civil society organization” as a virtual synonym for NGO. In reality, aid-funded groups tend to operate rather more like corporate and/or government contractors: paid service providers with comfortably salaried staff, focused on the goals of faraway clients, whose main unifying cause is “whatever helpful thing they pay us to do.”

And as it turns out, that model severely demotivates volunteers. Oxfam’s “capacity-built” Sindhi organizations were certainly non-governmental, but they were no longer voluntary—NGOs, but not VOs. Bano found the same pattern in the 2000s, in a survey of forty successful Pakistani civil society organizations. Of the twenty that implemented foreign-aid-funded projects, none were able to mobilize local volunteers (beyond some interns hoping to graduate to paid employment) and none raised any significant amount of local funding (only three had managed to elicit a few sparse donations from Pakistani companies).

The review goes into much more detail on why volunteers "abandon local collective action groups that reshape themselves to satisfy foreign funders".

Oxfam's capacity-building work in Sindh province as a case study, ft. "workshopias":

Oxfam soon discovered an unexpected dark side to its “capacity-building” work in Sindh province. It had trained six locally prominent welfare organizations in strategic planning and proposal writing, and got them started on implementation by giving them $1000 to work with. The VOs largely met their explicit project targets, and seemed ready to engage with international development funds on a scale much greater than they’d handled before.

Yet all six organisations were visibly more dysfunctional at the end of the project than they had been at the beginning. They were consumed by factionalism, accusations of favoritism, legal battles over control of funds. And they had lost virtually all of their volunteers—notably including the members whose monthly donations and contributions of time had for decades been the backbone of their work. An Oxfam official observed about one group: “From thirty active members, the size of the team shrank to three or four people who had become workshopias,” i.e. those who had gone to every training workshop and built the strongest relationships with the foreign donor. The kind of welfare work the VOs had previously done, based on mobilizing communities for mutual support, was now beyond their capacity.

But so what? 

So what? As long as the project achieves its outputs—the disaster relief or cash grants distributed, the latrines built, the agriculture training sessions delivered—why would we care what hair-splitting label gets put on the organization that did it? Shouldn’t we expect professionalism, the profit motive, and higher volumes of funding to yield better long-term results anyway, compared to unreliable, unscalable, harder-to-monitor voluntary work?

Bano is a rational choice theorist, and reminds her readers of the value of voluntary collective action through a recap of some classic theory by Garret Hardin, Mancur Olson, and Elinor Ostrom. Any community’s common interests require people to work together cooperatively; but it’s surprisingly difficult to sustain that cooperation, thanks to free-rider problems and inequalities of motivation and information. Shared resources, in particular, often collapse in a “tragedy of the commons” where individuals’ incentive to use as much as possible leads to overexploitation.

Coercive rules imposed by a state are one way to overcome this problem—but no state can generate all the cooperation we need for a functioning society, and in many countries the authoritarian, exploitative government is an unlikely source of public-interest regulation. That needn’t leave us in despair. Even management of common-pool resources (one of the thorniest collective action problems) can be done effectively through voluntary institutions. In fact, local collective action often yields better results than either state coercion or the individualized incentive structure of competitive markets. ... 

At the end of the day, institutions for voluntary collective action always rest—not solely, but significantly—on social trust. And once trust is lost, it’s exceedingly hard to rebuild. ... [Bano's] survey found that even Pakistani VOs that hadn’t taken foreign aid now experienced “difficulty retaining volunteers or inducting new ones from the younger generation, and most of them attributed this difficulty to the rise of the ‘NGO culture.’”

Lots of sobering anecdotes throughout, the reviewer seems to have spent a long time working in aid on the ground.

So what should folks do differently?

Bano doesn’t think the answer is to insulate local organizations entirely from international aid flows (still less to choke off those aid flows, DOGE-style). Instead, she calls on donors and INGOs to take deliberate steps to “minimize the negative impact” of their funding... 

... the fundamental change we’d make is to treat a VO’s ability to mobilize local collective action like it’s gold dust. Vocally, publicly value it. Talk to everyone about its strategic importance. Actually measure it—track the capacity of VOs to retain members, attract volunteers, and raise local donations as a core indicator of institutional health. And if it starts to drop, treat that as a crisis on par with financial fraud. Investigate its context-specific roots—what religious, social, and/or political values are motivating people to give time and money to the VO in question—so that we’ll have some idea of how to respond if our work does begin to diminish it.

In our capacity-building work, we wouldn’t focus on turning local groups into part of the management chain for dwindling foreign grants. Rather, we’d primarily aim to give them the equivalent of a “three-self” vision, and the skills to back it up. We’d encourage them to keep a primary focus on the priority problems of their target communities; we’d take care not to overburden them with monitoring and reporting requirements. ...

We wouldn’t aim (either explicitly or implicitly) to replace the “psychosocial and religious incentives” that motivate community volunteers and VO members with material ones, in the name of formalization or professionalization. We’d drop the host of small, commonplace, deeply counterproductive incentives that too many NGOs use to get people to attend trainings and advocacy events—even if that means we have to revise our project events so that local people are actually willing to invest their own time in attending them.

We’d do all we could to avoid destroying the most common signal of commitment—material sacrifice—that reassures local donors and volunteers the VO isn’t exploiting them. Bano doesn’t think this requires everyone working in the aid sector to live like Abdul Sattar Edhi or Mother Teresa. It likely does involve paying below-market salary rates to staff; thorough consultation with all of a VO’s members before agreeing to pay anyone; and setting policies on e.g. vehicle use, office location, and travel per diems with due consideration to public perceptions of waste and wealth. It would require a readiness to adapt incentives over the course of a project or funding partnership, especially if there’s evidence of harm.

We’d prioritize the funding of activities, rather than staff salaries and benefits, and not fund those activities so lavishly as to discourage economy or innovation. ... 

EA relevance:

What does Bano’s work imply for the approaches most beloved of effective altruists? Some cost-effective interventions don’t rely very heavily on local collective action capacity. Households can generally make use of bed nets or cash grants even in a low-trust environment; those distributions can be well-handled by NGOs whose primary competency is grant and project management.

The picture shifts slightly as we move into high value-for-money public health measures: deworming, malaria treatment, vaccines. It might seem that a big, salaried state health sector is all that’s required to deliver those at scale, but in a host of countries, the key frontline personnel are actually community health volunteers. The more that “NGO culture” strains collective action, making local people feel like suckers for giving their time to the community without pay, the more friction we’ll be creating for these lifesaving interventions.

We’ll also inevitably encounter situations where people’s ill-health and poverty are grounded in beliefs and attitudes—e.g. the deep suspicion of polio vaccines in the Pashtun borderlands, or the conviction in western Nepal that selling milk to a dairy coop is an intolerable risk (because it might be sold to a low-caste person, angering deities who will kill you or your cow.) A local VO, embedded in its community, can be a powerful and effective ambassador for changes in belief. But as Bano and Nevius both relate, salaried staff with no significant signs of sacrifice often struggle to catalyze this kind of transformation. “You’re just bringing me this message because they’re paying you to,” is a natural discounting factor.

Any humanitarian effort will also quickly run up against politics. Giving a cash grant to landless tenant farmers in Pakistan won’t have much impact when their landlords can just nudge up rent levels to extract the added wealth. Changing that kind of systemic exploitation requires a local institution like AMP whose core competence is risky, politically-charged community organizing. If in searching for a local cash delivery partner, an INGO takes that kind of institution and distracts and depoliticizes it, we’ll ultimately have done more to reinforce than alleviate poverty.

Western altruists could try to invest their donations only in areas requiring minimal entanglement with beliefs and power dynamics. That’s a defensible choice—not solely a drunkard’s search, investing in the areas with the fewest factors confounding our efforts at evidence generation. There’s unquestionably lifesaving work to be done in that space.

The main implication of Bano’s work then becomes a “do no harm” one: channel your funds through an institution that never had much capacity in the first place to mobilize local collective action, so that delivering your preferred interventions doesn’t kill off that capacity. Or (much better) take the time and care to engage with local partner institutions in ways that preserves their capacity to mobilize volunteers. Either way, it requires giving extra consideration not just to EA’s usual core question, “which are the best-evidenced interventions?” but “what are the least damaging institutional pathways for delivering those interventions?”

I'd be curious to get takes on any of this. 

They used to as OP, but as cG they now only feature selected grants on individual Funds pages. https://www.eagrantsdatabase.org/ is your best one-stop shop at the moment.

How does it compare to https://impactlist.xyz/?